Готові списки джерел за темами / Security attacks detection

Добірка наукової літератури з теми "Security attacks detection"

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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Статті в журналах з теми "Security attacks detection":

Jimmy, FNU. "Cyber security Vulnerabilities and Remediation Through Cloud Security Tools." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (April 12, 2024): 196–233. http://dx.doi.org/10.60087/jaigs.vol03.issue01.p233.

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The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Jimmy, Fnu. "Cyber security Vulnerabilities and Remediation Through Cloud Security Tools." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (April 12, 2024): 196–233. http://dx.doi.org/10.60087/jaigs.vol03.issue01.p234.

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Kumavat, Kavita S., and Joanne Gomes. "Common Mechanism for Detecting Multiple DDoS Attacks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (May 4, 2023): 81–90. http://dx.doi.org/10.17762/ijritcc.v11i4.6390.

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An important principle of an internet-based system is information security. Information security is a very important aspect of distributed systems and IoT (Internet of Things) based wireless systems. The attack which is more harmful to the distributed system and IoT-based wireless system is a DDoS (Distributed Denial of Service) attack since in this attack, an attacker can stop the work of all other connected devices or users to the network. For securing distributed applications, various intrusion detection mechanisms are used. But most existing mechanisms are only concentrated on one kind of DDoS attack. This paper focuses on the basic architecture of IoT systems and an overview of single intrusion detection systems. This paper presents a single detection method for different DDoS attacks on distributed systems with an IoT interface. In the future, the system will provide support for detecting and preventing different DDoS attacks in IoT-based systems.

Kareem, Mohammed Ibrahim, Mohammad Jawad Kadhim Abood, and Karrar Ibrahim. "Machine learning-based PortScan attacks detection using OneR classifier." Bulletin of Electrical Engineering and Informatics 12, no. 6 (December 1, 2023): 3690–96. http://dx.doi.org/10.11591/eei.v12i6.4142.

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PortScan attacks are a common security threat in computer networks, where an attacker systematically scans a range of network ports on a target system to identify potential vulnerabilities. Detecting such attacks in a timely and accurate manner is crucial to ensure network security. Attackers can determine whether a port is open by sending a detective message to it, which helps them find potential vulnerabilities. However, the best methods for spotting and identifying port scanner attacks are those that use machine learning. One of the most dangerous online threats is PortScan attack, according to experts. The research is work on detection while improving detection accuracy. Dataset containing tags from network traffic is used to train machine learning techniques for classification. The JRip algorithm is trained and tested using the CICIDS2017 dataset. As a consequence, the best performance results for JRip-based detection schemes were 99.84%, 99.80%, 99.80%, and 0.09 ms for accuracy, precision, recall, F-score, and detection overhead, respectively. Finally, the comparison with current models demonstrated our model's proficiency and advantage with increased attack discovery speed.

Du, Dajun, Rui Chen, Xue Li, Lei Wu, Peng Zhou, and Minrui Fei. "Malicious data deception attacks against power systems: A new case and its detection method." Transactions of the Institute of Measurement and Control 41, no. 6 (January 8, 2018): 1590–99. http://dx.doi.org/10.1177/0142331217740622.

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Power systems usually employ bad data detection (BDD) to avoid faulty measurements caused by their anomalies, and hence can ensure the security of the state estimation of power systems. However, recently BDD has been found vulnerable to malicious data deception attacks submerged in big data. Such attacks can purposely craft sparse measurement values (i.e. attack vectors) to mislead power estimates, while not posing any anomalies to the BDD. Some related work has been proposed to emphasize this attack. In this paper, a new malicious data deception attack by considering a practical attacking situation is investigated, where the attacker has limited resources for corrupting measurements. In this case, attackers generate attack vectors with less sparsity to evade conventional BDD, while using a convex optimization method to balance the sparsity and magnitude of attack vectors. Accordingly, the effects of such an attack on operational costs and the risks of power systems are analysed in detail. Moreover, according to security evaluation for individual measurements, such attacks can be detected with high probability by just securing one critical measurement. Numerical simulations illustrate the effectiveness of the proposed new attack case and its detection method.

Kumar, Sunil, and Maninder Singh. "Detection and Isolation of Zombie Attack under Cloud Environment." Oriental journal of computer science and technology 10, no. 2 (April 12, 2017): 338–44. http://dx.doi.org/10.13005/ojcst/10.02.12.

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Network security, data security and several other security types such as the computer security collectively compose the word “Cloud Security”. Cloud computing posses a new challenge because traditional security mechanism is being followed are insufficient to safeguard the cloud resources. Cloud computing can easily be targeted by the attackers. A group of malicious users or illegitimate users can attack on system which may lead to denial the services of legitimate users. Such kinds of attacks are performed by the malicious (zombie) attackers. The zombie attack will degrade the network performance to large extend. Traditional techniques are not easily capable to detect the zombie attacker in the cloud network. So in this paper we have proposed a technique which is the enhancement of the mutual authentication scheme in order to detect and isolate zombie attack for the efficient performance of the network.

Farane Shradha, Gotane Rutuja, Chandanshive Sakshi, Agrawal Khushi, and Khandekar Srushti. "Detection of cyber-attacks and network attacks using Machine Learning." World Journal of Advanced Engineering Technology and Sciences 12, no. 1 (May 30, 2024): 128–32. http://dx.doi.org/10.30574/wjaets.2024.12.1.0184.

