Gotowe bibliografie tematyczne / Attack Detection Automation

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Gotowa bibliografia na temat „Attack Detection Automation”

Autor: Grafiati

Data publikacji: 16 listopada 2024

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Attack Detection Automation"

Wressnegger, Christian. "Efficient machine learning for attack detection." it - Information Technology 62, no. 5-6 (2020): 279–86. http://dx.doi.org/10.1515/itit-2020-0015.

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AbstractDetecting and fending off attacks on computer systems is an enduring problem in computer security. In light of a plethora of different threats and the growing automation used by attackers, we are in urgent need of more advanced methods for attack detection. Manually crafting detection rules is by no means feasible at scale, and automatically generated signatures often lack context, such that they fall short in detecting slight variations of known threats.In the thesis “Efficient Machine Learning for Attack Detection” [35], we address the necessity of advanced attack detection. For the

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Beshah, Yonas Kibret, Surafel Lemma Abebe, and Henock Mulugeta Melaku. "Drift Adaptive Online DDoS Attack Detection Framework for IoT System." Electronics 13, no. 6 (2024): 1004. http://dx.doi.org/10.3390/electronics13061004.

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Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic enviro

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Okello, Fredrick Ochieng, Dennis Kaburu, and Ndia G. John. "Automation-Based User Input Sql Injection Detection and Prevention Framework." Computer and Information Science 16, no. 2 (2023): 51. http://dx.doi.org/10.5539/cis.v16n2p51.

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Autodect framework protects management information&nbsp;systems&nbsp;(MIS)&nbsp;and&nbsp;databases&nbsp;from&nbsp;user&nbsp;input SQL injection attacks. This framework&nbsp;overcomes intrusion or penetration into the&nbsp;system&nbsp;by&nbsp;automatically&nbsp;detecting&nbsp;and&nbsp;preventing attacks from the user input end.&nbsp;The&nbsp;attack&nbsp;intentions&nbsp;is&nbsp;also known&nbsp;since&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;

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Vennapureddy, Rajasree, and T. Srinivasulu. "Pragmatic Study of Botnet Attack Detection In An IoT Environment." E3S Web of Conferences 591 (2024): 09012. http://dx.doi.org/10.1051/e3sconf/202459109012.

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A comprehensive search for primary research published between 2014 and 2023 was carried across several databases. Studies that describe the application of machine learning (ML) and deep learning techniques for if they was carried out across several databases. Studies that described the application of deep learning (DL) and machine learning (ML) methods for IoT botnet attack detection. Numerous facets of contemporary life have been transformed by the Internet of Things (IoT), including home automation, industrial control systems, healthcare, and transportation. On the other hand, as more device

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Housh, Mashor, Noy Kadosh, and Jack Haddad. "Detecting and Localizing Cyber-Physical Attacks in Water Distribution Systems without Records of Labeled Attacks." Sensors 22, no. 16 (2022): 6035. http://dx.doi.org/10.3390/s22166035.

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Modern water distribution systems (WDSs) offer automated controls and operations to improve their efficiency and reliability. Nonetheless, such automation can be vulnerable to cyber-attacks. Therefore, various approaches have been suggested to detect cyber-attacks in WDSs. However, most of these approaches rely on labeled attack records which are rarely available in real-world applications. Thus, for a detection model to be practical, it should be able to detect and localize events without referring to a predetermined list of labeled attacks. This study proposes a semi-supervised approach that

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Ye, Shengke, Kaiye Dai, Guoli Fan, Ling Zhang, and Zhihao Liang. "Exploring the intersection of network security and database communication: a PostgreSQL Socket Connection case study." Transactions on Computer Science and Intelligent Systems Research 3 (April 10, 2024): 1–9. http://dx.doi.org/10.62051/pzqebt34.

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In this study, the network security of PostgreSQL database using Socket connection is deeply analyzed. By exploring Socket connections established by PostgreSQL over TCP, we find potential security threats and vulnerabilities during data transmission, which may expose database systems to network attacks such as unauthorized access and data leakage. In order to assess these security risks, this study simulated a variety of network attack scenarios, especially the implantation and detection of Webshell, to reveal the vulnerability of PostgreSQL to such network threats. Especially in defending ag

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Karthik Krishnan, T., S. Sridevi, G. Bindu, and R. Anandan. "Comparison and detail study of attacks and detection methods for wireless sensor network." International Journal of Engineering & Technology 7, no. 2.21 (2018): 405. http://dx.doi.org/10.14419/ijet.v7i2.21.12453.

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Wireless sensor network is emanating technology in the field of telecommunications. WSNs can be applied in many fields like machine surveillance, precision agriculture, home automation and intelligent building environments. However the major aspect of WSN is the security as the sensor nodes are limited because of these facing several security threats such as black hole attack, worm hole attack, flooding etc. which is finally affecting the functioning of the whole network. These attacks are maximizing the consumption of power in the node and also it decreases life of the battery. In this paper,

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Binbusayyis, Adel. "Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers." Mathematics 12, no. 11 (2024): 1720. http://dx.doi.org/10.3390/math12111720.

