Relevant bibliographies by topics / Classification of Cyber- Attacks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Classification of Cyber- Attacks'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Classification of Cyber- Attacks"

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Geetha, G., Manjula Rajagopal, and K. Purnachand. "An intelligent hybrid model for cyber attack classification with selected feature set." Intelligent Decision Technologies 18, no. 3 (2024): 2191–212. http://dx.doi.org/10.3233/idt-240362.

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Cyber security evolving as a severe problem almost in all sectors of cyberspace, due to the time-to-time increase in the number of security breaches. Numerous Zero-days attacks occur continuously, due to the increase in multiple protocols. Almost all of these attacks are small variants of previously known cyber attacks. Moreover, even the advanced approach like Machine Learning (ML), faces the difficulty in identifying those attack’s small mutants over time. Recently, Deep Learning (DL) has been utilized for multiple applications related to cybersecurity fields. Making use of this DL to identi
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Abirami, A., S. Lakshmanaprakash, R. L. Priya, Vaishali Hirlekar, and Bhargavi Dalal. "Proactive Analysis and Detection of Cyber-attacks using Deep Learning Techniques." Indian Journal Of Science And Technology 17, no. 15 (2024): 1596–605. http://dx.doi.org/10.17485/ijst/v17i15.3044.

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Objectives: This study objective is to create a proactive forensic framework with a classification model to identify the malicious content to avoid cyber-attacks. Methods: In this proposed work, a novel framework is introduced to analyze and detect network attacks before it happens. It monitors the network packet flow, captures the packets, analyzes the packet flow proactively, and detects cyber-attacks using different machine learning algorithms and Deep Convolution Neural network (CNN) technique. The KDD dataset is used in this experiment with 30% for testing and 80% for training. Findings:
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MT, Suryadi, Achmad Eriza Aminanto, and Muhamad Erza Aminanto. "Empowering Digital Resilience: Machine Learning-Based Policing Models for Cyber-Attack Detection in Wi-Fi Networks." Electronics 13, no. 13 (2024): 2583. http://dx.doi.org/10.3390/electronics13132583.

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In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’s Directorate of Cyber Crime is expected to play a preventive role in supervising these attacks, despite lacking a specific cyber-attack prevention function. An Intrusion Detection System (IDS), employing artificial intelligence, can differentiate between cyber-attacks and non-attacks. This study focuses on developing a machine learning-based policing mo
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Dasari, Kishore Babu, and Nagaraju Devarakonda. "Detection of DDoS Attacks Using Machine Learning Classification Algorithms." International Journal of Computer Network and Information Security 14, no. 6 (2022): 89–97. http://dx.doi.org/10.5815/ijcnis.2022.06.07.

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The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accu
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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 (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 p
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al-Khateeb, Haider, Rafael Salema Marques, Gregory Epiphaniou, and Carsten Maple. "Pivot Attack Classification for Cyber Threat Intelligence." Journal of Information Security and Cybercrimes Research 5, no. 2 (2022): 91–103. http://dx.doi.org/10.26735/zntl3639.

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The initial access achieved by cyber adversaries conducting a systematic attack against a targeted network is unlikely to be an asset of interest. Therefore, it is necessary to use lateral movement techniques to expand access to different devices within the network to accomplish the strategic attack’s objectives. The pivot attack technique is widely used in this context; the attacker creates an indirect communication tunnel with the target and uses traffic forwarding methods to send and receive commands. Recognising and classifying this technique in large corporate networks is a complex task,
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A, Abirami, Lakshmanaprakash S, L. Priya R, Hirlekar Vaishali, and Dalal Bhargavi. "Proactive Analysis and Detection of Cyber-attacks using Deep Learning Techniques." Indian Journal of Science and Technology 17, no. 15 (2024): 1596–605. https://doi.org/10.17485/IJST/v17i15.3044.

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Abstract <strong>Objectives:</strong>&nbsp;This study objective is to create a proactive forensic framework with a classification model to identify the malicious content to avoid cyber-attacks.&nbsp;<strong>Methods:</strong>&nbsp;In this proposed work, a novel framework is introduced to analyze and detect network attacks before it happens. It monitors the network packet flow, captures the packets, analyzes the packet flow proactively, and detects cyber-attacks using different machine learning algorithms and Deep Convolution Neural network (CNN) technique. The KDD dataset is used in this experi
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Hasan Jarjis, Alend, Nassima Yousef Saleem Al Zubaidi, and Meltem Kurt Pehlivanoglu. "Cyber Attacks Classification on Enriching IoT Datasets." EAI Endorsed Transactions on Internet of Things 9, no. 3 (2023): e2. http://dx.doi.org/10.4108/eetiot.v9i3.3030.

