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Academic literature on the topic 'Light-based Intrusion classification system'

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Published: 6 September 2023

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Journal articles on the topic "Light-based Intrusion classification system"

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Jecheva, Veselina, and Evgeniya Nikolova. "Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems." Serdica Journal of Computing 3, no. 4 (January 11, 2010): 335–58. http://dx.doi.org/10.55630/sjc.2009.3.335-358.

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Intrusion detection is a critical component of security information systems. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. This paper examines the anomaly-based intrusion detection based on sequences of system calls. The point is to construct a model that describes normal or acceptable system activity using the classification trees approach. The created database is utilized as a basis for distinguishing the intrusive activity from the legal one using string metric algorithms. The major results of the implemented simulation experiments are presented and discussed as well.
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Sandosh, S., Dr V. Govindasamy, and Dr G. Akila. "Novel Pattern Matching based Alert Classification Approach For Intrusion Detection System." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (November 29, 2019): 279–89. http://dx.doi.org/10.5373/jardcs/v11sp11/20193032.

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Kamble, Arvind, and Virendra S. Malemath. "Adam Improved Rider Optimization-Based Deep Recurrent Neural Network for the Intrusion Detection in Cyber Physical Systems." International Journal of Swarm Intelligence Research 13, no. 3 (July 1, 2022): 1–22. http://dx.doi.org/10.4018/ijsir.304402.

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This paper designed the intrusion detection systems for determining the intrusions. Here, Adam Improved rider optimization approach (Adam IROA) is newly developed for detecting the intrusion in intrusion detection. Accordingly, the training of DeepRNN is done by proposed Adam IROA, which is designed by combining the Adam optimization algorithm with IROA. Thus, the newly developed Adam IROA is applied for intrusion detection. Overall, two phases are included in the proposed intrusion detection system, which involves feature selection and classification. Here, the features selection is done using proposed WWIROA to select significant features from the input data. The proposed WWIROA is developed by combining WWO and IROA. The obtained features are fed to the classification module for discovering the intrusions present in the network. Here, the classification is progressed using Adam IROA-based DeepRNN. The proposed Adam IROA-based DeepRNN achieves maximal accuracy of 0.937, maximal sensitivity of 0.952, and maximal specificity of 0.908 based on SCADA dataset.
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Ahmad, Iftikhar, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi, and Rayed A. AlGhamdi. "An Efficient Network Intrusion Detection and Classification System." Mathematics 10, no. 3 (February 8, 2022): 530. http://dx.doi.org/10.3390/math10030530.

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Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains.
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Mohammed, Bilal, and Ekhlas K. Gbashi. "Intrusion Detection System for NSL-KDD Dataset Based on Deep Learning and Recursive Feature Elimination." Engineering and Technology Journal 39, no. 7 (July 25, 2021): 1069–79. http://dx.doi.org/10.30684/etj.v39i7.1695.

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Intrusion detection system is responsible for monitoring the systems and detect attacks, whether on (host or on a network) and identifying attacks that could come to the system and cause damage to them, that’s mean an IDS prevents unauthorized access to systems by giving an alert to the administrator before causing any serious harm. As a reasonable supplement of the firewall, intrusion detection technology can assist systems to deal with offensive, the Intrusions Detection Systems (IDSs) suffers from high false positive which leads to highly bad accuracy rate. So this work is suggested to implement (IDS) by using a Recursive Feature Elimination to select features and use Deep Neural Network (DNN) and Recurrent Neural Network (RNN) for classification, the suggested model gives good results with high accuracy rate reaching 94%, DNN was used in the binary classification to classify either attack or Normal, while RNN was used in the classifications for the five classes (Normal, Dos, Probe, R2L, U2R). The system was implemented by using (NSL-KDD) dataset, which was very efficient for offline analyses systems for IDS.
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Ali, Rashid, and Supriya Kamthania. "A Comparative Study of Different Relevant Features Hybrid Neural Networks Based Intrusion Detection Systems." Advanced Materials Research 403-408 (November 2011): 4703–10. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4703.

