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Jung, Daekyo, Vu Tran Tuan, Dai Quoc Tran, Minsoo Park, and Seunghee Park. "Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management." Applied Sciences 10, no. 2 (January 17, 2020): 666. http://dx.doi.org/10.3390/app10020666.
Повний текст джерелаАнотація:
In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused only on storms, floods, and earthquakes, and they have not used past data. This research proposes a new conceptual framework of an IDSS for disaster management, with particular attention paid to wildfires and cold/heat waves. The IDSS uses big data collected from open application programming interface (API) and artificial intelligence (AI) algorithms to help decision-makers make faster and more accurate decisions. In addition, a simple example of the use of a convolutional neural network (CNN) to detect fire in surveillance video has been developed, which can be used for automatic fire detection and provide an appropriate response. The system will also consider connecting to open source intelligence (OSINT) to identify vulnerabilities, mitigate risks, and develop more robust security policies than those currently in place to prevent cyber-attacks.
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Srinivasa Rao, Rayapudi, Sadhu Venkata Lakshmi Narasimham, Manyala Ramalinga Raju, and A. Srinivasa Rao. "Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithm." IEEE Transactions on Power Systems 26, no. 3 (August 2011): 1080–88. http://dx.doi.org/10.1109/idams.2010.2076839.
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Rzucidło, Paweł, Grzegorz Jaromi, Tomasz Kapuściński, Damian Kordos, Tomasz Rogalski, and Piotr Szczerba. "In-Flight Tests of Intruder Detection Vision System." Sensors 21, no. 21 (November 5, 2021): 7360. http://dx.doi.org/10.3390/s21217360.
Повний текст джерелаАнотація:
In the near future, the integration of manned and unmanned aerial vehicles into the common airspace will proceed. The changes taking place mean that the safety of light aircraft, ultralight aircraft and unmanned air vehicles (UAV) will become an increasing problem. The IDAAS project (Intruder Detection And collision Avoidance System) meets the new challenges as it aims to produce technically advanced detection and collision avoidance systems for light and unmanned aerial vehicles. The work discusses selected elements of research and practical tests of the intruder detection vision system, which is part the of IDAAS project. At the outset, the current formal requirements related to the necessity of installing anticollision systems on aircraft are presented. The concept of the IDAAS system and the structure of algorithms related to image processing are also discussed. The main part of the work presents the methodology developed for the needs of dedicated flight tests, its implementation and the results obtained. The initial tests of the IDAAS system carried out on an ultralight aircraft generally indicate the possibility of the effective detection of intruders in the airspace with the use of vision methods, although they also indicated the existence of conditions in which this detection may prove difficult or even impossible.
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Slam, Nady, Wushour Slamu, and Pei Wang. "A Prototype Intelligent Decision-Support System with a Unified Planning and Learning Capabilities." International Journal on Artificial Intelligence Tools 26, no. 06 (December 2017): 1750025. http://dx.doi.org/10.1142/s0218213017500257.
Повний текст джерелаАнотація:
This article summarizes reviews regarding the evolution of intelligent decision-support systems (IDSS). After doing an extensive literature survey, it was apparent that the theoretical foundation of IDSS has not undergone much improvement. The emergence and development of Artificial General Intelligence (AGI) provides a new theoretical perspective for constructing and developing IDSS. An AGI project, Non-Axiomatic Reasoning System (NARS), is built into the framework of a unified reasoning system, with a logic, Non-Axiomatic Logic (NAL). In this paper, we propose a formal model with unified planning, learning and uncertainty representation capabilities using NAL. We have developed a prototype decision-support system for urban fire-fighting within this framework, and obtained results demonstrating that this method can provide an effective way for intelligent decision-support systems.
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Reis, João, Paula Santo, and Nuno Melão. "Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States." Social Sciences 9, no. 11 (November 16, 2020): 207. http://dx.doi.org/10.3390/socsci9110207.
Повний текст джерелаАнотація:
In the last six decades, many advances have been made in the field of artificial intelligence (AI). Bearing in mind that AI technologies are influencing societies and political systems differently, it can be useful to understand what are the common issues between similar states in the European Union and how these political systems can collaborate with each other, seeking synergies, finding opportunities and saving costs. Therefore, we carried out an exploratory research among similar states of the European Union, in terms of scientific research in areas of AI technologies, namely: Portugal, Greece, Austria, Belgium and Sweden. A key finding of this research is that intelligent decision support systems (IDSS) are essential for the political decision-making process, since politics normally deals with complex and multifaceted decisions, which involve trade-offs between different stakeholders. As public health is becoming increasingly relevant in the field of the European Union, the IDSSs can provide relevant contributions, as it may allow sharing critical information and assist in the political decision-making process, especially in response to crisis situations.
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HUANG, ZU-LAN, RICHARD M. M. CHEN, and YAO-LIN JIANG. "A PARALLEL DECOUPLING TECHNIQUE TO ACCELERATE CONVERGENCE OF RELAXATION SOLUTION OF INTEGRAL-DIFFERENTIAL-ALGEBRAIC EQUATIONS." Journal of Interconnection Networks 02, no. 03 (September 2001): 295–304. http://dx.doi.org/10.1142/s0219265901000397.
Повний текст джерелаАнотація:
In this paper, we first study the covergence performance of relaxatio-based algorithms for linear integral differential-algebraic equations (IDAEs), then a parallel decoupling technique to speed up the convergence of the relaxation-based algorithms is derived. This novel technique is suitable for implementation of parallel processing for complicated systems of IDAEs. Factors taking effect on the performance of parallel processing are discussed in detail. Large numerical examples running on a network of IBM RS/6000 SP2 system are given to illustrate how judicious partitionings of matrices can help improve convergence in parallel processing.
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Behmel, Sonja, Mathieu Damour, Ralf Ludwig, and Manuel Rodriguez. "Optimization of River and Lake Monitoring Programs Using a Participative Approach and an Intelligent Decision-Support System." Applied Sciences 9, no. 19 (October 3, 2019): 4157. http://dx.doi.org/10.3390/app9194157.
Повний текст джерелаАнотація:
We developed a holistic intelligent decision-support system (IDSS) to provide decision-support for all steps in planning, managing and optimizing water quality monitoring programs (WQMPs). The IDSS is connected to a previously developed database, EnkiTM. The IDSS integrates tacit and explicit knowledge on WQMPs to standardize decision making and to make decisions transparent and transferable. The optimization features of the IDSS were tested on a lake and a river WQMP from two case studies in Canada. We illustrate how the IDSS provides decision support to understanding the underlying rationale of the existing WQMPs, validating and storing data, selecting optimization procedures proposed in the literature, applying the optimization procedures and finalizing the optimization procedure. We demonstrated that the IDSS/EnkiTM is necessary to take and document decisions during all phases of a WQMP to obtain a clear idea of when and why changes are made and determine actionable tasks in the optimization process.