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The Internet and computer networks have become an important part of organizations and everyday life. New threats and challenges have emerged to wireless communication systems especially in cyber security and network attacks. The network traffic must be monitored and analysed to detect malicious activities and attacks. Recently, machine learning techniques have been applied toward the detection of network attacks. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phishing detection. As a result, effective adaptive methods, such as machine learning techniques, can yield higher detection rates, lower false alarm rates and cheaper computing and transmission costs. Our key goal is detection of cyber security and network attacks such as IDS, phishing and XSS, SQL injection, respectively. The proposed strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the system of the attack's existence in the early stages of the attack.

Aslan, Ömer, Semih Serkant Aktuğ, Merve Ozkan-Okay, Abdullah Asim Yilmaz, and Erdal Akin. "A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions." Electronics 12, no. 6 (March 11, 2023): 1333. http://dx.doi.org/10.3390/electronics12061333.

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Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to automate attacks and leverage their impact. Attackers exploit vulnerabilities that exist in hardware, software, and communication layers. Various types of cyber attacks include distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware. Due to new-generation attacks and evasion techniques, traditional protection systems such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer effective in detecting these sophisticated attacks. Therefore, there is an urgent need to find innovative and more feasible solutions to prevent cyber attacks. The paper first extensively explains the main reasons for cyber attacks. Then, it reviews the most recent attacks, attack patterns, and detection techniques. Thirdly, the article discusses contemporary technical and nontechnical solutions for recognizing attacks in advance. Using trending technologies such as machine learning, deep learning, cloud platforms, big data, and blockchain can be a promising solution for current and future cyber attacks. These technological solutions may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats. However, some promising solutions, especially machine learning and deep learning, are not resistant to evasion techniques, which must be considered when proposing solutions against intelligent cyber attacks.

Alamsyah, Hendri, Riska, and Abdussalam Al Akbar. "Analisa Keamanan Jaringan Menggunakan Network Intrusion Detection and Prevention System." JOINTECS (Journal of Information Technology and Computer Science) 5, no. 1 (January 25, 2020): 17. http://dx.doi.org/10.31328/jointecs.v5i1.1240.

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Security is an important aspect to be considered in computer networks. This security system can be a detection and prevention of attacks that are being done by the attacker (intruders). The problem of attacks that occur in computer networks is that intruders can do port scanning, enter the system using open ports such as telnet, ftp and others.. The purpose of this study is the implementation of IDPS, can be from. To do network security from various attack threats, a system that can detect and prevent it directly is needed. The method that can be used is Intrusion Detection and Prevention System (NIDPS). NIDPS can exchange and block the attacks. This security system is collaborated with IP Tables. IP Tables is used to filter incoming data packets and drop packets of data that are indicated by attack. With the Intrusion Detection and Prevention system, it can detect attacks and prevent them by blocking data packets sent by intruders through port scanning, FTP attacks, and telnets.

Salih, Azar Abid, and Maiwan Bahjat Abdulrazzaq. "Cyber security: performance analysis and challenges for cyber attacks detection." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (September 1, 2023): 1763. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1763-1775.

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Nowadays, with the occurrence of new attacks and raised challenges have been facing the security of computer systems. Cyber security techniques have become essential for information technology services to detect and react against cyber-attacks. The strategy of cyber security enables visibility of various types of attacks and vulnerabilities throughout computer networks, whilst also provides detecting cyber-attacks and effective ways of identifying and preventing them. This study mainly focuses on the performance analysis and challenges faced by cyber security using the latest techniques. It also provides a review of the attack detection process including the robust effectiveness of intelligent techniques. Finally, summarize and discuss some methods to increase attack detection performance utilizing deep learning (DL) architectures.

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Дисертації з теми "Security attacks detection":

Kazi, Shehab. "Anomaly based Detection of Attacks on Security Protocols." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4806.

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Abstract. Security and privacy in digital communications is the need of the hour. SSL/TLS has become widely adopted to provide the same. Multiple application layer protocols can be layered on top of it. However protection is this form results in all the data being encrypted causing problems for an intrusion detection system which relies on a sniffer that analyses packets on a network. We thus hypothesise that a host based intrusion detection system that analyses packets after decryption would be able to detect attacks against security protocols. To this effect we conduct two experiments where we attack a web server and a mail server, collect data, analyse it and conclude with methods to detect such attacks. These methods are in the form of peudocode.

Whitelaw, Clayton. "Precise Detection of Injection Attacks on Concrete Systems." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/6051.

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Injection attacks, including SQL injection, cross-site scripting, and operating system command injection, rank the top two entries in the MITRE Common Vulnerability Enumeration (CVE) [1]. Under this attack model, an application (e.g., a web application) uses some untrusted input to produce an output program (e.g., a SQL query). Applications may be vulnerable to injection attacks because the untrusted input may alter the output program in malicious ways. Recent work has established a rigorous definition of injection attacks. Injections are benign iff they obey the NIE property, which states that injected symbols strictly insert or expand noncode tokens in the output program. Noncode symbols are strictly those that are either removed by the tokenizer (e.g., insignificant whitespace) or span closed values in the output program language, and code symbols are all other symbols. This thesis demonstrates that such attacks are possible on applications for Android—a mobile device operating system—and Bash—a common Linux shell—and shows by construction that these attacks can be detected precisely. Specifically, this thesis examines the recent Shellshock attacks on Bash and shows how it widely differs from ordinary attacks, but can still be precisely detected by instrumenting the output program’s runtime. The paper closes with a discussion of the lessons learned from this study and how best to overcome the practical challenges to precisely preventing these attacks in practice.