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Intrusion detection is the task of detecting, preventing and possibly reacting to the attacks and intrusions in a network based computer system. The neural network algorithms are popular for their ability to ’learn’ the so called patterns in a given environment. This feature can be used for intrusion detection, where the neural network can be trained to detect intrusions by recognizing patterns of an intrusion. In this work, we propose and compare the three different Relevant Features Hybrid Neural Networks based intrusion detection systems, where in, we first recognize the most relevant features for a connection record from a benchmark dataset and use these features in training the hybrid neural networks for intrusion detection. Performance of these three systems are evaluated on a well structured KDD CUP 99 dataset using a number of evaluation parameters such as classification rate, false positive rate, false negative rate etc.
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Ugendhar, A., Babu Illuri, Sridhar Reddy Vulapula, Marepalli Radha, Sukanya K, Fayadh Alenezi, Sara A. Althubiti, and Kemal Polat. "A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification." Mathematical Problems in Engineering 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/8030510.

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Cybersecurity in information technology (IT) infrastructures is one of the most significant and complex issues of the digital era. Increases in network size and associated data have directly affected technological breakthroughs in the Internet and communication areas. Malware attacks are becoming increasingly sophisticated and hazardous as technology advances, making it difficult to detect an incursion. Detecting and mitigating these threats is a significant issue for standard analytic methods. Furthermore, the attackers use complex processes to remain undetected for an extended period. The changing nature and many cyberattacks require a quick, adaptable, and scalable defense system. For the most part, traditional machine learning-based intrusion detection relies on only one algorithm to identify intrusions, which has a low detection rate and cannot handle large amounts of data. To enhance the performance of intrusion detection systems, a new deep multilayer classification approach is developed. This approach comprises five modules: preprocessing, autoencoding, database, classification, and feedback. The classification module uses an autoencoder to decrease the number of dimensions in a reconstruction feature. Our method was tested against a benchmark dataset, NSL-KDD. Compared to other state-of-the-art intrusion detection systems, our methodology has a 96.7% accuracy.
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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (December 31, 2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.
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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (December 31, 2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.
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Alzahrani, Mohammed Saeed, and Fawaz Waselallah Alsaade. "Computational Intelligence Approaches in Developing Cyberattack Detection System." Computational Intelligence and Neuroscience 2022 (March 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/4705325.

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The Internet plays a fundamental part in relentless correspondence, so its applicability can decrease the impact of intrusions. Intrusions are defined as movements that unfavorably influence the focus of a computer. Intrusions may sacrifice the reputability, integrity, privacy, and accessibility of the assets attacked. A computer security system will be traded off when an intrusion happens. The novelty of the proposed intelligent cybersecurity system is its ability to protect Internet of Things (IoT) devices and any networks from incoming attacks. In this research, various machine learning and deep learning algorithms, namely, the quantum support vector machine (QSVM), k-nearest neighbor (KNN), linear discriminant and quadratic discriminant long short-term memory (LSTM), and autoencoder algorithms, were applied to detect attacks from signature databases. The correlation method was used to select important network features by finding the features with a high-percentage relationship between the dataset features and classes. As a result, nine features were selected. A one-hot encoding method was applied to convert the categorical features into numerical features. The validation of the system was verified by employing the benchmark KDD Cup database. Statistical analysis methods were applied to evaluate the results of the proposed study. Binary and multiple classifications were conducted to classify the normal and attack packets. Experimental results demonstrated that KNN and LSTM algorithms achieved better classification performance for developing intrusion detection systems; the accuracy of KNN and LSTM algorithms for binary classification was 98.55% and 97.28%, whereas the KNN and LSTM attained a high accuracy for multiple classification (98.28% and 970.7%). Finally, the KNN and LSTM algorithms are fitting-based intrusion detection systems.
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Dissertations / Theses on the topic "Light-based Intrusion classification system"

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Lee, Keum-Chang. "Design of an intrusion detection system based on a fuzzy classification and voting approach." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506587.