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In the era of the 5.0 industry, the use of the Internet of Things (IoT) has increased. The data generates from sensors through IoT industrial systems, any fault in those systems affects their performance and leads to real disaster. Protecting them from any possible attacks is an essential task. to secure any system, it needs to predict in the first place possible attacks and faults that could happen in the future. Predicting and initiating the attack type and the accuracy of these predictions can be done with machine learning models nowadays on the datasets produced with IoT networks. This pap
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Dasari, Kishore Babu, and Nagaraju Devarakonda. "Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms." Ingénierie des systèmes d information 26, no. 5 (2021): 461–68. http://dx.doi.org/10.18280/isi.260505.

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Cyber attacks are one of the world's most serious challenges nowadays. A Distributed Denial of Service (DDoS) attack is one of the most common cyberattacks that has affected availability, which is one of the most important principles of information security. It leads to so many negative consequences in terms of business, production, reputation, data theft, etc. It shows the importance of effective DDoS detection mechanisms to reduce losses. In order to detect DDoS attacks, statistical and data mining methods have not been given good accuracy values. Researchers get good accuracy values while d
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Kadirov, M. M. "АНАЛИЗ И КЛАССИФИКАЦИЯ КИБЕРАТАК НА ИНФОРМАЦИОННО-КОММУНИКАЦИОННЫЕ СИСТЕМЫ". Journal of Science and Innovative Development 6, № 4 (2023): 27–36. http://dx.doi.org/10.36522/2181-9637-2023-4-3.

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This article analyzes distributed cyberattacks of the “Denial of Service” type and develops their classification. A description of the shortcomings and vulnerabilities of distributed denial-of-service attacks by category is given. An implementation diagram with examples for each category of distributed denial of service attacks is presented. Analyzed the number of attacks carried out by attackers, as well as the purpose and type of attack used. According to the results of the study, the UDP-flood 53.64% attack type is the most commonly used type by attackers in implementing distributed attacks
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Dissertations / Theses on the topic "Classification of Cyber- Attacks"

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Lyn, Kevin G. "Classification of and resilience to cyber-attacks on cyber-physical systems." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53926.

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The growing connectivity of cyber-physical systems (CPSes) has led to an increased concern over the ability of cyber-attacks to inflict physical damage. Current cybersecurity measures focus on preventing attacks from penetrating control supervisory networks. These reactive techniques, however, are often plagued with vulnerabilities and zero-day exploits. Embedded processors in CPS field devices often possess little security of their own, and are easily exploited once the network is penetrated. In response, researchers at Georgia Tech and Virginia Tech have proposed a Trustworthy Autonomic Inte
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Thames, John Lane. "Advancing cyber security with a semantic path merger packet classification algorithm." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45872.

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This dissertation investigates and introduces novel algorithms, theories, and supporting frameworks to significantly improve the growing problem of Internet security. A distributed firewall and active response architecture is introduced that enables any device within a cyber environment to participate in the active discovery and response of cyber attacks. A theory of semantic association systems is developed for the general problem of knowledge discovery in data. The theory of semantic association systems forms the basis of a novel semantic path merger packet classification algorithm. The theo
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Hurst, William. "Behavioural observation for critical infrastructure security support." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4382/.

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Critical infrastructures include sectors such as energy resources, finance, food and water distribution, health, manufacturing and government services. In recent years, critical infrastructures have become increasingly dependent on ICT; more interconnected and are often, as a result, linked to the Internet. Consequently, this makes these systems more vulnerable and increases the threat of cyber-attack. In addition, the growing use of wireless networks means that infrastructures can be more susceptible to a direct digital attack than ever before. Traditionally, protecting against environmental
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Kameníček, Lukáš. "Kybernetická bezpečnost z pohledu podnikové informatiky." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264292.

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This thesis deals with the issue of cyber security in enterprises. The main objective of this thesis is to compile a comprehensive overview of the issue of cyber security related to the Czech business entities. It is written in the context in Czech language and in less technical form. This thesis first identifies the current cyber threats, supported by examples of actual incidents. On the basis of the threats thesis defines the categories of business entities that are vulnerable to the threats, and with regard to the Czech legal system analyzes cybersecurity responsibilities. Above the legal o
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Bedhammar, Jennifer, and Oliver Johansson. "Visualization of cyber security attacks." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167144.