Jan, Steve T. K. "Robustifying Machine Learning based Security Applications." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99862.

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In recent years, machine learning (ML) has been explored and employed in many fields. However, there are growing concerns about the robustness of machine learning models. These concerns are further amplified in security-critical applications — attackers can manipulate the inputs (i.e., adversarial examples) to cause machine learning models to make a mistake, and it's very challenging to obtain a large amount of attackers' data. These make applying machine learning in security-critical applications difficult. In this dissertation, we present several approaches to robustifying three machine learning based security applications. First, we start from adversarial examples in image recognition. We develop a method to generate robust adversarial examples that remain effective in the physical domain. Our core idea is to use an image-to-image translation network to simulate the digital-to-physical transformation process for generating robust adversarial examples. We further show these robust adversarial examples can improve the robustness of machine learning models by adversarial retraining. The second application is bot detection. We show that the performance of existing machine learning models is not effective if we only have the limit attackers' data. We develop a data synthesis method to address this problem. The key novelty is that our method is distribution aware synthesis, using two different generators in a Generative Adversarial Network to synthesize data for the clustered regions and the outlier regions in the feature space. We show the detection performance using 1% of attackers' data is close to existing methods trained with 100% of the attackers' data. The third component of this dissertation is phishing detection. By designing a novel measurement system, we search and detect phishing websites that adopt evasion techniques not only at the page content level but also at the web domain level. The key novelty is that our system is built on the observation of the evasive behaviors of phishing pages in practice. We also study how existing browsers defenses against phishing websites that impersonate trusted entities at the web domain. Our results show existing browsers are not yet effective to detect them.
Doctor of Philosophy
Machine learning (ML) is computer algorithms that aim to identify hidden patterns from the data. In recent years, machine learning has been widely used in many fields. The range of them is broad, from natural language to autonomous driving. However, there are growing concerns about the robustness of machine learning models. And these concerns are further amplified in security-critical applications — Attackers can manipulate their inputs (i.e., adversarial examples) to cause machine learning models to predict wrong, and it's highly expensive and difficult to obtain a huge amount of attackers' data because attackers are rare compared to the normal users. These make applying machine learning in security-critical applications concerning. In this dissertation, we seek to build better defenses in three types of machine learning based security applications. The first one is image recognition, by developing a method to generate realistic adversarial examples, the machine learning models are more robust for defending against adversarial examples by adversarial retraining. The second one is bot detection, we develop a data synthesis method to detect malicious bots when we only have the limit malicious bots data. For phishing websites, we implement a tool to detect domain name impersonation and detect phishing pages using dynamic and static analysis.

Taub, Lawrence. "Application of a Layered Hidden Markov Model in the Detection of Network Attacks." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/320.

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Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload's contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates.

Rosa, José Luís da Silva. "Customer-side detection of BGP routing attacks." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17808.

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Mestrado em Engenharia de Computadores e Telemática
A utilização diária da Internet tornou-se uma rotina que foi assimilada pelas pessoas sem considerarem a complexidade interna desta gigante rede. Até um certo ponto, o Border Gateway Protocol é o que mantem toda esta conectividade possível apesar de ser um protocolo defeituoso por natureza. Em 2008, um ataque Man-In-The-Middle foi pela primeira vez apresentado ao grande público e desde de então mais técnicas para explorar este protocolo e obter tráfego alheio de forma ilícita foram dadas a conhecer. Mesmo que o desvio não aconteça com natureza maliciosa, mas sim devido a um erro de configuração, este é um problema que deverá ser enfrentado. Alguns provedores de serviço e institutos de investigação já apresentaram propostas para novos protocolos e/ou sistemas de monitorização, mas estes estão atrasados no seu desenvolvimento ou apenas afetam a camada superior da rede, deixando utilizadores e um grande número de empresas que estão ligadas a um provedor sem meios para agir e sem informação sobre o encaminhamento do seu tráfego. Nesta dissertação, é apresentado, concebido e implementado um sistema que atinge uma monitorização ativa do BGP através da medição do tempo médio de viagem de vários pacotes enviados de várias localizações, através de uma rede mundial de sondas, e do processamento dos resultados obtidos, permitindo que todos os interessados possam ser alertados.
The daily use of the Internet has become a routine that many people absorbed into their lives without even thinking about the insides of this gigantic network. To an extent, the Border Gateway Protocol is what is keeping all this connectivity together despite being a very flawed protocol due to its design. In 2008 a Man-In-The-Middle attack was first presented to the general audience and ever since more techniques were reported to use the protocol to obtain traffic illicitly. Even if the routing deviation does not occur via a malicious intention but due to some poorly configured router, this is a problem that must be tackled. Some network providers and research institutes already presented some drafts for new protocols or monitoring systems but they are late into deployment or only affect the top layer of the network, leaving users and most part of the companies connected to the provider impotent and without any proper information about the routing of their traffic. In this dissertation a system is presented, implemented and deployed, achieving an active monitorization of BGP through measurements of the average travel time of several packets sent to various locations by a worldwide set of Probes and the collected results processed allowing all concerned actors to be alerted.

Lantz, David. "Detection of side-channel attacks targeting Intel SGX." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177987.

