Thematische Bibliographien / Known and Zero-Day Attacks Detection

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Auswahl der wissenschaftlichen Literatur zum Thema „Known and Zero-Day Attacks Detection“

Autor: Grafiati

Veröffentlicht am 16. November 2024

Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Known and Zero-Day Attacks Detection"

Saurabh Kansal. "Utilizing Deep Learning Techniques for Effective Zero-Day Attack Detection." Economic Sciences 21, no. 1 (2025): 246–57. https://doi.org/10.69889/m3jzbt24.

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Zero-day attacks take use of undiscovered flaws to evade detection by cybersecurity detection systems. According to the findings, zero-day attacks are prevalent and pose a serious risk to computer security. Zero-day attacks are difficult to detect using the conventional signature-based detection approach since their signatures are usually not accessible in advance. Because machine learning (ML)-based detection techniques can capture the statistical features of assaults, they hold promise for the detection of zero-day attacks. This survey study presents a thorough analysis of ML-based methods f

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Nerella Sameera, M.Siva Jyothi, K.Lakshmaji, and V.S.R.Pavan Kumar. Neeli. "Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks." Journal of Advanced Zoology 44, no. 4 (2023): 969–75. http://dx.doi.org/10.17762/jaz.v44i4.2423.

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Developing effective security measures is the most challenging task now a days and hence calls for the development of intelligent intrusion detection systems. Most of the existing intrusion detection systems perform best at detecting known attacks but fail to detect zero-day attacks due to the lack of labeled examples. Authors in this paper, comes with a clustering-based IDS framework that can effectively detect both known and zero-day attacks by following unsupervised machine learning techniques. This research uses NSL-KDD dataset for the motive of experimentation and the experimental results

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Ohtani, Takahiro, Ryo Yamamoto, and Satoshi Ohzahata. "IDAC: Federated Learning-Based Intrusion Detection Using Autonomously Extracted Anomalies in IoT." Sensors 24, no. 10 (2024): 3218. http://dx.doi.org/10.3390/s24103218.

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The recent rapid growth in Internet of Things (IoT) technologies is enriching our daily lives but significant information security risks in IoT fields have become apparent. In fact, there have been large-scale botnet attacks that exploit undiscovered vulnerabilities, known as zero-day attacks. Several intrusion detection methods based on network traffic monitoring have been proposed to address this issue. These methods employ federated learning to share learned attack information among multiple IoT networks, aiming to improve collective detection capabilities against attacks including zero-day

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Hindy, Hanan, Robert Atkinson, Christos Tachtatzis, Jean-Noël Colin, Ethan Bayne, and Xavier Bellekens. "Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection." Electronics 9, no. 10 (2020): 1684. http://dx.doi.org/10.3390/electronics9101684.

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Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-da

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Abdel Wahed, Mutaz. "AI-Enhanced Threat Intelligence for Proactive Zero-Day Attack Detection." Gamification and Augmented Reality 3 (April 13, 2025): 112. https://doi.org/10.56294/gr2025112.

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Introduction: zero-day attacks pose a critical cybersecurity challenge by targeting vulnerabilities that are undisclosed to software vendors and security experts. Conventional threat intelligence approaches, which rely on known signatures and attack patterns, often fail to detect these stealthy threats.Methods: this study proposes a comprehensive framework that combines AI technologies, including machine learning algorithms, natural language processing (NLP), and anomaly detection, to analyze threats in real time. The framework incorporates predictive modeling to anticipate potential attack ve

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Hairab, Belal Ibrahim, Heba K. Aslan, Mahmoud Said Elsayed, Anca D. Jurcut, and Marianne A. Azer. "Anomaly Detection of Zero-Day Attacks Based on CNN and Regularization Techniques." Electronics 12, no. 3 (2023): 573. http://dx.doi.org/10.3390/electronics12030573.