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The Swedish Defence Research Agency (FOI) simulates cyber attacks for research and education purposes in their cyber range, CRATE, with a system called SVED. This thesis describes the process of creating a visualization of the log files produced by SVED, with the purpose of increasing the users’ comprehension of the log files and thereby increasing their knowledge of the simulated attacks. To create an effective visualization a user study was held to know the users’ needs, experiences and requirements. Several designs were created based on the results and one was selected and refined using fee
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Masood, Ummi Hani Binti. "Countering cyber attacks in Malaysian law : assessing the concept of cyber attacks and the countermeasures." Thesis, University of Leeds, 2017. http://etheses.whiterose.ac.uk/17787/.

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An examination of the nature of cyber attacks (meaning attacks on computer systems and the disruption of national security and order through online seditious and defamatory statements) and the appropriate countermeasures under the law of Malaysia and international law are undertaken in this project. This study explores the emergence of cyber attacks as a serious threat to security and a challenge to current legal norms. As such, it uses ontologies to encapsulate and analyse the existence and reality of the cyber attacks phenomenon. It provides an open-ended concept and categories of cyber atta
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Hätty, Niklas. "Representing attacks in a cyber range." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159838.

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Trained security experts can be a mitigating factor to sophisticated cyberattacks that aim to violate the confidentiality, integrity, and availability of information. Reproducible sessions in a safe training environment is an effective way of increasing the excellence of security experts. One approach to achieving this is by using cyber ranges, which essentially is a set of hardware nodes that can virtually represent a large organization or system. The Swedish Defense Research Agency (FOI) develops and maintains a fully functioning cyber range and has the ability to automatically deploy sophis
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Liivakant, André. "Web Warfare : NATO, Cyber-attacks and Territoriality." Thesis, Uppsala universitet, Statsvetenskapliga institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-341302.

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Dai, Pei-Ying. "Non-technical approaches to fighting cyber attacks." Thesis, University of Lincoln, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685305.

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Yermalovich, Pavel. "Risk estimation and prediction of cyber attacks." Doctoral thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/67982.

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L’utilisation de l’information est étroitement liée à sa sécurité. Le fait d’exploiter des vulnérabilités permet à une tierce personne de compromettre la sécurité d’un système d’information. La modélisation des menaces aide à prévoir les attaques les plus probables visant une infrastructure donnée afin de mieux les contrer. Le projet de recherche proposé « Estimation des risques et prédiction des cyber-attaques » vise la combinaison de différentes techniques de prédiction de cyber-attaques pour mieux protéger un système informatique. Il est nécessaire de trouver les paramètres les plus informa
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Books on the topic "Classification of Cyber- Attacks"

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Amoroso, Edward G. Cyber attacks: Protecting national infrastructure. Butterworth-Heinemann, 2012.

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Iglezakis, Ioannis. The legal regulation of cyber attacks. Kluwer Law International B. V., 2016.

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Bhardwaj, Akashdeep, and Varun Sapra, eds. Security Incidents & Response Against Cyber Attacks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69174-5.

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Cyber Attacks. Elsevier, 2011. http://dx.doi.org/10.1016/c2010-0-64972-8.

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Targeted Cyber Attacks. Elsevier, 2014. http://dx.doi.org/10.1016/c2013-0-14275-4.

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Cyber-Physical Attacks. Elsevier, 2015. http://dx.doi.org/10.1016/c2013-0-19393-2.

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Amoroso, Edward. Cyber Attacks: Protecting National Infrastructure. Elsevier Science & Technology Books, 2010.

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Stiennon, Richard. Cyber Defense: Countering Targeted Attacks. Rowman & Littlefield Publishers, Incorporated, 2012.

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Cyber Defense: Countering Targeted Attacks. Government Institutes, 2011.

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Cyber Attacks, Counterattacks, and Espionage. Rosen Publishing Group, 2016.

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Book chapters on the topic "Classification of Cyber- Attacks"

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Toms, Joel Mathew, Setareh Jalali Ghazaani, Sina Pournouri, and Eghe Ehiorobo. "Profiling Suspected Chinese Cyber Attacks by Classification Techniques." In Advanced Sciences and Technologies for Security Applications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20160-8_16.

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Specht, Felix, and Jens Otto. "Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_11.