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WU, YEU, YONGHONG TIAN, and WANLEI ZHOU. "THE DEVELOPMENT OF A MOBILE DECISION SUPPORT SYSTEM." Journal of Interconnection Networks 02, no. 03 (September 2001): 379–90. http://dx.doi.org/10.1142/s0219265901000452.
Повний текст джерелаАнотація:
The emergence of mobile computing environments brings out various changes in the requirements and applications involving distributed data and has made the traditional Intelligent Decision Support System (IDSS) architectures based on the client/server model ineffective in mobile computing environments. This paper discusses the deficiencies of the current IDSS architectures based on data warehouse, on-line analysis processing (OLAP), model base (MB) and knowledge based (KB) technologies. By adopting the agent technology, the paper extends the IDSS system architecture to the Mobile Decision Support System (MDSS) architecture. The logical structure and the application architecture of the MDSS and the mechanisms and implementation strategies of the User Access Agent System, a major component of the MDSS, are described in this paper.
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Sadek, Adel W., and Bernard Baah. "Cost-Effectiveness of Intelligent Transportation System (ITS) Deployment in a Medium-Sized Area: Case Study with ITS Deployment Analysis System." Transportation Research Record: Journal of the Transportation Research Board 1826, no. 1 (January 2003): 16–24. http://dx.doi.org/10.3141/1826-03.
Повний текст джерелаАнотація:
To help transportation planners, the Federal Highway Administration (FHWA) has recently sponsored the development of a sketch planning analysis tool for estimating the benefits and costs of intelligence transportation system (ITS) deployment. Called the ITS Deployment Analysis System (IDAS), the tool was developed by Cambridge Systematics for FHWA and operates as a postprocessor to traditional planning models that are based on the four-step planning process. IDAS was used to predict the likely benefits of deploying ITS in Chittenden County, Vermont, a medium-sized region in northwestern Vermont, to gain insight into IDAS’ applicability in evaluating ITS benefits. A sensitivity analysis was also conducted to assess the sensitivity of IDAS’ results to the choice of values of some of the model’s parameters. The sensitivity analysis demonstrates that some of the model’s parameters tend to have a more significant impact on the results than others. Finally, the results indicate that, for medium-sized areas similar to Chittenden County, coordination of signals along major arterials and transit projects for automatic vehicle location and scheduling appear to be more cost-effective than freeway and incident management systems.
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Pietro Spadaccino and Francesca Cuomo. "Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing." ITU Journal on Future and Evolving Technologies 3, no. 2 (September 22, 2022): 408–20. http://dx.doi.org/10.52953/wnvi5792.
Повний текст джерелаАнотація:
Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs), where different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether anomalous events occur, named anomaly-based. This work is dedicated to the application of IDS to the Internet of Things (IoT) networks, where also edge computing is used to support IDS implementation. Specific attention is given to IDSs which leverage device classification at the edge. New challenges that arise when deploying an IDS in an edge scenario are identified and remedies are proposed.
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Kuzmin, Vladimir R., and Yury A. Zagorulko. "Usage of the Agent-Service Approach for the Development of Intelligent Decision Support Systems in the Energy Sector." Vestnik NSU. Series: Information Technologies 18, no. 3 (2020): 5–18. http://dx.doi.org/10.25205/1818-7900-2020-18-3-5-18.
Повний текст джерелаАнотація:
Recently, the concept of intelligent energy systems becomes more popular in Russia. In order to implement such systems, it is required both development and usage of modern information technologies to manage technological infrastructure, and upgrade of this infrastructure. In turn, infrastructure upgrade requires a strategic decision-making for energy sector development. To provide a high quality of such decisions, intellectualization of the process of making them is required. This article deals with usage of agent-service approach for development of intelligent decision support systems (IDSS). Concepts of agent and multiagent system are being considered. Detailed description of agentservice approach and architecture of a typical IDSS, used in this approach, is provided. Also, article considers usage of agent-service approach for development of IDSS “Situation polygon” (for strategic decision-making support in energy sector) and web-oriented information-analytical system WIS (for impact assessment of the energy sector on geoecology). In the future, we plan to add additional functionality in these systems, in particular, to provide in the “Situation polygon” an automatic setting of the values of connections in cognitive maps based on the values of connections in the ontology, and to add in WIS the possibility of calculating the trajectory of the transfer of harmful substances.
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Fayzullin, Alexey, Alesia Bakulina, Karen Mikaelyan, Anatoly Shekhter, and Anna Guller. "Implantable Drug Delivery Systems and Foreign Body Reaction: Traversing the Current Clinical Landscape." Bioengineering 8, no. 12 (December 9, 2021): 205. http://dx.doi.org/10.3390/bioengineering8120205.
Повний текст джерелаАнотація:
Precise delivery of therapeutics to the target structures is essential for treatment efficiency and safety. Drug administration via conventional routes requires overcoming multiple transport barriers to achieve and maintain the local drug concentration and commonly results in unwanted off-target effects. Patients’ compliance with the treatment schedule remains another challenge. Implantable drug delivery systems (IDDSs) provide a way to solve these problems. IDDSs are bioengineering devices surgically placed inside the patient’s tissues to avoid first-pass metabolism and reduce the systemic toxicity of the drug by eluting the therapeutic payload in the vicinity of the target tissues. IDDSs present an impressive example of successful translation of the research and engineering findings to the patient’s bedside. It is envisaged that the IDDS technologies will grow exponentially in the coming years. However, to pave the way for this progress, it is essential to learn lessons from the past and present of IDDSs clinical applications. The efficiency and safety of the drug-eluting implants depend on the interactions between the device and the hosting tissues. In this review, we address this need and analyze the clinical landscape of the FDA-approved IDDSs applications in the context of the foreign body reaction, a key aspect of implant–tissue integration.
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Alyami, Hashem, Md Tarique Jamal Ansari, Abdullah Alharbi, Wael Alosaimi, Majid Alshammari, Dhirendra Pandey, Alka Agrawal, Rajeev Kumar, and Raees Ahmad Khan. "Effectiveness Evaluation of Different IDSs Using Integrated Fuzzy MCDM Model." Electronics 11, no. 6 (March 9, 2022): 859. http://dx.doi.org/10.3390/electronics11060859.