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The rapid development of cyberattacks in the field of the Internet of things (IoT) introduces new security challenges regarding zero-day attacks. Intrusion-detection systems (IDS) are usually trained on specific attacks to protect the IoT application, but the attacks that are yet unknown for IDS (i.e., zero-day attacks) still represent challenges and concerns regarding users’ data privacy and security in those applications. Anomaly-detection methods usually depend on machine learning (ML)-based methods. Under the ML umbrella are classical ML-based methods, which are known to have low predictio

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Alam, Naushad, and Muqeem Ahmed. "Zero-day Network Intrusion Detection using Machine Learning Approach." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 194–201. http://dx.doi.org/10.17762/ijritcc.v11i8s.7190.

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Zero-day network attacks are a growing global cybersecurity concern. Hackers exploit vulnerabilities in network systems, making network traffic analysis crucial in detecting and mitigating unauthorized attacks. However, inadequate and ineffective network traffic analysis can lead to prolonged network compromises. To address this, machine learning-based zero-day network intrusion detection systems (ZDNIDS) rely on monitoring and collecting relevant information from network traffic data. The selection of pertinent features is essential for optimal ZDNIDS performance given the voluminous nature o

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AL Rafy, Md Mashfiquer Rahman, Sharmin Nahar, Md. Najmul Gony, and MD IMRANUL HOQUE Bhuiyan. "The role of machine learning in predicting zero-day vulnerabilities." International Journal of Science and Research Archive 10, no. 1 (2023): 1197–208. https://doi.org/10.30574/ijsra.2023.10.1.0838.

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Zero-day vulnerabilities keep growing as an important threat in cybersecurity because attackers discover them before security teams can detect them. Signature-based detection methods fail to discover unknown vulnerabilities since they need prior knowledge of known attack techniques. ML technology emerges as the promising tool that predicts zero-day threats before attackers exploit them. This research aims to study the training approach of ML models that detect vulnerabilities by analyzing code structures, behavioral irregularities, and network traffic characteristics. The research examines zer

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Bu, Seok-Jun, and Sung-Bae Cho. "Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection." Electronics 10, no. 12 (2021): 1492. http://dx.doi.org/10.3390/electronics10121492.

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Considering the fatality of phishing attacks, the data-driven approach using massive URL observations has been verified, especially in the field of cyber security. On the other hand, the supervised learning approach relying on known attacks has limitations in terms of robustness against zero-day phishing attacks. Moreover, it is known that it is critical for the phishing detection task to fully exploit the sequential features from the URL characters. Taken together, to ensure both sustainability and intelligibility, we propose the combination of a convolution operation to model the character-l

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Al-Rushdan, Huthifh, Mohammad Shurman, and Sharhabeel Alnabelsi. "On Detection and Prevention of Zero-Day Attack Using Cuckoo Sandbox in Software-Defined Networks." International Arab Journal of Information Technology 17, no. 4A (2020): 662–70. http://dx.doi.org/10.34028/iajit/17/4a/11.

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Networks attacker may identify the network vulnerability within less than one day; this kind of attack is known as zero-day attack. This undiscovered vulnerability by vendors empowers the attacker to affect or damage the network operation, because vendors have less than one day to fix this new exposed vulnerability. The existing defense mechanisms against the zero-day attacks focus on the prevention effort, in which unknown or new vulnerabilities typically cannot be detected. To the best of our knowledge the protection mechanism against zero-day attack is not widely investigated for Software-D

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Dissertationen zum Thema "Known and Zero-Day Attacks Detection"

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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Khraisat, Ansam. "Intelligent zero-day intrusion detection framework for internet of things." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179729.

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Zero-day intrusion detection system faces serious challenges as hundreds of thousands of new instances of malware are being created every day to cause harm or damage to the computer system. Cyber-attacks are becoming more sophisticated, leading to challenges in intrusion detection. There are many Intrusion Detection Systems (IDSs), which are proposed to identify abnormal activities, but most of these IDSs produce a large number of false positives and low detection accuracy. Hence, a significant quantity of false positives could generate a high-level of alerts in a short period of time as the n

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Peddisetty, Naga Raju. "State-of-the-art Intrusion Detection: Technology, Challenges, and Evaluation." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2792.