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In recent years, trusted execution environments like Intel SGX have allowed developers to protect sensitive code inside so called enclaves. These enclaves protect its code and data even in the cases of a compromised OS. However, SGX enclaves have been shown to be vulnerable to numerous side-channel attacks. Therefore, there is a need to investigate ways that such attacks against enclaves can be detected. This thesis investigates the viability of using performance counters to detect an SGX-targeting side-channel attack, specifically the recent Load Value Injection (LVI) class of attacks. A case study is thus presented where performance counters and a threshold-based detection method is used to detect variants of the LVI attack. The results show that certain attack variants could be reliably detected using this approach without false positives for a range of benign applications. The results also demonstrate reasonable levels of speed and overhead for the detection tool. Some of the practical limitations of using performance counters, particularly in an SGX-context, are also brought up and discussed.

Aditham, Santosh. "Mitigation of Insider Attacks for Data Security in Distributed Computing Environments." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6639.

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In big data systems, the infrastructure is such that large amounts of data are hosted away from the users. Information security is a major challenge in such systems. From the customer’s perspective, one of the big risks in adopting big data systems is in trusting the service provider who designs and owns the infrastructure, with data security and privacy. However, big data frameworks typically focus on performance and the opportunity for including enhanced security measures is limited. In this dissertation, the problem of mitigating insider attacks is extensively investigated and several static and dynamic run-time techniques are developed. The proposed techniques are targeted at big data systems but applicable to any data system in general. First, a framework is developed to host the proposed security techniques and integrate with the underlying distributed computing environment. We endorse the idea of deploying this framework on special purpose hardware and a basic model of the software architecture for such security coprocessors is presented. Then, a set of compile-time and run-time techniques are proposed to protect user data from the perpetrators. These techniques target detection of insider attacks that exploit data and infrastructure. The compile-time intrusion detection techniques analyze the control flow by disassembling program binaries while the run-time techniques analyze the memory access patterns of processes running on the system. The proposed techniques have been implemented as prototypes and extensively tested using big data applications. Experiments were conducted on big data frameworks such as Hadoop and Spark using cloud-based services. Experimental results indicate that the proposed techniques successfully detect insider attacks in the context of data loss, data degradation, data exposure and infrastructure degradation.

Rubio, Hernan Jose Manuel. "Detection of attacks against cyber-physical industrial systems." Thesis, Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0015/document.

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Nous abordons des problèmes de sécurité dans des systèmes cyber-physiques industriels. Les attaques contre ces systèmes doivent être traitées à la fois en matière de sûreté et de sécurité. Les technologies de contrôles imposés par les normes industrielles, couvrent déjà la sûreté. Cependant, du point de vue de la sécurité, la littérature a prouvé que l’utilisation de techniques cyber pour traiter la sécurité de ces systèmes n’est pas suffisante, car les actions physiques malveillantes seront ignorées. Pour cette raison, on a besoin de mécanismes pour protéger les deux couches à la fois. Certains auteurs ont traité des attaques de rejeu et d’intégrité en utilisant une attestation physique, p. ex., le tatouage des paramètres physiques du système. Néanmoins, ces détecteurs fonctionnent correctement uniquement si les adversaires n’ont pas assez de connaissances pour tromper les deux couches. Cette thèse porte sur les limites mentionnées ci-dessus. Nous commençons en testant l’efficacité d’un détecteur qui utilise une signature stationnaire afin de détecter des actions malveillantes. Nous montrons que ce détecteur est incapable d’identifier les adversaires cyber-physiques qui tentent de connaître la dynamique du système. Nous analysons son ratio de détection sous la présence de nouveaux adversaires capables de déduire la dynamique du système. Nous revisitons le design original, en utilisant une signature non stationnaire, afin de gérer les adversaires visant à échapper à la détection. Nous proposons également une nouvelle approche qui combine des stratégies de contrôle et de communication. Toutes les solutions son validées à l’aide de simulations et maquettes d’entraînement
We address security issues in cyber-physical industrial systems. Attacks against these systems shall be handled both in terms of safety and security. Control technologies imposed by industrial standards already cover the safety dimension. From a security standpoint, the literature has shown that using only cyber information to handle the security of cyber-physical systems is not enough, since physical malicious actions are ignored. For this reason, cyber-physical systems have to be protected from threats to their cyber and physical layers. Some authors handle the attacks by using physical attestations of the underlying processes, f.i., physical watermarking to ensure the truthfulness of the process. However, these detectors work properly only if the adversaries do not have enough knowledge to mislead crosslayer data. This thesis focuses on the aforementioned limitations. It starts by testing the effectiveness of a stationary watermark-based fault detector, to detect, as well, malicious actions produced by adversaries. We show that the stationary watermark-based detector is unable to identify cyber-physical adversaries. We show that the approach only detects adversaries that do not attempt to get any knowledge about the system dynamics. We analyze the detection performance of the original design under the presence of adversaries that infer the system dynamics to evade detection. We revisit the original design, using a non-stationary watermark-based design, to handle those adversaries. We also propose a novel approach that combines control and communication strategies. We validate our solutions using numeric simulations and training cyber-physical testbeds

Wang, Le. "Detection of Man-in-the-middle Attacks Using Physical Layer Wireless Security Techniques." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/992.