Повний текст джерелаАнотація:
Cyber-attacks are becoming progressively complicated; hence, the functional issues of intrusion-detection systems (IDSs) present ever-growing challenges. Failing to detect intrusions may jeopardize the trustworthiness of security services, such as privacy preservation, authenticity, and accessibility. To fight these risks, different organizations nowadays use a variety of approaches, techniques, and technologies to safeguard the systems’ credibility. Establishing policies and procedures, raising user awareness, implementing firewall and verification systems, controlling system access, and building computer-issue management groups are all examples of safeguarding methods. There is a lack of sufficient emphasis on the effectiveness of intrusion-detection systems. In enterprises, IDS is used to analyze the potentially dangerous activities taking place within the technological settings. The selection of efficient IDS is a challenging task for organizations. This research evaluates the impact of five popular IDSs for their efficiency and effectiveness in information security. The authors used the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS)-based integrated multi-criteria decision-making (MCDM) methodology to evaluate the efficacy of the popular IDSs. The findings of this research suggest that most of the IDSs appear to be highly potential tools. Even though Snort is extensively deployed, Suricata has a substantial advantage over Snort. Suricata uses multi-threading functionality in comparison to Snort to boost the processing performance.
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Qie, Xiushu, Xueke Wu, Tie Yuan, Jianchun Bian, and Daren Lu. "Comprehensive Pattern of Deep Convective Systems over the Tibetan Plateau–South Asian Monsoon Region Based on TRMM Data." Journal of Climate 27, no. 17 (August 28, 2014): 6612–26. http://dx.doi.org/10.1175/jcli-d-14-00076.1.
Повний текст джерелаАнотація:
Abstract Diurnal and seasonal variation, intensity, and structure of deep convective systems (DCSs; with 20-dBZ echo tops exceeding 14 km) over the Tibetan Plateau–South Asian monsoon region from the Tibetan Plateau (TP) to the ocean are investigated using 14 yr of Tropical Rainfall Measuring Mission (TRMM) data. Four unique regions characterized by different orography are selected for comparison, including the TP, the southern Himalayan front (SHF), the South Asian subcontinent (SAS), and the ocean. DCSs and intense DCSs (IDCSs; with 40-dBZ echo tops exceeding 10 km) occur more frequently over the continent than over the ocean. About 23% of total DCSs develop into IDCSs in the SHF, followed by the TP (21%) and the SAS (15%), with the least over the ocean (2%). The average 20-dBZ echo-top height of IDCSs exceeds 16 km and 9% of them even exceed 18 km. DCSs and IDCSs are the most frequent over the SHF, especially in the westernmost SHF, where the intensity—in terms of strong radar echo-top (viz., 40 dBZ) height, ice-particle content, and lightning flash rate—is the strongest. DCSs over the TP are relatively weak in convective intensity and small in size but occur frequently. Oceanic DCSs possess the tallest cloud top (which mainly reflects small ice particles) and the largest size, but their convective intensity is markedly weaker. DCSs and IDCSs show a similar diurnal variation, mainly occurring in the afternoon with a peak at 1600 local time over land. Although most of both DCSs and IDCSs occur between April and October, DCSs have a peak in August, whereas IDCSs have a peak in May.
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Al-Shourbaji, Ibrahim, and Samaher Al-Janabi. "Intrusion Detection and Prevention Systems in Wireless Networks." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 267–72. http://dx.doi.org/10.24017/science.2017.3.48.
Повний текст джерелаАнотація:
In society today, public and personal communication are often carried out through wireless technology. These technologies can be vulnerable to various types of attacks. Attackers can access the signal to listen or to cause more damage on the wireless networks. Intrusion Detection and Prevention System (IDPS) technology can be used to monitor and analyze the signal for any infiltration to prevent interception or other malicious intrusion. An overview description of IDPSs and their core functions, the primary types of intrusion detection mechanisms, and the limitations of IDPSs are discussed. This work perceives the requirements of developing new and sophisticated detection and prevention methods based on, and managed by, combining smart techniques including machine learning, data mining, and game theory along with risk analysis and assessment techniques. This assists wireless networks toremain secure and aids system administrators to effectively monitor their systems.
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Özalp, Murat, Cihan Karakuzu, and Ahmet Zengin. "Distributed Intrusion Detection Systems: A Survey." Academic Perspective Procedia 2, no. 3 (November 22, 2019): 400–407. http://dx.doi.org/10.33793/acperpro.02.03.18.
Повний текст джерелаАнотація:
In this paper, distributed intrusion detection systems (IDSs)in the literature are reviewed. There are two types of IDS, depending on the interoperability. Stand-alone systems decide on their own. Distributed systems are composed of multiple components processing different data and work together to make a global decision. Distributed IDSs present some difficulties compared to stand-alone systems. For example, problems such as the structure of message communication, establishment of a trust mechanism, joint decision making are the issues discussed in the studies related to such systems. A detailed literature review has been made for the distributed IDSs which are the focus of our study. The studies considered to be within the scope of our study were investigated and presented comparatively. Although the initial studies on interoperable systems began in the 1990s, the issue is still open to improvement, as there is no widespread system that has become "product". On the other hand, due to the development of artificial intelligence systems, innovative studies are being conducted on cyber threat detection. Therefore, the subject is thought to be open to improvement and in the last part of the study, suggestions are given for those who want to work on the subject.
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Baldin, Michel, Tom Breunig, Roger Cue, Albert De Vries, Mark Doornink, Jan Drevenak, Robert Fourdraine, et al. "Integrated Decision Support Systems (IDSS) for Dairy Farming: A Discussion on How to Improve Their Sustained Adoption." Animals 11, no. 7 (July 7, 2021): 2025. http://dx.doi.org/10.3390/ani11072025.
Повний текст джерелаАнотація:
Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.
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Yu, Kun, and Fang Wang. "Research and Design on Intelligent Decision Support System Frame for Vehicle Maintenance." Advanced Materials Research 765-767 (September 2013): 3–7. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.3.
Повний текст джерелаАнотація:
This paper analyzes current research results of IDSS and researches the knowledge of vehicle maintenance. By using technologies such as expert system (ES), Data warehouse (DW), fault tree automatic generation, intelligent interface etc. based on the development process and characteristics of IDSS, this paper combines both virtual maintenance and fault diagnosis together, presenting an intelligent decision support system framework for vehicle maintenance with intelligent , interaction and integration.
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Zhang, Ming, Jiang Yu, Min Hua Wang, and Chun Ying Qi. "Study on Design of Intelligent Decision Support System of Engineering Equipment Repair." Applied Mechanics and Materials 127 (October 2011): 36–41. http://dx.doi.org/10.4028/www.scientific.net/amm.127.36.
Повний текст джерелаАнотація:
By introducing ES (Expert System) of AI (Artificial Intelligence) and KP (Knowledge Processing), etc. into DSS (Decision Support system), the study on design of IDSS_EER (Intelligent Decision Support System of Engineering Equipment Repair) is discussed in detail combined with the characteristics of EER (Engineering Equipment Repair).To start with, the research status quo and analysis of IDSS problems are analyzed and the traditional design problem of IDSS is improved in the system structure and system function of IDSS with a clear aim. Finally, the design and development of IDSS_EER are given a particular description. The system design embodies the strong points of the qualitative and quantitative analyses and can be achieved theatrically and technically. Simultaneously, the system provides the man-in-the-loop support and avoids the intelligent blindness.