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Al, Tobi Amjad Mohamed. "Anomaly-based network intrusion detection enhancement by prediction threshold adaptation of binary classification models." Thesis, University of St Andrews, 2018. http://hdl.handle.net/10023/17050.

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Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known Machine Learning algorithms: C5.0, Random Forest, and Support Vector Machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model's accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall accuracy rate of nearly 90% of the optimal threshold.
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Shafi, Kamran Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "An online and adaptive signature-based approach for intrusion detection using learning classifier systems." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38991.

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This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, which are referred to as signatures, from network traffic through a supervised learning classifier system, UCS. The rules are learnt dynamically (i.e., using machine intelligence and without the requirement of a domain expert), and adaptively (i.e., as the data arrives without the need to relearn the complete model after presenting each data instance to the current model). Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt. The rule based profiling of normal behaviour allows for anomaly detection in that the events not matching any of the rules are considered potentially harmful and could be escalated for an action. We study the effect of key UCS parameters and operators on its performance and identify areas of improvement through this analysis. Several new heuristics are proposed that improve the effectiveness of UCS for the prediction of unseen and extremely rare intrusive activities. A signature extraction system is developed that adaptively retrieves signatures as they are discovered by UCS. The signature extraction algorithm is augmented by introducing novel subsumption operators that minimise overlap between signatures. Mechanisms are provided to adapt the main algorithm parameters to deal with online noisy and imbalanced class data. The performance of UCS, its variants and the signature extraction system is measured through standard evaluation metrics on a publicly available intrusion detection dataset provided during the 1999 KDD Cup intrusion detection competition. We show that the extended UCS significantly improves test accuracy and hit rate while significantly reducing the rate of false alarms and cost per example scores than the standard UCS. The results are competitive to the best systems participated in the competition in addition to our systems being online and incremental rule learners. The signature extraction system built on top of the extended UCS retrieves a magnitude smaller rule set than the base UCS learner without any significant performance loss. We extend the evaluation of our systems to real time network traffic which is captured from a university departmental server. A methodology is developed to build fully labelled intrusion detection dataset by mixing real background traffic with attacks simulated in a controlled environment. Tools are developed to pre-process the raw network data into feature vector format suitable for UCS and other related machine learning systems. We show the effectiveness of our feature set in detecting payload based attacks.
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Silva, Eduardo Germano da. "A one-class NIDS for SDN-based SCADA systems." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/164632.

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Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo.
Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.
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Noordhuis-Fairfax, Sarina. "Field | Guide: John Berger and the diagrammatic exploration of place." Phd thesis, Canberra, ACT : The Australian National University, 2018. http://hdl.handle.net/1885/154278.

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Positioned between writing and drawing, the diagram is proposed by John Berger as an alternative strategy for articulating encounters with landscape. A diagrammatic approach offers a schematic vocabulary that can compress time and offer a spatial reading of information. Situated within the contemporary field of direct data visualisation, my practice-led research interprets Berger’s ‘Field’ essay as a guide to producing four field | studies within a suburban park in Canberra. My seasonal investigations demonstrate how applying the conventions of the pictorial list, dot-distribution map, routing diagram and colour-wheel reveals subtle ecological and biographical narratives.
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Tseng, Hung-Lin, and 曾鴻麟. "An Ensemble Based Classification Algorithm for Network Intrusion Detection System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/16771777095571370354.