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"In a wireless network environment, all the users are able to access the wireless channel. Thus, if malicious users exploit this feature by mimicking the characteristics of a normal user or even the central wireless access point (AP), they can intercept almost all the information through the network. This scenario is referred as a Man-in-the-middle (MITM) attack. In the MITM attack, the attackers usually set up a rogue AP to spoof the clients. In this thesis, we focus on the detection of MITM attacks in Wi-Fi networks. The thesis introduces the entire process of performing and detecting the MITM attack in two separate sections. The first section starts from creating a rogue AP by imitating the characteristics of the legitimate AP. Then a multi-point jamming attack is conducted to kidnap the clients and force them to connect to the rogue AP. Furthermore, the sniffer software is used to intercept the private information passing through the rogue AP. The second section focuses on the detection of MITM attacks from two aspects: jamming attacks detection and rogue AP detection. In order to enable the network to perform defensive strategies more effectively, distinguishing different types of jamming attacks is necessary. We begin by using signal strength consistency mechanism in order to detect jamming attacks. Then, based on the statistical data of packets send ratio (PSR) and packets delivery ratio (PDR) in different jamming situations, a model is built to further differentiate the jamming attacks. At the same time, we gather the received signal strength indication (RSSI) values from three monitor nodes which process the random RSSI values employing a sliding window algorithm. According to the mean and standard deviation curve of RSSI, we can detect if a rogue AP is present within the vicinity. All these proposed approaches, either attack or detection, have been validated via computer simulations and experimental hardware implementations including Backtrack 5 Tools and MATLAB software suite. "

Rubio, Hernan Jose Manuel. "Detection of attacks against cyber-physical industrial systems." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0015.

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Книги з теми "Security attacks detection":

Dübendorfer, Thomas P. Impact analysis, early detection, and mitigation of large-scale Internet attacks. Aachen: Shaker, 2005.

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Raghavan, S. V. An Investigation into the Detection and Mitigation of Denial of Service (DoS) Attacks: Critical Information Infrastructure Protection. India: Springer India Pvt. Ltd., 2011.

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Nelson A. Rockefeller Institute of Government., ed. The role of "home" in homeland security: The prevention and detection of terrorist attacks : the challenge for state and local government. Albany, N.Y: The Institute, 2003.

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Brancik, Kenneth C. Insider computer fraud: An indepth framework for detecting and defending against insider it attacks. Boca Raton: Auerbach Publications, 2007.

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United States. Congress. House. Committee on Science and Technology (2007). Subcommittee on Technology and Innovation. Planning for the future of cyber attack attribution: Hearing before the Subcommittee on Technology and Innovation, Committee on Science and Technology, House of Representatives, One Hundred Eleventh Congress, second session, July 15, 2010. Washington: U.S. G.P.O., 2010.

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United States. Congress. House. Committee on Homeland Security. Subcommittee on the Prevention of Nuclear and Biological Attack. DHS coordination of nuclear detection efforts.: Hearing before the Subcommittee on Prevention of Nuclear and Biological Attack of the Committee on Homeland Security, House of Representatives, One Hundred Ninth Congress, first session, April 19, 2005 and April 20, 2005. Washington: U.S. G.P.O., 2005.

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Attack, United States Congress House Committee on Homeland Security Subcommittee on the Prevention of Nuclear and Biological. Enlisting foreign cooperation in U.S. efforts to prevent nuclear smuggling: Hearing before the Subcommittee on [the] Prevention of Nuclear and Biological Attack of the Committee on Homeland Security, House of Representatives, One Hundred Ninth Congress, second session, May 25, 2006. Washington: U.S. G.P.O., 2007.

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United States. Congress. House. Committee on Homeland Security. Subcommittee on the Prevention of Nuclear and Biological Attack. The science of prevention: Hearing before the Subcommittee on Prevention of Nuclear and Biological Attack of the Committee on Homeland Security, House of Representatives, One Hundred Ninth Congress, second session, September 14, 2006. Washington: U.S. G.P.O., 2007.

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Attack, United States Congress House Committee on Homeland Security Subcommittee on the Prevention of Nuclear and Biological. Detecting nuclear weapons and radiological materials: How effective is available technology? : joint hearing before the Subcommittee on Prevention of Nuclear and Biological Attack with the Subcommittee on Emergency Preparedness, and Science, and Technology of the Committee on Homeland Security, House of Representatives, One Hundred Ninth Congress, first session, June 21, 2005. Washington: U.S. G.P.O., 2007.

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Bhattacharyya, Dhruba Kumar, and Jugal Kumar Kalita. DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance. Taylor & Francis Group, 2016.

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Частини книг з теми "Security attacks detection":

van Oorschot, Paul C. "Intrusion Detection and Network-Based Attacks." In Information Security and Cryptography, 309–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33649-3_11.

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van Oorschot, Paul C. "Intrusion Detection and Network-Based Attacks." In Information Security and Cryptography, 309–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83411-1_11.

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Lucas, Keane, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, and Saurabh Shintre. "Deceiving ML-Based Friend-or-Foe Identification for Executables." In Advances in Information Security, 217–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16613-6_10.

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AbstractDeceiving an adversary who may, e.g., attempt to reconnoiter a system before launching an attack, typically involves changing the system’s behavior such that it deceives the attacker while still permitting the system to perform its intended function. We develop techniques to achieve such deception by studying a proxy problem: malware detection.Researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we propose an attack that interweaves binary-diversification techniques and optimization frameworks to mislead such DNNs while preserving the functionality of binaries. Unlike prior attacks, ours manipulates instructions that are a functional part of the binary, which makes it particularly challenging to defend against. We evaluated our attack against three DNNs in white- and black-box settings and found that it often achieved success rates near 100%. Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%. We explored several defenses, both new and old, and identified some that can foil over 80% of our evasion attempts. However, these defenses may still be susceptible to evasion by attacks, and so we advocate for augmenting malware-detection systems with methods that do not rely on machine learning.