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Almomani, Iman, and Afnan Alromi. "Integrating Software Engineering Processes in the Development of Efficient Intrusion Detection Systems in Wireless Sensor Networks." Sensors 20, no. 5 (March 3, 2020): 1375. http://dx.doi.org/10.3390/s20051375.
Повний текст джерелаАнотація:
Applying Software Engineering (SE) processes is vital to critical and complex systems including security and networking systems. Nowadays, Wireless Sensor Networks (WSNs) and their applications are found in many military and civilian systems which make them attractive to security attackers. The increasing risks and system vulnerabilities of WSNs have encouraged researchers and developers to propose many security solutions including software-based Intrusion Detection Systems (IDSs). The main drawbacks of current IDSs are due to the lack of clear, structured software development processes. Unfortunately, a substantial gap has been observed between WSN and SE research communities. Integrating SE and WSNs is an emerging topic that will be expanded as technology evolves and spreads in all life aspects. Consequently, this paper highlighted the importance of Requirement Engineering, Software Design, and Testing when developing IDSs for WSNs. Three software IDS designs were proposed in this study: Scheduling, Broadcast, and Watchdog designs. The three designs were compared in terms of consumed energy and network lifetime. Although the same IDS approach was used, but, by highlighting the design phase and implementing different designs, the network lifetime was increased by 73.6% and the consumed energy was reduced by 20% in some of the designs. This is a clear indication of how following a proper SE process could affect the performance of the IDS in WSN. Moreover, conclusions were drawn in regard to applying software engineering processes to IDSs to deliver the required functionalities, with respect to operational constraints, with an improved performance, accuracy and reliability.
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Ray, Loye Lynn, and Henry Felch. "Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems." International Journal of Strategic Information Technology and Applications 5, no. 3 (July 2014): 24–36. http://dx.doi.org/10.4018/ijsita.2014070102.
Повний текст джерелаАнотація:
Today's anomaly-based network intrusion detection systems (IDSs) are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching the problem of detecting these attacks using multi-layered feed forward neural network (MLFFNN) IDSs. The scope of the paper focused on a review of the literature from primarily 2008 to the present found in peer-review and scholarly journals. A key word search was used to compare and contrast the literature to find strengths, weaknesses and gaps. The significance of the research found that further work is needed to improve the performance and convergence rates of MLFFNN IDSs. This literature review contributes to the area of intrusion detection by looking at the effects of architecture, algorithms, and input data on the performance and convergence rates of MLFFNN IDSs.
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Fahmi, Ali, Amin Dorostanian, Hassan Rezazadeh, and Alireza Ostadrahimi. "An Intelligent Decision Support System (IDSS) for Nutrition Therapy." International Journal of Reliable and Quality E-Healthcare 2, no. 4 (October 2013): 14–27. http://dx.doi.org/10.4018/ijrqeh.2013100102.
Повний текст джерелаАнотація:
In this paper, the authors have presented an expert system to support decision makers in nutrition therapy planning. This system is an extended version of a fuzzy decision support system for nutrition therapy. The presented expert system is equipped with an updated knowledge base component by using a set of rules. Also in order to deal with vagueness and uncertainty, fuzzy set theory could provide a suitable framework for data management, modelling and decision support. Therefore, fuzzy rules empower this system to be implemented more realistically. In addition, for developing knowledge management component, artificial neural network (ANN) is applied to survey the input data and information in the long term. The integration of ANN with the expert system provides the possibility for a set of novel rules to be generated and consequently adds new knowledge to the system.
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Mhashilkar, Parag, Mine Altunay, Eileen Berman, David Dagenhart, Stuart Fuess, Burt Holzman, James Kowalkowski, et al. "HEPCloud, an Elastic Hybrid HEP Facility using an Intelligent Decision Support System." EPJ Web of Conferences 214 (2019): 03060. http://dx.doi.org/10.1051/epjconf/201921403060.
Повний текст джерелаАнотація:
HEPCloud is rapidly becoming the primary system for provisioning compute resources for all Fermilab-affiliated experiments. In order to reliably meet the peak demands of the next generation of High Energy Physics experiments, Fermilab must plan to elastically expand its computational capabilities to cover the forecasted need. Commercial cloud and allocation-based High Performance Computing (HPC) resources both have explicit and implicit costs that must be considered when deciding when to provision these resources, and at which scale. In order to support such provisioning in a manner consistent with organizational business rules and budget constraints, we have developed a modular intelligent decision support system (IDSS) to aid in the automatic provisioning of resources spanning multiple cloud providers, multiple HPC centers, and grid computing federations. In this paper, we discuss the goals and architecture of the HEPCloud Facility, the architecture of the IDSS, and our early experience in using the IDSS for automated facility expansion both at Fermi and Brookhaven National Laboratory.
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El-Taj, Homam Reda. "Aggregating IDS Alerts Based on Time Threshold: Testing and Results." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 2 (October 10, 2013): 2216–25. http://dx.doi.org/10.24297/ijct.v11i2.1175.
Повний текст джерелаАнотація:
Every secure system has the possibility to fail. Therefore, extra effort should be taken to protect these systems. Intrusion Detection Systems (IDSs) had been proposed with the aim of providing extra protection to security systems. These systems trigger thousands of alerts per day, which prompt security analysts to verify each alert for relevance and severity based on an aggregation criterion. Several aggregation methods have been proposed to collect these alerts. This paper presents our threshold aggregation system (TAS). Results shows that TAS aggregates IDS alerts accurately based on user demands and threshold value.
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Teixeira, Diogo, Silvestre Malta, and Pedro Pinto. "A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning." Future Internet 14, no. 3 (February 25, 2022): 72. http://dx.doi.org/10.3390/fi14030072.
Повний текст джерелаАнотація:
An intrusion detection system (IDS) is an important tool to prevent potential threats to systems and data. Anomaly-based IDSs may deploy machine learning algorithms to classify events either as normal or anomalous and trigger the adequate response. When using supervised learning, these algorithms require classified, rich, and recent datasets. Thus, to foster the performance of these machine learning models, datasets can be generated from different sources in a collaborative approach, and trained with multiple algorithms. This paper proposes a vote-based architecture to generate classified datasets and improve the performance of supervised learning-based IDSs. On a regular basis, multiple IDSs in different locations send their logs to a central system that combines and classifies them using different machine learning models and a majority vote system. Then, it generates a new and classified dataset, which is trained to obtain the best updated model to be integrated into the IDS of the companies involved. The proposed architecture trains multiple times with several algorithms. To shorten the overall runtimes, the proposed architecture was deployed in Fed4FIRE+ with Ray to distribute the tasks by the available resources. A set of machine learning algorithms and the proposed architecture were assessed. When compared with a baseline scenario, the proposed architecture enabled to increase the accuracy by 11.5% and the precision by 11.2%.