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AbstractCondition monitoring systems based on deep neural networks are used for system failure detection in cyber-physical production systems. However, deep neural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, e.g. sensor signals, are able to mislead a deep neural network into misclassification. A consequence of such an attack may be the manipulation of the physical production process of a cyber-physical production system without being recognized by the condition monitoring system. This can result in a serious threat for production systems and employees. This work introduces an approach named CyberProtect to prevent misclassification caused by adversarial example attacks. The approach generates adversarial examples for retraining a deep neural network which results in a hardened variant of the deep neural network. The hardened deep neural network sustains a significant better classification rate (82% compared to 20%) while under attack with adversarial examples, as shown by empirical results.
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Kapoor, Meet, Rishabh Aggarwal, and Suman Madan. "Cyber-Security: Critical Analysis on Attacks, Classification, and Issues." In Innovative Computing and Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3588-4_40.

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Hasan, Basil Abbas Hasan, and Muhammad Ilyas. "Improving Classification Accuracy of Cyber Attacks in the Banking Sector." In Computational Intelligence, Data Analytics and Applications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27099-4_43.

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Shahin, Mohammad, F. Chen, Hamed Bouzary, Ali Hosseinzadeh, and Rasoul Rashidifar. "Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_10.

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AbstractThe term “the Industrial Internet of Things” has become increasingly more pervasive in the context of manufacturing as digitization has become a business priority for many manufacturers. IIoT refers to a network of interconnected industrial devices, resulting in systems that can monitor, collect, exchange, analyze, and deliver valuable data and new insights. These insights can then help drive smarter, and faster business decisions for manufacturers. However, these benefits have come at the cost of creating a new attack vector for the malicious agents that aim at stealing manufacturing trade secrets, blueprints, or designs. As a result, cybersecurity concerns have become more relevant across the field of manufacturing. One of the main tracks of research in this field deals with developing effective cyber-security mechanisms and frameworks that can identify, classify, and detect malicious attacks in industrial IoT devices. In this paper, we have developed and implemented a classification and detection framework for addressing cyber-security concerns in industrial IoT which takes advantage of various machine learning algorithms. The results prove the satisfactory performance and robustness of the approach in classifying and detecting the attacks.
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Yang, Wenzhuo, and Kwok-Yan Lam. "Automated Cyber Threat Intelligence Reports Classification for Early Warning of Cyber Attacks in Next Generation SOC." In Information and Communications Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41579-2_9.

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Kumar, Sheo, C. Syamsunda, R. Venkateswara Reddy, R. Suhasini, G. Vinoda Reddy, and Adepu Kiran Kumar. "Analysis on Identifying and Attributing of Cyber-Attacks in Cyber-Physical Classification Through Internet of Things." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78949-6_37.

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Alsadi, Naseem, Hadis Karimipour, Ali Dehghantanha, and Gautam Srivastava. "A Recurrent Attention Model for Cyber Attack Classification." In AI-Enabled Threat Detection and Security Analysis for Industrial IoT. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76613-9_13.

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Kandula, Pradeep, Monideepa Roy, Kuntal Ghosh, Budipi Nageswara Rao, and Sujoy Datta. "A Classification of Attacks in an IDS Using Sparse Convolutional Autoencoder and DNN." In Proceedings of 3rd International Conference on Smart Computing and Cyber Security. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0573-3_5.

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Wass, Shannon, Sina Pournouri, and Gregg Ibbotson. "Prediction of Cyber Attacks During Coronavirus Pandemic by Classification Techniques and Open Source Intelligence." In Cybersecurity, Privacy and Freedom Protection in the Connected World. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68534-8_6.

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Conference papers on the topic "Classification of Cyber- Attacks"

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Bennet, Deepthi Tabitha, Preethi Samantha Bennet, Anitha Gracy J, and Sharmila P. "Combating Cyber Attacks by Malware Classification Using Deep Neural Networks." In 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM). IEEE, 2024. https://doi.org/10.1109/iipem62726.2024.10925735.

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Maham, Salah Dine, Guy Pujolle, Atiq Ahmed, and Dominique Gaiti. "A New Similarity-Based Classification Scheme of Drone Network Attacks." In 2024 8th Cyber Security in Networking Conference (CSNet). IEEE, 2024. https://doi.org/10.1109/csnet64211.2024.10851739.

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Fehrman, Brian, Francis Akowuah, and Randy Hoover. "Evading VBA Malware Classification using Model Extraction Attacks and Stochastic Search Methods." In 2024 Cyber Awareness and Research Symposium (CARS). IEEE, 2024. https://doi.org/10.1109/cars61786.2024.10778829.