Singh, Gulshan Kumar, and Gaurav Somani. "Detecting Cloud Originated DDoS Attacks at the Source Using Out-Cloud Attack Detection (OCAD)." In Information Systems Security, 169–85. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23690-7_10.

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Narisada, Shintaro, Shoichiro Sasaki, Seira Hidano, Toshihiro Uchibayashi, Takuo Suganuma, Masahiro Hiji, and Shinsaku Kiyomoto. "Stronger Targeted Poisoning Attacks Against Malware Detection." In Cryptology and Network Security, 65–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65411-5_4.

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Arya, Shivani, and Saurabh Chamotra. "Multi Layer Detection Framework for Spear-Phishing Attacks." In Information Systems Security, 38–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92571-0_3.

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Harish, R., and K. Praveen. "Review on Wi-Fi Attacks and Detection Methods." In Information Technology Security, 101–17. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0407-1_5.

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Shirazi, Hossein, Bruhadeshwar Bezawada, Indrakshi Ray, and Charles Anderson. "Adversarial Sampling Attacks Against Phishing Detection." In Data and Applications Security and Privacy XXXIII, 83–101. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22479-0_5.

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Szynkiewicz, Paweł. "Signature-Based Detection of Botnet DDoS Attacks." In Cybersecurity of Digital Service Chains, 120–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_6.

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AbstractThe distributed denial of service (DDoS) attack is an attempt to disrupt the proper availability of a targeted server, service or network. The attack is achieved by corrupting or overwhelming the target’s communications with a flood of malicious network traffic. In the current era of mass connectivity DDoS attacks emerge as one of the biggest threats, staidly causing greater collateral damage and heaving a negate impacting on the integral Internet Infrastructure. DDoS attacks come in a variety of types and schemes, they continue to evolve, steadily becoming more sophisticated and larger at scale. A close investigation of attack vectors and refining current security measures is required to efficiently mitigate new DDoS threats. The solution described in this article concerns a less explored variation of signature-based techniques for DDoS mitigation. The approach exploits one of the traits of modern DDoS attacks, the utilization of Packet generation algorithms (PGA) in the attack execution. Proposed method performs a fast, protocol-level detection of DDoS network packets and can easily be employed to provide an effective, supplementary protection against DDoS attacks.

Salazar-Hernández, Rolando, and Jesús E. Díaz-Verdejo. "Hybrid Detection of Application Layer Attacks Using Markov Models for Normality and Attacks." In Information and Communications Security, 416–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17650-0_29.

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Тези доповідей конференцій з теми "Security attacks detection":

Mihai, Ioan cosmin, and Laurentiu Giurea. "MANAGEMENT OF ELEARNING PLATFORMS SECURITY." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-061.

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The existence of many cyber-attacks targeted to online environment, make eLearning platforms security a major concern. To secure an eLearning platform there are three interconnected strategies: prevention (the actions taken before an attack), detection (the action taken during an attack) and response (the action taken after an attack). This paper focuses on detection, providing different strategies to detect if eLearning platform security was compromised: intrusion detection, malware detection and suspicious activities detection. An attack tree is developed to simulate and to observe the impact of cyber-attacks on eLearning platforms. The attack tree lists and develops methods by which an attacker can cause a security incident on platforms. The attack tree is useful to explore certain attack paths in depth and to generate intrusion scenarios on a website. To conduct a cyber-attack to an eLearning platform, each edge to the internal node structure of the attack tree must be traversed. The internal nodes of the attack tree represent the seven stages of the intrusion model Kill Chain, which was defined by researchers from Lockheed Martin. This model consists of seven stages: reconnaissance, weaponization, delivery, exploitation, installation, command and control and action on objectives. The external edges of the tree that connect the leaf nodes, represent optional attack vectors. The results from the simulation attacks are used to presents the management of eLearning platforms security against cyber-attacks. An eLearning platform security is affected when the integrity or availability of the platform's files are compromised or additional malicious activity has been detected; for example malware infections, redirections to malicious websites or other suspicious activities like phishing or spamming. While there are no solutions to guarantee the security of eLearning platforms, this paper describes the attack vectors and presents various solutions to detect indicators of compromise.

Kolodziej, Joanna, Mateusz Krzyszton, and Pawel Szynkiewicz. "Anomaly Detection In TCP/IP Networks." In 37th ECMS International Conference on Modelling and Simulation. ECMS, 2023. http://dx.doi.org/10.7148/2023-0542.

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Intrusion Detection Systems (IDS) should be capable of quickly detecting attacks and network traffic anomalies to reduce the damage to the network components. They may efficiently detect threats based on prior knowledge of attack characteristics and the potential threat impact ('known attacks'). However, IDS cannot recognize threats, and attacks ('unknown attacks') usually occur when using brand-new technologies for system damage. This paper presents two security services -- Net Anomaly Detector (NAD) and a signature-based PGA Filter for detecting attacks and anomalies in TCP/IP networks. Both services are modules of the cloud-based GUARD platform developed in the H2020 GUARD project. Such a platform was the main component of the simulation environment in the work presented in this paper. The provided experiments show that both modules achieved satisfactory results in detecting an unknown type of DoS attacks and signatures of DDoS attacks.