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碩士
國防大學理工學院
資訊科學碩士班
99
In the environment of changing information security threats, an intrusion detection system (IDS) is an important line of defense. With the continuous progress of information technology, the network speed and throughput are also increasing. There are hundreds of thousands of packets per second in the network. Taking both information security and network quality into account are a very important issue. In recent years, data mining technology becomes very popular and is applied in various fields successfully. Data mining can discover the useful information from a large volume of data. The current research tends to apply data mining technology in constructing the IDSs. However, many challenges still exist to be overcomed in the field of data mining-based IDSs, such as the imbalanced data sets, poor detection rate of the minority class, and low accuracy rate, etc. Therefore, by integrating the data selection, sampling, and feature selection methods, this thesis proposes an “Enhanced Integrated Learning” algorithm and an “EIL-Algorithm Based Ensemble System” to strengthen the classification model and its performance. This thesis uses KDD99 data set as the experiment data source. A series of experiments are conducted to show that the proposed algorithms can enhance the classification performance of the minority class. For U2R attack class, Recall and F-measure are 57.01% and 38.98%, respectively, which shows the classification performance for U2R attack class is effectively improved. Meanwhile, the overall classification performance of anomaly network-based IDS is enhanced.
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HUANG, HUI-YING, and 黃蕙嫈. "Classification of Intrusion Detection System Based on Machine Learning Technology." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/pz9b2z.

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Choubisa, Tarun. "Design, Development, Deployment and Performance Evaluation of Pyroelectric Infra-Red and Optical Camera based Intrusion Detection Systems in an Outdoor Setting." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5306.

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The primary contribution of this thesis is the comprehensive design, development, and deployment of a pyroElectric, InfraRed-sensor-based IntruSion classification (EIRIS) platform, along with attendant detection and classification algorithms and performance evaluation. While the use of Pyroelectric InfraRed (PIR) sensors to detect human motion in an indoor setting has been extensively studied in the literature, there is considerably less research in comparison which deals with the use of PIR sensor in an outdoor environment. The outdoor environment poses additional challenges in the form of the presence of animals, moving vegetative clutter and environments in which the ambient temperature is close to that of the human body. An additional contribution of the thesis, is the development of a separate, optical-camera-based sensing platform termed as LITE (short for Light-based Intrusion classificaion system) which is designed to be used as a sensing modality complementary to the EIRIS platform, that could step in situations where the accuracy of the EIRIS platform was compromised by adverse ambient-temperature conditions
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Weigert, Stefan. "Community-Based Intrusion Detection." Doctoral thesis, 2015. https://tud.qucosa.de/id/qucosa%3A30127.

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Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the adversaries manage to steal important data or disrupt vital services. Victims are preferably companies from vital industries, such as banks, defense contractors, or power plants. Given that these industries are well-protected, often employing a team of security specialists, the question is: How can these attacks be so successful? Researchers have identified several properties of APT attacks which make them so efficient. First, they are adaptable. This means that they can change the way they attack and the tools they use for this purpose at any given moment in time. Second, they conceal their actions and communication by using encryption, for example. This renders many defense systems useless as they assume complete access to the actual communication content. Third, their actions are stealthy — either by keeping communication to the bare minimum or by mimicking legitimate users. This makes them “fly below the radar” of defense systems which check for anomalous communication. And finally, with the goal to increase their impact or monetisation prospects, their attacks are targeted against several companies from the same industry. Since months can pass between the first attack, its detection, and comprehensive analysis, it is often too late to deploy appropriate counter-measures at businesses peers. Instead, it is much more likely that they have already been attacked successfully. This thesis tries to answer the question whether the last property (industry-wide attacks) can be used to detect such attacks. It presents the design, implementation and evaluation of a community-based intrusion detection system, capable of protecting businesses at industry-scale. The contributions of this thesis are as follows. First, it presents a novel algorithm for community detection which can detect an industry (e.g., energy, financial, or defense industries) in Internet communication. Second, it demonstrates the design, implementation, and evaluation of a distributed graph mining engine that is able to scale with the throughput of the input data while maintaining an end-to-end latency for updates in the range of a few milliseconds. Third, it illustrates the usage of this engine to detect APT attacks against industries by analyzing IP flow information from an Internet service provider. Finally, it introduces a detection algorithm- and input-agnostic intrusion detection engine which supports not only intrusion detection on IP flow but any other intrusion detection algorithm and data-source as well.
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Saradha, R. "Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/3129.