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Dharmarajan, R., and V. Thiagarasu. "Computation of Risk Severity of the Malicious Node using Adaptive Neuro Fuzzy Inference System (ANFIS)." Asian Journal of Engineering and Applied Technology 8, no. 1 (February 5, 2019): 9–14. http://dx.doi.org/10.51983/ajeat-2019.8.1.1067.
Повний текст джерелаАнотація:
The Intrusion Detection System (IDS) can be employed broadly for safety network. Intrusion Detection Systems (IDSs) are commonly positioned alongside with other protecting safety mechanisms, such as authentication and access control, as a subsequent line of defence that guards data structures. In this paper, Adaptive Neuro Fuzzy Inference System has utilized to predict the risk severity of the malicious nodes found the previous classification phase.
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Hadi, Mohammed A., Diane Quigley, Prasoon Sinha, and Liang Hsia. "Benefit and Cost Parameters of Intelligent Transportation Systems." Transportation Research Record: Journal of the Transportation Research Board 1910, no. 1 (January 2005): 57–63. http://dx.doi.org/10.1177/0361198105191000107.
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The Intelligent Transportation Systems (ITS) Deployment Analysis System (IDAS) is a sketch planning analysis tool that can be used to estimate the impacts and costs resulting from the deployment of ITS components. The program includes default values for the parameters required to calculate ITS deployment costs and benefits. IDAS allows users to override the default values of ITS device cost and benefit parameters, including unit cost estimates, cost assumptions, benefit module parameters, ITS impacts, and impact dollar values. On one hand, the use of cost and benefit parameter values that better represent the region under consideration is expected to produce better assessments of ITS deployment alternatives. On the other hand, collection of regional ITS cost and benefit parameters could be expensive, and some required parameters might not be available for the region under consideration. For this reason, IDAS users tend to use many of the program's default values in their analyses. This paper reports on the results of a project that was initiated by the Florida Department of Transportation to identify Florida-specific cost and benefit parameters for use as inputs to IDAS ITS alternative assessments in Florida. The paper also compares the results obtained by using these derived parameters with the results obtained by using the national defaults when a real world ITS deployment project is being evaluated. The produced cost estimate for this deployment project is also compared with the actual cost of the project.
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Awuzie, Bankole O., and Peter McDermott. "Towards an Understanding of the Influence of National Culture on Organizational Viability: An Exploratory Study." International Journal of Construction Supply Chain Management 7, no. 1 (December 31, 2017): 20–38. http://dx.doi.org/10.14424/ijcscm701017-20-38.
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Viability connotes a system’s ability to become ultra-stable through effective self-regulation of its internal processes and information processing among its subsystems. Applying this to an infrastructure delivery system (IDS) context, this study proposes that an IDS can successfully deliver on client requirements only if they attain and maintain viability. Research into the influence of National Culture (NC) on an IDS’s viability appears to be lacking; hence this study. Adopting a multi-case study, qualitative research design, this study explores three IDSs involved in the delivery of infrastructure projects in two different NC contexts; Nigeria and the United Kingdom. 25 semi-structured interviews were conducted across the cases to provide for an in-depth understanding of existing interactions between participants in these delivery systems: client/project sponsor; main contractor and sub-contractors and to understand the influence of the prevailing national culture on such interactions, if any. Findings indicate that NC in project delivery environments influence the ability of IDSs to attain viability, especially as it pertains to the sustenance of Team Quality Attributes (TWQ) within the system. Based on these findings, it is expected that in modelling IDSs for viability, adequate consideration should be given to the prevailing NC by project managers and planners.
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Budler, Marko, Miha Bratec, Katarzyna Barbara Minor, and Luka Tomat. "A business model approach towards the understanding of daily deals within Internet distribution systems." Tourism Economics 26, no. 6 (July 22, 2019): 976–1000. http://dx.doi.org/10.1177/1354816619863261.
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The business model (BM) approach is making a slow, yet steady footprint in tourism research, can offer a fresh perspective and a viable level of analysis for broader networks of Internet distribution systems (IDSs). To date, the researchers have devoted limited attention to studying the daily deal (DD) providers and their position next to the online travel agencies (OTAs). More importantly, the DD providers developed a unique BM characteristics, which contrary to early predictions, enabled them to proliferate post-recession and maintain their position. The current study uses the BM approach to observe the original dimensions and BM elements, to identify distinct characteristics of generic and niche DD providers, and to benchmark them against the established OTAs. The BM approach enhances the understanding of the overlooked niche DD providers within IDSs. Further, this article provides a comparison of the BM characteristics in IDSs to help facilitate the formulation of a unique value proposition for each of them. Ultimately, the authors acknowledge limited possibilities for cooperation between the providers and discuss future directions of DD and broader IDSs BM transformations.
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Yu, Kai Ji, Zhong Li Yang, and You Yang. "Research Summary of Dispatching System Based on Multi-Agent Technology." Applied Mechanics and Materials 462-463 (November 2013): 217–24. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.217.
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The demerits and current demand of the traditional decision support system (DSS) for power dispatching are analyzed,and the advantages of incorporating the agent technology and the intelligent decision support system (IDSS) are expatiated in detail. A new intelligent decision system model is put forward consequently and the models framework is also given. An example of the integration of three existed expert systems for power system dispatching management into one MAS is given,and the cooperation activities between the agents are described.
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Jaromi, Grzegorz, Damian Kordos, Tomasz Rogalski, Paweł Rzucidło, and Piotr Szczerba. "Selected elements of visual inspection of the collision avoidance system for light and unmanned aircraft." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 20, no. 1-2 (February 28, 2019): 265–71. http://dx.doi.org/10.24136/atest.2019.048.
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The work discusses selected elements of research and practical tests of the vision anti-collision system, designed for ultralight and light aircraft and unmanned aerial vehicles. At the outset, current formal requirements related to the necessity of installing anti-collision systems on aircraft are presented. The concept of IDAAS (Intruder Detection And collision Avoidance System for light aircraft) and the structure of algorithms related to image processing were presented. The main part of the work is to discuss the selected scenarios implemented during the research.
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Benmoussa, N., M. Fakhouri Amr, S. Ahriz, K. Mansouri, and E. Illoussamen. "Outlining a Model of an Intelligent Decision Support System Based on Multi Agents." Engineering, Technology & Applied Science Research 8, no. 3 (June 19, 2018): 2937–42. http://dx.doi.org/10.48084/etasr.1936.