Segura, Gustavo A. Nunez, Arsenia Chorti, and Cíntia Borges Margi. "IDIT-SDN: Intrusion Detection Framework for Software-defined Wireless Sensor Networks." In Anais Estendidos do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbrc_estendido.2023.817.

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Software-Defined Networking has been used to leverage security solutions for wireless sensor networks. However, this paradigm turns networks vulnerable to distributed denial of service attacks. IDIT-SDN is a tool for Software-defined Wireless Sensor Networks devised for DoS and DDoS attacks simulation and detection. This tool provides a framework for anomaly detection and a communication protocol to share security wise information from the sensor network to the controller. We demonstrate its use by showing a cooperative DDoS attack detection and attacker identification application based on distributed (every node) and centralized (controller) anomaly detection.

Ramesh Kumar, M., and Pradeep Sudhakaran. "Comprehensive Survey on Detecting Security Attacks of IoT Intrusion Detection Systems." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-270t9z.

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With the growth of Internet of Things (IoT), which connects billions of small, smart devices to the Internet, cyber security has become more difficult to manage. These devices are vulnerable to cyberattacks because they lack defensive measures and hardware security support. In addition, IoT gateways provide the most fundamental security mechanisms like firewall, antivirus and access control mechanism for identifying such attacks. In IoT setting, it is critical to maintain security, and protecting the network is even more critical in an IoT network. Because it works directly at local gateways, the Network Intrusion Detection System (NIDS) is one of the most significant solutions for securing IoT devices in a network. This research includes various IoT threats as well as different intrusion detection systems (IDS) methodologies for providing security in an IoT environment, with the goal of evaluating the pros and drawbacks of each methodology in order to discover future IDS implementation paths.

Lim, Wei Heng, Weng Foong Liew, Chun Yew Lum, and Seah Fang Lee. "Phishing Security: Attack, Detection, and Prevention Mechanisms." In International Conference on Digital Transformation and Applications (ICDXA 2020). Tunku Abdul Rahman University College, 2020. http://dx.doi.org/10.56453/icdxa.2020.1017.

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While the Internet technologies have been developed over the decades, a significant issue has been coming along with it, the cybercrime. Cybercrime consists of various types of cyberattacks which could bring mild to serious adverse effects to individuals or organizations’ operations. Among those cybercrime attacks, phishing is one of the common mechanisms used. The phishing attack could target on any of the electronic communication users. The paper provides an overview insight on the phishing security concepts, ranging from various types of phishing attack techniques, phishing detection mechanism to prevention approaches. Comparison were included for each of the phishing aspects. Keywords: Phishing attack, phishing detection, phishing prevention, phishing security

Babbage, S. H. "Improved “exhaustive search” attacks on stream ciphers." In European Convention on Security and Detection. IEE, 1995. http://dx.doi.org/10.1049/cp:19950490.

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Gregorio- de Souza, Ian, Vincent H. Berk, Annarita Giani, George Bakos, Marion Bates, George Cybenko, and Doug Madory. "Detection of complex cyber attacks." In Defense and Security Symposium, edited by Edward M. Carapezza. SPIE, 2006. http://dx.doi.org/10.1117/12.670131.

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S. Jabor, Maytham, Aqeel Salman Azez, Azhar Hasan Nsaif, Azhar Sabah Abdulaziz, and Worud Mahdi Saleh. "Security Challenges and Threats in Wireless Sensor Networks: A Review." In IX. International Scientific Congress of Pure, Applied and Technological Sciences. Rimar Academy, 2023. http://dx.doi.org/10.47832/minarcongress9-21.

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Wireless Sensor Networks (WSNs) have gained increasing popularity in recent years due to their diverse range of applications. However, owing to their distinctive characteristics—such as limited computational power, energy resources, and a dynamic nature—these networks present unique challenges. Comprising numerous small, lowpower sensor nodes, WSNs are deployed in specific areas to gather and transmit data to a base station or sink node. Nevertheless, they remain susceptible to various security threats. A significant concern in WSNs involves attacks compromising data confidentiality, integrity, and availability. Adversaries can intercept and modify transmitted data before forwarding it, thereby undermining its integrity. Additionally, attackers can compromise sensor nodes themselves, thereby gaining unauthorized network access, data manipulation abilities, and the potential to launch subsequent attacks. This paper comprehensively addresses security challenges in WSNs, encompassing attacks on data confidentiality, integrity, availability, and network vulnerabilities. Furthermore, it delves into various attack types against WSNs, such as node compromise, denial-of-service attacks, and network topology breaches. The document provides a thorough review of existing security solutions and protocols proposed to counter these attacks, including encryption, access control, and intrusion detection systems. Lastly, the paper identifies ongoing research challenges and outlines future strategies for enhancing WSN security

Baykara, Muhammet, and Zahit Ziya Gurel. "Detection of phishing attacks." In 2018 6th International Symposium on Digital Forensic and Security (ISDFS). IEEE, 2018. http://dx.doi.org/10.1109/isdfs.2018.8355389.

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Kim, Hannah, Celia Cintas, Girmaw Abebe Tadesse, and Skyler Speakman. "Spatially Constrained Adversarial Attack Detection and Localization in the Representation Space of Optical Flow Networks." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/107.