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<p>Due to the invention of automated hacking tools, Hacking is not a black art anymore. Even script kiddies can launch attacks in few seconds. Therefore, there is a great emphasize on the Security to protect the resources from camouflage. Intrusion Detection System is also one weapon in the security arsenal. It is the process of monitoring and analyzing information sources in order to detect vicious traffic. With its unique capabilities like monitoring, analyzing, detecting and archiving, IDS assists the organizations to combat against threats, to have a snap-shot of the networks, and to condu

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Buchteile zum Thema "Known and Zero-Day Attacks Detection"

Wang, Lingyu, Mengyuan Zhang, and Anoop Singhal. "Network Security Metrics: From Known Vulnerabilities to Zero Day Attacks." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04834-1_22.

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Hamid, Khalid, Muhammad Waseem Iqbal, Muhammad Aqeel, Xiangyong Liu, and Muhammad Arif. "Analysis of Techniques for Detection and Removal of Zero-Day Attacks (ZDA)." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0272-9_17.

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Ngo, Quoc-Dung, and Quoc-Huu Nguyen. "A Reinforcement Learning-Based Approach for Detection Zero-Day Malware Attacks on IoT System." In Artificial Intelligence Trends in Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09076-9_34.

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Singh, Mahendra Pratap, Virendra Pratap Singh, and Maanak Gupta. "Early Detection and Classification of Zero-Day Attacks in Network Traffic Using Convolutional Neural Network." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60935-0_70.

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Blancaflor, Eric B., Charlene Grazielle E. Lara, Leila Joy M. Bautista, Drizzle Joy V. Caberto, and Danielle Franchesca L. Mancilla. "Detecting the Unknown: Evaluating the Efficacy of Host-Based Intrusion Detection Systems (IDS) in Zero-Day Attacks." In Signals and Communication Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78131-5_3.

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Jorquera Valero, José María, Manuel Gil Pérez, Alberto Huertas Celdrán, and Gregorio Martínez Pérez. "Identification and Classification of Cyber Threats Through SSH Honeypot Systems." In Handbook of Research on Intrusion Detection Systems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2242-4.ch006.

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As the number and sophistication of cyber threats increases year after year, security systems such as antivirus, firewalls, or Intrusion Detection Systems based on misuse detection techniques are improved in detection capabilities. However, these traditional systems are usually limited to detect potential threats, since they are inadequate to spot zero-day attacks or mutations in behaviour. Authors propose using honeypot systems as a further security layer able to provide an intelligence holistic level in detecting unknown threats, or well-known attacks with new behaviour patterns. Since brute

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Roseline, S. Abijah, and S. Geetha. "Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security." In Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8241-0.ch011.

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Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but

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Thapa, Vidhanth Maan, Sudhanshu Srivastava, and Shelly Garg. "Zero Day Vulnerabilities Assessments, Exploits Detection, and Various Design Patterns in Cyber Software." In AI Tools for Protecting and Preventing Sophisticated Cyber Attacks. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7110-4.ch006.

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In this technology-driven era, software development and maintenance is a rapidly growing domain and is predestined to thrive over the coming decade. But the growing demand for software solutions also brings its own implications. Software vulnerabilities are the most crucial of these. Software Vulnerabilities can be referred to as weaknesses or shortcomings of the software solutions which increase the risks of exploitation of resources and information. In the past few years, the number of exploits has been increasing rapidly, reaching an all-time high in 2021 affecting more than 100 million peo

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Sethuraman, Murugan Sethuraman. "Survey of Unknown Malware Attack Finding." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3129-6.ch011.