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Performance optimization has become a necessity for the survival of enterprises as emerging technologies continue to impact them. To achieve this goal, decision making, a complex process which depends on big data and human issues, must be effective. As enterprises are being subjected to a multi-faceted pressure, they must ensure the optimization of their performance. This article investigates an intelligent decision support system (IDSS) based on multi agent systems (MAS). Our contribution consists in developing an intelligent model with an IDSS MAS approach that can detect and evaluate changes in both the external environment and the enterprise itself. This model is an adequate management tool for optimal and sustainable performance and offers real-time analytical, prospecting and optimization methods.
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Siddiqi, Danya Arif, Rozina Feroz Ali, Mubarak Taighoon Shah, Vijay Kumar Dharma, Anokhi Ali Khan, Tapash Roy, and Subhash Chandir. "Evaluation of a Mobile-Based Immunization Decision Support System for Scheduling Age-Appropriate Vaccine Schedules for Children Younger Than 2 Years in Pakistan and Bangladesh: Lessons From a Multisite, Mixed Methods Study." JMIR Pediatrics and Parenting 6 (February 17, 2023): e40269. http://dx.doi.org/10.2196/40269.
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Background Missed opportunities for vaccination (MOVs), that is, when children interact with the health system but fail to receive age-eligible vaccines, pose a crucial challenge for equitable and universal immunization coverage. Inaccurate interpretations of complex catch-up schedules by health workers contribute to MOVs. Objective We assessed the feasibility of a mobile-based immunization decision support system (iDSS) to automatically construct age-appropriate vaccination schedules for children and to prevent MOVs. Methods A sequential exploratory mixed methods study was conducted at 6 immunization centers in Pakistan and Bangladesh. An android-based iDSS that is packaged in the form of an application programming interface constructed age-appropriate immunization schedules for eligible children. The diagnostic accuracy of the iDSS was measured by comparing the schedules constructed by the iDSS with the gold standard of evaluation (World Health Organization–recommended Expanded Programme on Immunization schedule constructed by a vaccines expert). Preliminary estimates were collected on the number of MOVs among visiting children (caused by inaccurate vaccination scheduling by vaccinators) that could be reduced through iDSS by comparing the manual schedules constructed by vaccinators with the gold standard. Finally, the vaccinators’ understanding, perceived usability, and acceptability of the iDSS were determined through interviews with key informants. Results From July 5, 2019, to April 11, 2020, a total of 6241 immunization visits were recorded from 4613 eligible children. Data were collected for 17,961 immunization doses for all antigens. The iDSS correctly scheduled 99.8% (17,932/17,961) of all age-appropriate immunization doses compared with the gold standard. In comparison, vaccinators correctly scheduled 96.8% (17,378/17,961) of all immunization doses. A total of 3.2% (583/17,961) of all due doses (across antigens) were missed in age-eligible children by the vaccinators across both countries. Vaccinators reported positively on the usefulness of iDSS, as well as the understanding and benefits of the technology. Conclusions This study demonstrated the feasibility of a mobile-based iDSS to accurately construct age-appropriate vaccination schedules for children aged 0 to 23 months across multicountry and low- and middle-income country settings, and underscores its potential to increase immunization coverage and timeliness by eliminating MOVs.
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Yang, Hua, Tao Li, Xinlei Hu, Feng Wang, and Yang Zou. "A Survey of Artificial Immune System Based Intrusion Detection." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/156790.
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In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.
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Eremeev, Aleksandr P., and Sergey A. Ivliev. "Methods and Software for Analysis and Diagnostics of Complex Vision Pathologies." Vestnik MEI 5, no. 5 (2020): 140–47. http://dx.doi.org/10.24160/1993-6982-2020-5-140-147.
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The article addresses topical matters concerned with development of prospective intelligent decision making support systems (IDMSS) in analysis and diagnostics of complex problem situations taking complex vision pathologies as an example. The aim of the research is to develop an IDMSS prototype that would make it possible to automatically draw conclusions about a possible diagnosis based on the results of medical studies obtained from a special apparatus (an electroretinograph), patient examination results, and the knowledge of expert physiologists. Electroretinography is a method for assessing the functional state of the retina, which is based on recording the biopotentials arising in it during light stimulation, and the curve that images them is called the electroretinogram. The applied methods include those for constructing ontologies, neural networks, inverse development of applications, and constructing an IDMSS based on expert knowledge and human-machine interfaces. Methods for preliminary (preprocessor) processing of large data and for arranging their storage in a dedicated database and for displaying the results (diagnostic solutions) produced by the system are also considered. The novelty of the proposed approach consists in integrating several methods and in justifying the use of ontology and nonrelational model (database) in terms of expanding their scope at the early stages of the disease and improving the quality of diagnosing visual pathologies. The developed software tools of the IDMSS prototype are pointed out. The presented studies and developments have been carried out jointly by the Department of Applied Mathematics and Artificial Intelligence of the National Research University Moscow Power Engineering Institute (NRU MPEI) and the Department of Clinical Physiology of Vision of the Helmholtz Moscow Research Institute of Eye Diseases.
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Mahiddin, N., Z. A. Othman, and A. A. Bakar. "An architecture of Multiagent System (MAS) for healthcare Intelligent Decision Support System (IDSS)." Journal of Fundamental and Applied Sciences 9, no. 5S (January 19, 2018): 144. http://dx.doi.org/10.4314/jfas.v9i5s.12.
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Teghem, Jacques, C. Delhaye, and Pierre L. Kunsch. "An interactive decision support system (IDSS) for multicriteria decision aid." Mathematical and Computer Modelling 12, no. 10-11 (1989): 1311–20. http://dx.doi.org/10.1016/0895-7177(89)90370-1.
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Liu, Hongyu, and Bo Lang. "Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey." Applied Sciences 9, no. 20 (October 17, 2019): 4396. http://dx.doi.org/10.3390/app9204396.
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Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.
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Singh, Raman, Harish Kumar, Ravinder Kumar Singla, and Ramachandran Ramkumar Ketti. "Internet attacks and intrusion detection system." Online Information Review 41, no. 2 (April 10, 2017): 171–84. http://dx.doi.org/10.1108/oir-12-2015-0394.