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In the last decade, a lot of machine learning and data mining based approaches have been used in the areas of intrusion detection, malware detection and classification and also traffic analysis. In the area of malware analysis, static binary analysis techniques have become increasingly difficult with the code obfuscation methods and code packing employed when writing the malware. The behavior-based analysis techniques are being used in large malware analysis systems because of this reason. In prior art, a number of clustering and classification techniques have been used to classify the malwares into families and to also identify new malware families, from the behavior reports. In this thesis, we have analysed in detail about the use of Profile Hidden Markov models for the problem of malware classification and clustering. The advantage of building accurate models with limited examples is very helpful in early detection and modeling of malware families. The thesis also revisits the learning setting of an Intrusion Detection System that employs machine learning for identifying attacks and normal traffic. It substantiates the suitability of incremental learning setting(or stream based learning setting) for the problem of learning attack patterns in IDS, when large volume of data arrive in a stream. Related to the above problem, an elaborate survey of the IDS that use data mining and machine learning was done. Experimental evaluation and comparison show that in terms of speed and accuracy, the stream based algorithms perform very well as large volumes of data are presented for classification as attack or non-attack patterns. The possibilities for using stream algorithms in different problems in security is elucidated in conclusion.
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Books on the topic "Light-based Intrusion classification system"

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Sabri, Omar, and Martin Bircher. Management of limb and pelvic injuries. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0336.

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Pelvic ring injuries can be life and limb threatening. The mechanism of injury can often be a good indicator of the type of injury; the Young & Burgess classification deploys that concept to full effect. Early identification based on mechanism of injury and improved prehospital care can play a major role in the outcome following such injuries. Pelvic ring injuries can lead to significant haemorrhage. Mechanical measures to stabilize the pelvis, in addition to modern concepts of damage control resuscitation (DCR), have been shown to be effective in early management of potentially life-threatening haemorrhage. Emphasis is now entirely on protecting the primary clot following a pelvic ring injury. Mechanical disturbance by log rolling the patient or springing of the pelvis are strongly discouraged. Early radiological clearance of the pelvis is encouraged. The lethal triad of coagulopathy, acidosis, and hypothermia should be corrected simultaneously to improve outcome. A traffic light system for monitoring venous lactate as an indicator of the patients’ physiological state can help the intensive care practitioner and the surgeon identify optimum timing for surgery. Pelvic ring injuries are associated with significant concomitant injuries. Limb trauma can also be life or limb threatening. Early identification, splinting, and resuscitation follow the same guidelines as pelvic ring injuries. Open long bone fractures should be managed by senior orthopaedic and plastic surgeons.
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Book chapters on the topic "Light-based Intrusion classification system"

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Chuang, Hsiu-Min, Hui-Ying Huang, Fanpyn Liu, and Chung-Hsien Tsai. "Classification of Intrusion Detection System Based on Machine Learning." In Communications in Computer and Information Science, 492–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6113-9_55.

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Shin, Moon Sun, Eun Hee Kim, and Keun Ho Ryu. "False Alarm Classification Model for Network-Based Intrusion Detection System." In Lecture Notes in Computer Science, 259–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_38.

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Nadiammai, G. V., and M. Hemalatha. "Performance Analysis of Tree Based Classification Algorithms for Intrusion Detection System." In Mining Intelligence and Knowledge Exploration, 82–89. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_9.

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Wang, Yunpeng, Yuzhou Li, Daxin Tian, Congyu Wang, Wenyang Wang, Rong Hui, Peng Guo, and Haijun Zhang. "A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 581–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72823-0_53.