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Ruuhwan, Rikza Fauzan Nuradiah, Aso Sudiarjo, Dhema Yunautama, Gunawansyah, and Edi Andriansyah. "A Comparative Analysis of Logistic Regression and Random Forest Algorithm for Cyber Attacks Classification." In 2024 18th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2024. https://doi.org/10.1109/tssa63730.2024.10863823.

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Bhatnagar, Amrita, Arun Giri, and Aditi Sharma. "Anomaly Intrusion Detection System Based On RNN-LSTM For Cyber-Attack Classification." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687401.

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J, Jasper, Praveen B. M, and Berlin Shaheema S. "Res2-UNeXt Combined with Federated Learning for Cyber-Attack Detection and Classification in Multi Area Smart Grid Power System." In 2024 IEEE Silchar Subsection Conference (SILCON). IEEE, 2024. https://doi.org/10.1109/silcon63976.2024.10910310.

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Yasir, A., and Kalaivani Kathirvelu. "LDA-OOBO Based Dimensionality Reduction and Classification Using Hybrid BiGRU-MLP for Web Based Cyber-Attack Prediction in Industrial System." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673215.

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Almass, Shafeyeen, and Sunil Kumar Chowdhary. "Comprehensive Study on Cyber Security and Cyber Attacks." In 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP). IEEE, 2024. http://dx.doi.org/10.1109/icecsp61809.2024.10698540.

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Espinoza-Zelaya, Carlos, and Young Moon. "Taxonomy of Severity of Cyber-Attacks in Cyber-Manufacturing Systems." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94492.

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Abstract Increasingly named as the number one non-traditional risk cyber-attacks against Cyber-manufacturing Systems (CMS) can cause a wide variety of losses. As the 4th industrial revolution is taking place CMS have become more resilient with the implementation of prevention, detection, redundancy, withstanding, and recovery mechanisms against cyber-attacks. However, the ever-evolving nature of these threats require systems to still be prepared for their eventual occurrence as it’s been demonstrated in the increasingly more common advent of successful cyber-attacks. While multiple generic thr
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Aref, Yazan, and Abdelkader Ouda. "Autonomous Vehicle Cyber-Attacks Classification Framework." In 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2023. http://dx.doi.org/10.1109/comsnets56262.2023.10041387.

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Reports on the topic "Classification of Cyber- Attacks"

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de Barros Barreto, Alexandre, Paulo Costa, and Michael Hieb. Cyber-Argus: Modeling C2 Impacts of Cyber Attacks. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada607024.

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Buchanan, Ben, John Bansemer, Dakota Cary, Jack Lucas, and Micah Musser. Automating Cyber Attacks: Hype and Reality. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/2020ca002.

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Based on an in-depth analysis of artificial intelligence and machine learning systems, the authors consider the future of applying such systems to cyber attacks, and what strategies attackers are likely or less likely to use. As nuanced, complex, and overhyped as machine learning is, they argue, it remains too important to ignore.
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Juremi, Julia. Malaysia’s blueprint to block cyber attacks. Edited by Shahirah Hamid and Chris Bartlett. Monash University, 2023. http://dx.doi.org/10.54377/6851-cf78.

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Detlefsen, William R. Cyber Attacks, Attribution, and Deterrence: Three Case Studies. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1001276.

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Dong Wei, Yan Lu, Mohsen Jafari, Paul Skare, and Kenneth Rohde. Protecting Intelligent Distributed Power Grids against Cyber Attacks. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1033753.

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Allen, J. Techniques for Judging Intent Behind Network Based Cyber Attacks. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/15013916.

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Park, Ji Min. Finding Effective Responses Against Cyber Attacks for Divided Nations. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1009192.

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Vatis, Michael A. Cyber Attacks During the War on Terrorism: A Predictive Analysis. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada386280.

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Scholten, Wade. Cyber Security Classification Model Evaluation and Comparison. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-472.

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Harriss, Lydia, and Clare West. Cyber Security of Consumer Devices. Parliamentary Office of Science and Technology, 2019. http://dx.doi.org/10.58248/pn593.

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Abstract:
Weaknesses in the cyber security of internet-connected consumer devices can undermine the privacy and safety of individual users and can be used for large-scale cyber-attacks. This briefing looks at the cyber threats associated with consumer devices and their causes, as well as initiatives to improve device security, and the related challenges.
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