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In the modern world, the evolution of the internet supports the automation of several tasks, such as communication, education, sports, etc. Conversely, it is prone to several types of attacks that disturb data transfer in the network. Efficient attack detection is needed to avoid the consequences of an attack. Traditionally, manual attack detection is limited by human error, less efficiency, and a time-consuming mechanism. To address the problem, a large number of existing methods focus on several techniques for better efficacy in attack detection. However, improvement is needed in significant

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Sztyber-Betley, Anna, Michał Syfert, Jan Maciej Kościelny, and Zuzanna Górecka. "Controller Cyber-Attack Detection and Isolation." Sensors 23, no. 5 (2023): 2778. http://dx.doi.org/10.3390/s23052778.

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This article deals with the cyber security of industrial control systems. Methods for detecting and isolating process faults and cyber-attacks, consisting of elementary actions named “cybernetic faults” that penetrate the control system and destructively affect its operation, are analysed. FDI fault detection and isolation methods and the assessment of control loop performance methods developed in the automation community are used to diagnose these anomalies. An integration of both approaches is proposed, which consists of checking the correct functioning of the control algorithm based on its

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Almedires, Motaz Abdulaziz, Ahmed Elkhalil, and Mohammed Amin. "Adversarial Attack Detection in Industrial Control Systems Using LSTM-Based Intrusion Detection and Black-Box Defense Strategies." Journal of Cyber Security and Risk Auditing 2025, no. 3 (2025): 4–22. https://doi.org/10.63180/jcsra.thestap.2025.3.2.

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In industrial control systems (ICS), neural networks are increasingly being utilized to detect intrusions. The term ICS refers to a group of controlling technology and associated equipment that includes the devices, systems, networks, and controllers that are used to manage and/or execute manufacturing processes. Each ICS is developed to successfully handle work digitally and operates differently depending on the business. ICS devices and procedures are now found in practically every industry sector and key infrastructure, including production, transportation, power, and treatment plants. To a

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Rozprawy doktorskie na temat "Attack Detection Automation"

Toure, Almamy. "Collection, analysis and harnessing of communication flows for cyber-attack detection." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. http://www.theses.fr/2024UPHF0023.

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La complexité croissante des cyberattaques, caractérisée par une diversification des techniques d'attaque, une expansion des surfaces d'attaque et une interconnexion croissante d'applications avec Internet, rend impérative la gestion du trafic réseau en milieu professionnel. Les entreprises de tous types collectent et analysent les flux réseau et les journaux de logs pour assurer la sécurité des données échangées et prévenir la compromission des systèmes d'information. Cependant, les techniques de collecte et de traitement des données du trafic réseau varient d'un jeu de données à l'autre, et

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Štangler, Jan. "Architektura a správa zabezpečených sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413065.

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This work is focused on the security of small to medium-sized networks with central administration, especially on the creation of a methodology for secure network design.The design of a secure network for a start-up IT company, using open-source software, is described. Deployment of the designed secure network, with central management, is performed and the connectivity of network elements are tested. The model simulates network traffic situations and network attacks using penetration testing techniques. In terms of the severity of the impact on network security, intercepted attacks are evaluat

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Nama, Sumanth. "Detecting attacks in building automation system." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1597784.

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Building Automation System (BAS) was proposed to have the automatic centralized control of various appliances in the building such as heating, ventilating, air conditioning and other systems. Providing high security for the network layer in BAS was the major concern in recent times of studies. Researchers have been proposing different authentication protocols to stop the intruders from attacking the network, of which Time Efficient Stream Loss Authentication (TESLA) was the most secured protocol. Apart from its low computational and communicational overhead, there are few possible ways from

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Yadav, Tarun Kumar. "Automatic Detection and Prevention of Fake Key Attacks in Signal." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/9072.

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The Signal protocol provides end-to-end encryption for billions of users in popular instant messaging applications like WhatsApp, Facebook Messenger, and Google Allo. The protocol relies on an app-specific central server to distribute public keys and relay encrypted messages between the users. Signal prevents passive attacks. However, it is vulnerable to some active attacks due to its reliance on a trusted key server. A malicious key server can distribute fake keys to users to perform man-in-the-middle or impersonation attacks. Signal applications support an authentication ceremony to detect t

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Giunta, Alberto. "Implementazione e analisi comparativa di tecniche di Face Morphing Detection." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17029/.