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Optical flow estimation have shown significant improvements with advances in deep neural networks. However, these flow networks have recently been shown to be vulnerable to patch-based adversarial attacks, which poses security risks in real-world applications, such as self-driving cars and robotics. We propose SADL, a Spatially constrained adversarial Attack Detection and Localization framework, to detect and localize these patch-based attack without requiring a dedicated training. The detection of an attacked input sequence is performed via iterative optimization on the features from the inner layers of flow networks, without any prior knowledge of the attacks. The novel spatially constrained optimization ensures that the detected anomalous subset of features comes from a local region. To this end, SADL provides a subset of nodes within a spatial neighborhood that contribute more to the detection, which will be utilized to localize the attack in the input sequence. The proposed SADL is validated across multiple datasets and flow networks. With patch attacks 4.8% of the size of the input image resolution on RAFT, our method successfully detects and localizes them with an average precision of 0.946 and 0.951 for KITTI-2015 and MPI-Sintel datasets, respectively. The results show that SADL consistently achieves higher detection rates than existing methods and provides new localization capabilities.

Звіти організацій з теми "Security attacks detection":

Fedchenko, Vitaly. Nuclear Security During Armed Conflict: Lessons From Ukraine. Stockholm International Peace Research Institute, March 2023. http://dx.doi.org/10.55163/zzsp5617.

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The attacks on nuclear installations in Ukraine by the Russian military in 2022 were unprecedented. Nuclear security aims at prevention, detection and response to malicious or unauthorized acts by non-state actors, not the armed forces of a state. However, an international armed conflict creates new circumstances in which a national nuclear security regime must operate. In March 2022 the director general of the International Atomic Energy Agency (IAEA) highlighted ‘seven indispensable pillars of nuclear safety and security’ in extraordinary circumstances. There are three further areas in which the international nuclear security framework can be strengthened and prepared for extraordinary events, including armed conflict. First, there is a need to further clarify and plan the actions of competent authorities. Second, the IAEA may be able to assist member states in developing guidance for specific scenarios during extraordinary events. Third, there should be further integration of nuclear security with nuclear safety and emergency preparedness and response.

Kolencik, Marian. A critical evaluation of the risk indicators of criminal conduct involving CBRN and explosive materials - Behavioural and observational analysis in crime detection and investigation. ISEM Institute, n.p.o., October 2023. http://dx.doi.org/10.52824/vzrb5079.

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Terrorist attacks using explosives and CBRN (Chemical, Biological, Radiological and Nuclear) materials have been present throughout history. While the frequency of CBRN terrorist attacks is relatively low compared to those with explosives and other types of weapons, it is crucial to treat the efforts of both terrorist organizations and individuals with appropriate gravity in order to avert catastrophic consequences. Identifying warning signs that indicate criminal behaviour is crucial for preventing planned crimes or terrorist attacks, and there is a need for more precise coverage of potential risk indicators related to CBRN and explosive crimes. This research aimed at examining and scrutinizing possible warning signs associated with planning and conducting terrorist attacks using CBRN and explosive materials. The research was implemented in three phases. First, comprise the systematic literature review. In the second phase, the case studies and CCTV records from past cases from Europe, USA, Australia and Asia were analysed and the aim was to create a list of risk indicators and categories for future reference by developing a methodological tool. The last phase represented a survey in which the practitioners from European Law enforcement and Intelligence Agencies critically assessed the list of risk indicators and their categories created based on the previous two steps of the research. The last goal was to gain the agreement and endorsement of law enforcement officials from different European nations regarding the validity and importance of recognized risk indicators and their categories, as well as their ranking for use in operational tasks, investigations, and training. The majority of the respondents found the identified categories and risk indicators as reliable and relevant for their operational activities and investigations. For the second research question, the survey results prioritized categories of risk indicators that are most suitable for the detection tactics of investigators and intelligence officers. The third research question examined the ease of observing identified risk indicators, with the category of technological detection/air sampling alarm risk indicators ranking as the easiest to detect. Finally, the survey found that the identified risk indicators are useful for training activities of security entities. Several final comments and recommendations from participants were also discussed, emphasizing the importance of considering multiple factors when identifying risk indicators and the value of the comprehensive list of identified risk indicators. The publication also examines some terrorist theories, the advantages, limitations, and the ongoing debate surrounding the use of profiling in protective security.

Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.

Peterson, Dale. Cyber Security Audit and Attack Detection Toolkit. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1097617.

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In Hot Water? The Growing Threat of Cyber Attacks to Water Distribution Systems. American Society of Civil Engineers, March 2022. http://dx.doi.org/10.1061/infographic.000003.

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rsecurity into the current practices of the public water infrastructure sector? ASCE collection on cybersecurity in water distribution networks Overview of smart water networks, their advantages and weaknesses, and growing challenges in securing resilience Lessons learned from past cybersecurity incidents AI-based algorithms for detecting and localizing cyber attacks Integrating cyber attacks into resilience and risk assessment procedures and emergency response measures Analyzing different types of cyber-physical attacks and their effects Modeling and simulation methodologies for managing water distribution security Understanding cybersecurity from the perspective of different stakeholders Cyber attacks will become a more serious and recurring threat the more we transition into smart water distribution systems—we must remain vigilant! This collection will help engineers and decision makers become familiar with the state-of-the-art in cybersecurity for water infrastructure networks, leading to: •Resilient and reliable drinking water infrastructure •Better guidelines and protocols C − In ASCE’s 2021 Report Card for America’s Infrastructure, the Drinking Water category got a ‘C −’ The Infrastructure Investment and Jobs Act supports cybersecurity for the public water system •Clean Water Resiliency and Sustainability Program: Grants to increase resiliency of public treatment systems and distribution networks to cyber attacks and natural hazards •$25 million annually for five years T