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Mehrotra, Latika, Prashant Sahai Saxena, and Nitika Vats Doohan. "A Data Classification Model: For Effective Classification of Intrusion in an Intrusion Detection System Based on Decision Tree Learning Algorithm." In Information and Communication Technology for Sustainable Development, 61–66. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3932-4_7.

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Shyu, Mei-Ling, and Varsha Sainani. "A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification." In Data Mining and Multi-agent Integration, 127–42. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2_8.

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Vitorino, João, Rui Andrade, Isabel Praça, Orlando Sousa, and Eva Maia. "A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection." In Foundations and Practice of Security, 191–207. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08147-7_13.

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AbstractThe digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQIN), adapted to the intrusion detection context. The most reliable performance was achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection results and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.
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Alasad, Qutaiba, Maytham M. Hammood, and Shahad Alahmed. "Performance and Complexity Tradeoffs of Feature Selection on Intrusion Detection System-Based Neural Network Classification with High-Dimensional Dataset." In Lecture Notes in Networks and Systems, 533–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25274-7_45.

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Liu, Fang, and Yun Tian. "Intrusion Detection Based on Clustering Organizational Co-Evolutionary Classification." In Fuzzy Systems and Knowledge Discovery, 1113–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_139.

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Nanda, Manas Kumar, and Manas Ranjan Patra. "Intrusion Detection and Classification Using Decision Tree-Based Feature Selection Classifiers." In Smart Innovation, Systems and Technologies, 157–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6202-0_17.

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Conference papers on the topic "Light-based Intrusion classification system"

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Shang-fu, Gong, and Zhao Chun-lan. "Intrusion detection system based on classification." In 2012 IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment (ICADE). IEEE, 2012. http://dx.doi.org/10.1109/icade.2012.6330103.

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Belhor, Mariem, and Farah Jemili. "Intrusion detection based on genetic fuzzy classification system." In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945690.

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Jabbar, M. A., Rajanikanth Aluvalu, and S. Sai Satyanarayana Reddy. "Cluster Based Ensemble Classification for Intrusion Detection System." In ICMLC 2017: 2017 the 9th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3055635.3056595.

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Sharma, Rachana, Priyanka Sharma, Preeti Mishra, and Emmanuel S. Pilli. "Towards MapReduce based classification approaches for Intrusion Detection." In 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence). IEEE, 2016. http://dx.doi.org/10.1109/confluence.2016.7508144.

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Gupta, Prabhav, Yash Ghatole, and Nihal Reddy. "Stacked Autoencoder based Intrusion Detection System using One-Class Classification." In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377069.

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Ogundokun, Roseline Oluwaseun, Sanjay Misra, Akinbowale Nathaniel Babatunde, and Sabarathinam Chockalingam. "Cyber Intrusion Detection System based on Machine Learning Classification Approaches." In 2022 International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2022. http://dx.doi.org/10.1109/icapai55158.2022.9801566.

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Singh, Abhay Pratap, Sanjeev Kumar, Amit Kumar, and Mohd Usama. "Machine Learning based Intrusion Detection System for Minority Attacks Classification." In 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES). IEEE, 2022. http://dx.doi.org/10.1109/cises54857.2022.9844381.

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Effendy, David Ahmad, Kusrini Kusrini, and Sudarmawan Sudarmawan. "Classification of intrusion detection system (IDS) based on computer network." In 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285566.

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Kumar, Sanjay, Ari Viinikainen, and Timo Hamalainen. "Machine learning classification model for Network based Intrusion Detection System." In 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2016. http://dx.doi.org/10.1109/icitst.2016.7856705.

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Subba, Basant, Santosh Biswas, and Sushanta Karmakar. "A Neural Network based system for Intrusion Detection and attack classification." In 2016 Twenty Second National Conference on Communication (NCC). IEEE, 2016. http://dx.doi.org/10.1109/ncc.2016.7561088.

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