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Il Face Morphing Attack consiste nel presentare ai portali di Automatic Border Control dei passaporti sostanzialmente validi e regolarmente emessi, ma con fotografie e quindi dati biometrici del volto che ne permettano l’uso da parte di più di un solo soggetto, e quindi da parte di soggetti diversi rispetto al legittimo proprietario del documento. Il campo di ricerca in ambito di Face Morphing Detection è ancora molto giovane e attivo, nonché frammentato: ciascuno studio sull’argomento propone tecniche in qualche modo differenti dalle precedenti e ne verifica l’efficacia su dataset proprie

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Gill, Rupinder S. "Intrusion detection techniques in wireless local area networks." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/29351/1/Rupinder_Gill_Thesis.pdf.

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This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection tech

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Gill, Rupinder S. "Intrusion detection techniques in wireless local area networks." Queensland University of Technology, 2009. http://eprints.qut.edu.au/29351/.

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Bláha, Lukáš. "Analýza automatizovaného generování signatur s využitím Honeypotu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236430.

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In this paper, system of automatic processing of attacks using honeypots is discussed. The first goal of the thesis is to become familiar with the issue of signatures to detect malware on the network, especially the analysis and description of existing methods for automatic generation of signatures using honeypots. The main goal is to use the acquired knowledge to the design and implementation of tool which will perform the detection of new malicious software on the network or end user's workstation.

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Lin, Yu-Ren, and 林育任. "Automatic Construction of Primitive Attack Templates for Primitive Attack-based Heterogeneous Intrusion Detection." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/50928028528463091853.

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碩士 國立臺灣科技大學 資訊工程系 93 The security of networked computers strongly affects network applications. Although we already have firewalls and encryption systems, intrusion still happens often. IDSs (Intrusion Detection Systems) with different techniques and characteristics have thus been developed to serve as the second layer protection. Problems associated with IDS include: (1) IDSs often produce lots of low level alerts which aren’t integrated. (2) IDSs produce lots of false alerts. (3) Heterogeneous IDSs have their specific capabilities of detecting attacks; however, their detection sc

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Liang, Ti-Hung, and 梁滌宏. "A Study on Network ARP Attack Detection, Prevention and Automatic Connection Restoration." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/21127536084053917591.

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碩士 國立臺灣海洋大學 電機工程學系 103 This research focuses on the analysis of network attacking via the NetCut software. This kind of software will send out huge ARP( Address Resolution Protocol ) packets to the network switch, whether the attacking is occurred or not. Using this characteristic, debugging mode through the layer3 of network switch will be used to collect the ARP information. The programming language Perl will be used to analyze the amount of ARP packets periodically. When the amount of ARP packets exceed the specific guarding value, the MAC of the host running NetCut will be bloc

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Części książek na temat "Attack Detection Automation"

Alsabbagh, Wael, and Peter Langendoerfer. "A Remote Attack Tool Against Siemens S7-300 Controllers: A Practical Report." In Technologien für die intelligente Automation. Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64283-2_1.

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AbstractThis paper presents a series of attacks against Siemens S7-300 programmable logic controllers (PLCs), using our remote IHP-Attack tool. Due to the lack of integrity checks in S7-300 PLCs, such controllers execute commands whether or not they are delivered from a legitimate user. Thus, they were exposed to various kind of cyber-attacks over the last years such as reply, bypass authentication and access control attacks. In this work, we build up our tool to carry out a series of attacks based on the existing reported vulnerabilities of S7-300 PLCs in the research community. For real worl

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Zhang, Yanjing, Jianming Cui, and Ming Liu. "Research on Adversarial Patch Attack Defense Method for Traffic Sign Detection." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8285-9_15.

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AbstractAccurate and stable traffic sign detection is a key technology to achieve L3 driving automation, and its performance has been significantly improved by the development of deep learning technology in recent years. However, the current traffic sign detection has inadequate difficulty resisting anti-attack ability and even does not have basic defense capability. To solve this critical issue, an adversarial patch attack defense model IYOLO-TS is proposed in this paper. The main innovation is to simulate the conditions of traffic signs being partially damaged, obscured or maliciously modifi

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Doniyorbek, Usmanbayev, and Bozorov Suhrobjon. "Analysis of Algorithm of Binary Classifiers to Improve Attack Detection Systems." In 12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022). Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51521-7_12.

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Fritsch, Lothar, Aws Jaber, and Anis Yazidi. "An Overview of Artificial Intelligence Used in Malware." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_4.

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AbstractArtificial intelligence (AI) and machine learning (ML) methods are increasingly adopted in cyberattacks. AI supports the establishment of covert channels, as well as the obfuscation of malware. Additionally, AI results in new forms of phishing attacks and enables hard-to-detect cyber-physical sabotage. Malware creators increasingly deploy AI and ML methods to improve their attack’s capabilities. Defenders must therefore expect unconventional malware with new, sophisticated and changing features and functions. AI’s potential for automation of complex tasks serves as a challenge in the f

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Wurzenberger, Markus, Max Landauer, Agron Bajraktari, and Florian Skopik. "Automatic Attack Pattern Mining for Generating Actionable CTI Applying Alert Aggregation." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_7.