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Purpose The paper addresses various cyber threats and their effects on the internet. A review of the literature on intrusion detection systems (IDSs) as a means of mitigating internet attacks is presented, and gaps in the research are identified. The purpose of this paper is to identify the limitations of the current research and presents future directions for intrusion/malware detection research. Design/methodology/approach The paper presents a review of the research literature on IDSs, prior to identifying research gaps and limitations and suggesting future directions. Findings The popularity of the internet makes it vulnerable against various cyber-attacks. Ongoing research on intrusion detection methods aims to overcome the limitations of earlier approaches to internet security. However, findings from the literature review indicate a number of different limitations of existing techniques: poor accuracy, high detection time, and low flexibility in detecting zero-day attacks. Originality/value This paper provides a review of major issues in intrusion detection approaches. On the basis of a systematic and detailed review of the literature, various research limitations are discovered. Clear and concise directions for future research are provided.
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Raman, M. R. Gauthama, K. Kannan, S. K. Pal, and V. S. Shankar Sriram. "Rough Set-hypergraph-based Feature Selection Approach for Intrusion Detection Systems." Defence Science Journal 66, no. 6 (October 31, 2016): 612. http://dx.doi.org/10.14429/dsj.66.10802.
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Immense growth in network-based services had resulted in the upsurge of internet users, security threats and cyber-attacks. Intrusion detection systems (IDSs) have become an essential component of any network architecture, in order to secure an IT infrastructure from the malicious activities of the intruders. An efficient IDS should be able to detect, identify and track the malicious attempts made by the intruders. With many IDSs available in the literature, the most common challenge due to voluminous network traffic patterns is the curse of dimensionality. This scenario emphasizes the importance of feature selection algorithm, which can identify the relevant features and ignore the rest without any information loss. In this paper, a novel rough set κ-Helly property technique (RSKHT) feature selection algorithm had been proposed to identify the key features for network IDSs. Experiments carried using benchmark KDD cup 1999 dataset were found to be promising, when compared with the existing feature selection algorithms with respect to reduct size, classifier’s performance and time complexity. RSKHT was found to be computationally attractive and flexible for massive datasets.
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Vimala, S., V. Khanna, and C. Nalini. "A determinant fuzzy apparoach for intrusion detection system." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 245. http://dx.doi.org/10.14419/ijet.v7i1.9.10006.
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In MANETs, versatile hubs can impart transparently to each other without the need of predefined framework. Interruption location framework is a fundamental bit of security for MANETs. It is uncommonly convincing for identifying the Intrusions and for the most part used to supplement for other security segment. That is the reason Intrusion discovery framework (IDS) is known as the second mass of assurance for any survivable framework security. The proposed fluffy based IDSs for recognition of Intrusions in MANETs are not prepared to adjust up all sort of assaults. We have examined that all proposed fluffy based IDSs are seen as to a great degree obliged segments or qualities for data collection which is specific for a particular assault. So that these IDSs are simply recognize the particular assault in MANETs. The fluffy motor may perceive blockage from channel mistake conditions, and along these lines helps the TCP blunder discovery. Examination has been made on the issues for upgrading the steady quality and precision of the decisions in MANET. This approach offers a strategy for joining remote units' estimation comes to fruition with alliance information open or priori decided at conglomerating hubs. In our investigation work, the best need was to reduce the measure of information required for getting ready and the false alarm rate. We are chiefly endeavoring to improve the execution of a present framework rather than endeavoring to supplant current Intrusion recognition systems with an information mining approach. While current mark based Intrusion identification procedures have imperatives as communicated in the past region, they do even now give basic organizations and this normal us to choose how information mining could be used as a piece of a correlative way to deal with existing measures and improves it.
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Fishbein, Dawn A., Ian Brooks, Emanuel Villa Baca, Ozgur Ozmen, Mallikarjun Shankar, Gil Weigand, Kristina Thiagarajan, et al. "2354." Journal of Clinical and Translational Science 1, S1 (September 2017): 15–16. http://dx.doi.org/10.1017/cts.2017.70.
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OBJECTIVES/SPECIFIC AIMS: Hepatitis C viral (HCV) infections are rising significantly both in young adults and as newly diagnosed cases in “baby boomers.” New HCV therapeutics cure over 95% of cases, and a call has been made for elimination of the epidemic by 2030; yet major HCV cascade of care (CoC) barriers exist. We secured CTSA pilot funding to obtain preliminary data for an innovative clinical trial utilizing big data modeling toward HCV elimination. METHODS/STUDY POPULATION: Our pilot work has developed a coordinated, real-time clinical data management process across 3 major CTSA affiliated hospital systems (MedStar Health, Emory-Grady, and UT-Southwestern), and additional data will be obtained from a pragmatic clinical trial. Electronic medical records data will be mapped to the OHDSI model, securely transmitted to Oak Ridge National Laboratory, Knoxville, TN and exposed to integrated data, analytics, modeling and simulation (IDAMS). RESULTS/ANTICIPATED RESULTS: Our U01 CTSA application proposes that HCV-IDAMS will model modifications to the established HCV CoC at community and population levels and thus simulate future outcomes. As data volume increases, system knowledge will expand and recursive applications of IDAMS will increase the accuracy of our models. This will reveal real-world reactions contingent upon population dynamics and composition, geographies, and local applications of the HCV CoC. DISCUSSION/SIGNIFICANCE OF IMPACT: Only an innovative, integrated approach harnessing pragmatic clinical data, big data and supercomputing power can create a realistic model toward HCV elimination.
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Al-Gethami, Khalid M., Mousa T. Al-Akhras, and Mohammed Alawairdhi. "Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets." Security and Communication Networks 2021 (January 15, 2021): 1–28. http://dx.doi.org/10.1155/2021/8836057.
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Optimizing the detection of intrusions is becoming more crucial due to the continuously rising rates and ferocity of cyber threats and attacks. One of the popular methods to optimize the accuracy of intrusion detection systems (IDSs) is by employing machine learning (ML) techniques. However, there are many factors that affect the accuracy of the ML-based IDSs. One of these factors is noise, which can be in the form of mislabelled instances, outliers, or extreme values. Determining the extent effect of noise helps to design and build more robust ML-based IDSs. This paper empirically examines the extent effect of noise on the accuracy of the ML-based IDSs by conducting a wide set of different experiments. The used ML algorithms are decision tree (DT), random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and Naïve Bayes (NB). In addition, the experiments are conducted on two widely used intrusion datasets, which are NSL-KDD and UNSW-NB15. Moreover, the paper also investigates the use of these ML algorithms as base classifiers with two ensembles of classifiers learning methods, which are bagging and boosting. The detailed results and findings are illustrated and discussed in this paper.
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Priyadarsini, Pullagura Indira, and G. Anuradha. "A novel ensemble modeling for intrusion detection system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1963. http://dx.doi.org/10.11591/ijece.v10i2.pp1963-1971.
Повний текст джерелаАнотація:
Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs. And hence our system is robust and efficient.
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Hidouri, Abdelhak, Nasreddine Hajlaoui, Haifa Touati, Mohamed Hadded, and Paul Muhlethaler. "A Survey on Security Attacks and Intrusion Detection Mechanisms in Named Data Networking." Computers 11, no. 12 (December 14, 2022): 186. http://dx.doi.org/10.3390/computers11120186.