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AbstractIntrusion Detection Systems (IDSs) monitor all kinds of IT infrastructures to automatically detect malicious activities related to cyber attacks. Unfortunately, especially anomaly-based IDS are known to produce large numbers of alerts, including false positives, that often become overwhelming for manual analysis. However, due to a fast changing threat landscape, quickly evolving attack techniques, and ever growing number of vulnerabilities, novel anomaly detection systems that enable detection of unknown attacks are indispensable. Therefore, to reduce the number of alerts that have to

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Hoyos, Isaias, Bruno Esposito, and Miguel Nunez-del-Prado. "DETECTOR: Automatic Detection System for Terrorist Attack Trajectories." In Information Management and Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11680-4_17.

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Lebrun, Stéphanie, Stéphane Kaloustian, Raphaël Rollier, and Colin Barschel. "GNSS Positioning Security: Automatic Anomaly Detection on Reference Stations." In Critical Information Infrastructures Security. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93200-8_4.

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AbstractThe dependency of critical infrastructures on Global Navigation Satellite Systems (GNSS) keeps increasing over the years. This over-reliance brings concerns as those systems are vulnerable and consequently prone to human-made perturbations, such as jamming and spoofing attacks. Solutions for detecting such disturbances are therefore crucially needed to raise GNSS users’ awareness and protection. This paper suggests an approach for detecting anomalous events (i.e., potentially an attack attempt) based on measurements recorded by Continuously Operating GNSS Reference Stations (CORS). Pre

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Yang, Xu, Qian Li, Cong Li, and Yong Qi. "Robust Malware Detection System Against Adversarial Attacks." In Advances in Intelligent Automation and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_122.

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Perkins, Jeff, Jordan Eikenberry, Alessandro Coglio, Daniel Willenson, Stelios Sidiroglou-Douskos, and Martin Rinard. "AutoRand: Automatic Keyword Randomization to Prevent Injection Attacks." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40667-1_3.

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Jin, Shuyuan, Zhi Yang, and Xiang Cui. "Automatic Covert Channel Detection in Asbestos System (Poster Abstract)." In Research in Attacks, Intrusions, and Defenses. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33338-5_22.

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Streszczenia konferencji na temat "Attack Detection Automation"

Ghamarilangroudi, Azam, Shahin Hashtrudi Zad, and Youmin Zhang. "Replay Attack Detection using Switching Multi-sine Watermarking." In 2025 33rd Mediterranean Conference on Control and Automation (MED). IEEE, 2025. https://doi.org/10.1109/med64031.2025.11073356.

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Li, Mingyang, Wei Zhang, Yadong Wang, et al. "CFA: Consistent Feature Attribution for Black-Box Adversarial Attack Against Object Detection." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864831.

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Zhang, Ruo, Guiqin Yang, and Wei Zhang. "DDoS Attack Detection System Based on GBDT Under SDN." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733143.

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Zhang, Wei, Guiqin Yang, and Ruo Zhang. "DDoS Attack Detection Based on Rényi-RF in SDN Environment." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733276.

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Zhu, Mengjiang, Tianfu Xu, Qun He, et al. "Research on APT Attack Detection Methods for Power Information Systems." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733125.

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Rajmohan, R., A. Meiappane, Chirag Gupta, and Shivam Bhavsar. "Intelligent SSH Attack Detection Model Using K-Clique Clustering and Reinforcement Learning." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894529.

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Huang, Siyu, Chen Ma, Yinhao Wang, Aibing Qiu, and Jingfeng Mao. "Model-Based Covert Cyber-Attack Detection for Cooling System in Data Center." In 2024 7th International Conference on Robotics, Control and Automation Engineering (RCAE). IEEE, 2024. https://doi.org/10.1109/rcae62637.2024.10834175.

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Gómez, Álvaro Martín, Sina Hassani, and Rafał Wiśniewski. "Reference Model-Based Cyber-Attack Detection for Wind Turbine Systems with Polytopic Uncertainties." In 2025 33rd Mediterranean Conference on Control and Automation (MED). IEEE, 2025. https://doi.org/10.1109/med64031.2025.11073450.

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Desnitsky, Vasily, and Alexey Meleshko. "Modeling and Analysis of Secure Blockchain-Driven Self-Organized Decentralized Wireless Sensor Networks for Attack Detection." In 2024 International Russian Automation Conference (RusAutoCon). IEEE, 2024. http://dx.doi.org/10.1109/rusautocon61949.2024.10694225.

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Ludun, Liu. "Research on Communication Network Attack Detection and Defense Mechanism Based on Deep Learning." In 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869774.

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Raporty organizacyjne na temat "Attack Detection Automation"

Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-ba

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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum

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