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Despite the highly secure content sharing and the optimized forwarding mechanism, the content delivery in a Named Data Network (NDN) still suffers from numerous vulnerabilities that can be exploited to reduce the efficiency of such architecture. Malicious attacks in NDN have become more sophisticated and the foremost challenge is to identify unknown and obfuscated malware, as the malware authors use different evasion techniques for information concealing to prevent detection by an Intrusion Detection System (IDS). For the most part, NDN faces immense negative impacts from attacks such as Cache Pollution Attacks (CPA), Cache Privacy Attacks, Cache Poisoning Attacks, and Interest Flooding Attacks (IFA), that target different security components, including availability, integrity, and confidentiality. This poses a critical challenge to the design of IDS in NDN. This paper provides the latest taxonomy, together with a review of the significant research works on IDSs up to the present time, and a classification of the proposed systems according to the taxonomy. It provides a structured and comprehensive overview of the existing IDSs so that a researcher can create an even better mechanism for the previously mentioned attacks. This paper discusses the limits of the techniques applied to design IDSs with recent findings that can be further exploited in order to optimize those detection and mitigation mechanisms.
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Sun, Hong Fei, Wei Hou, Yan Yan Wang, and Rui Ling Lu. "Thermal Power Plant Bidding Strategies IDSS Analysis and Design." Advanced Materials Research 717 (July 2013): 899–903. http://dx.doi.org/10.4028/www.scientific.net/amr.717.899.
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The bidding decision-making of power plant was a complicated system engineering which had high complexity and real-time, so power plant must rely on the support of information technology to complete the bidding. Therefore, how to use all kinds of advanced information technologies had become one of topics which broad power enterprises must face. This paper established a kind of intelligent decision support system (IDSS) which was based on multi-agent intelligent decision support system and combined with the data warehouse, data mining and on-line analysis processing. Compared with the former information management, IDSS had so many advantages in auxiliary support for decision-making. If it was applied to power plant bidding strategies or other semi-structural and unstructured problems in power market, it would provide good auxiliary support for them.
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Abdel-Basset, Mohamed, Abduallah Gamal, Karam M. Sallam, Ibrahim Elgendi, Kumudu Munasinghe, and Abbas Jamalipour. "An Optimization Model for Appraising Intrusion-Detection Systems for Network Security Communications: Applications, Challenges, and Solutions." Sensors 22, no. 11 (May 29, 2022): 4123. http://dx.doi.org/10.3390/s22114123.
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Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations currently utilize a variety of tactics, strategies, and technology to protect the systems’ credibility in order to combat these dangers. Safeguarding approaches include establishing rules and procedures, developing user awareness, deploying firewall and verification systems, regulating system access, and forming computer-issue management groups. The effectiveness of intrusion-detection systems is not sufficiently recognized. IDS is used in businesses to examine possibly harmful tendencies occurring in technological environments. Determining an effective IDS is a complex task for organizations that require consideration of many key criteria and their sub-aspects. To deal with these multiple and interrelated criteria and their sub-aspects, a multi-criteria decision-making (MCMD) approach was applied. These criteria and their sub-aspects can also include some ambiguity and uncertainty, and thus they were treated using q-rung orthopair fuzzy sets (q-ROFS) and q-rung orthopair fuzzy numbers (q-ROFNs). Additionally, the problem of combining expert and specialist opinions was dealt with using the q-rung orthopair fuzzy weighted geometric (q-ROFWG). Initially, the entropy method was applied to assess the priorities of the key criteria and their sub-aspects. Then, the combined compromised solution (CoCoSo) method was applied to evaluate six IDSs according to their effectiveness and reliability. Afterward, comparative and sensitivity analyses were performed to confirm the stability, reliability, and performance of the proposed approach. The findings indicate that most of the IDSs appear to be systems with high potential. According to the results, Suricata is the best IDS that relies on multi-threading performance.
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Khan, Zeeshan Ali, and Peter Herrmann. "Recent Advancements in Intrusion Detection Systems for the Internet of Things." Security and Communication Networks 2019 (July 3, 2019): 1–19. http://dx.doi.org/10.1155/2019/4301409.
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Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.
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Song, Yajie, Bing Bu, and Li Zhu. "A Novel Intrusion Detection Model Using a Fusion of Network and Device States for Communication-Based Train Control Systems." Electronics 9, no. 1 (January 18, 2020): 181. http://dx.doi.org/10.3390/electronics9010181.
Повний текст джерелаАнотація:
Security is crucial in cyber-physical systems (CPS). As a typical CPS, the communication-based train control (CBTC) system is facing increasingly serious cyber-attacks. Intrusion detection systems (IDSs) are vital to protect the system against cyber-attacks. The traditional IDS cannot distinguish between cyber-attacks and system faults. Furthermore, the design of the traditional IDS does not take the principles of CBTC systems into consideration. When deployed, it cannot effectively detect cyber-attacks against CBTC systems. In this paper, we propose a novel intrusion detection method that considers both the status of the networks and those of the equipment to identify if the abnormality is caused by cyber-attacks or by system faults. The proposed method is verified on a hardware-in-the-loop simulation platform of CBTC systems. Simulation results indicate that the proposed method has achieved 97.64% true positive rate, which can significantly improve the security protection level of CBTC systems.
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Popa, Ioan Gabriel, and Leontin Stanciu. "Short Considerations On The Integrated Defense Procurement Management System Of Romania." International conference KNOWLEDGE-BASED ORGANIZATION 21, no. 2 (June 1, 2015): 354–59. http://dx.doi.org/10.1515/kbo-2015-0059.
Повний текст джерелаАнотація:
Abstract In the current context and along with the emergence and implementation of the Government Emergency Ordinance no. 114/2011 regarding the award of certain public contracts in the fields of defense and security, ordinance imposing the mandatory application of art. 288 of the Treaty on the Functioning of the European Union concerning the transposition of the Union’s directives, both purchases of products, works and services and the purchase of military products are regulated. In order to implement this ordinance, the MoD is “asked” to rethink the whole system of defense procurement. In my opinion, the current Integrated Defense Procurement Management System (IDAMS) consists of Technical-Operational Requirements System, Planning, Programming, Budgeting and Evaluation System, and Integrated Procurement Management System even if it has introduced a series of instructions, policies, principles and procedures based on defense procurements and respond only partially to the current reality specifically referring to the purchase of arms and ammunition from their own production and less to their procurement from external suppliers. For this reason, we believe that IDAMS must be known and understood, which is exactly what the article below intends to de, and ultimately, this system should be changed, the by amending general and specific legislation.