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Intrusion detection system(IDS) has played a vital role as a device to guard our networks from unknown malware attacks. However, since it still suffers from detecting an unknown attack, i.e., 0-day attack, the ultimate challenge in intrusion detection field is how we can precisely identify such an attack. This chapter will analyze the various unknown malware activities while networking, internet or remote connection. For identifying known malware various tools are available but that does not detect Unknown malware exactly. It will vary according to connectivity and using tools and finding stra

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Sethuraman, Murugan Sethuraman. "Survey of Unknown Malware Attack Finding." In Intelligent Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5643-5.ch099.

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Konferenzberichte zum Thema "Known and Zero-Day Attacks Detection"

Anjum, Asma, P. Rama Subramanian, R. Stalinbabu, Deepthi Kothapeta, K. Santha Sheela, and B. Jegajothi. "Detecting Zero-Day Attacks using Advanced Anomaly Detection in Network Traffic." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11034868.

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Chenet, Cristiano Pegoraro, Alessandro Savino, and Stefano Di Carlo. "Zero-Day Hardware-Supported Malware Detection of Stack Buffer Overflow Attacks: An Application Exploiting the CV32e40p RISC-V Core." In 2025 IEEE 26th Latin American Test Symposium (LATS). IEEE, 2025. https://doi.org/10.1109/lats65346.2025.10963939.

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Mote, Mrs Savitri Ashok, and Dr Sudhakar Arabagatte. "Survey on Detection and Mitigation Strategies for Zero-day Attacks: An Extensive Analysis of Current Developments and Case Studies." In First International Conference on Computer, Computation and Communication (IC3C-2025). River Publishers, 2025. https://doi.org/10.13052/rp-9788743808268a037.

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Wang, Di, Tuba Unsal, Tingyue Gu, Sith Kumseranee, and Suchada Punpruk. "Severe Microbiologically Influenced Corrosion (MIC) of Pure Zinc and Galvanized Steel in the Presence of Sulfate Reducing Desulfovibrio Vulgaris." In CORROSION 2020. NACE International, 2020. https://doi.org/10.5006/c2020-14537.

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Abstract Zinc and its alloys are used as sacrificial anodes because zinc is an active metal. Carbon steel can be coated with zinc to protect against corrosion. These metals are known as galvanized steel. In this work, microbiologically influenced corrosion (MIC) of pure zinc and galvanized steel caused by a sulfate reducing bacterium was investigated. After 7 days of incubation in 125 mL anaerobic vials with 100 mL culture medium and 1 mL inoculum, the sessile cell count on the galvanized steel was slightly higher than that on pure zinc. The abiotic weight loss for pure zinc was 1.4 ± 0.1 mg/c

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Wang, Shen, Zhengzhang Chen, Xiao Yu, et al. "Heterogeneous Graph Matching Networks for Unknown Malware Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/522.

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Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohibitively high training cost. To address the limitations of existing techniques, we propose MatchGNet, a heterogeneous Graph Matching Network model to learn the graph representation and similarity metric simultaneously based on the invariant graph modeling of the program's executi

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Sejr, Jonas Herskind, Arthur Zimek, and Peter Schneider-Kamp. "Explainable Detection of Zero Day Web Attacks." In 2020 3rd International Conference on Data Intelligence and Security (ICDIS). IEEE, 2020. http://dx.doi.org/10.1109/icdis50059.2020.00016.

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Reardon, Shay, Murtadha D. Hssayeni, and Imadeldin Mahgoub. "Detection of Zero-Day Attacks on IoT." In 2024 International Conference on Smart Applications, Communications and Networking (SmartNets). IEEE, 2024. http://dx.doi.org/10.1109/smartnets61466.2024.10577735.

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AlEroud, Ahmed, and George Karabatis. "A Contextual Anomaly Detection Approach to Discover Zero-Day Attacks." In 2012 International Conference on Cyber Security (CyberSecurity). IEEE, 2012. http://dx.doi.org/10.1109/cybersecurity.2012.12.

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Gao, Xueqin, Kai Chen, Yufei Zhao, Peng Zhang, Longxi Han, and Daojuan Zhang. "A Zero-Shot Learning-Based Detection Model Against Zero-Day Attacks in IoT." In 2024 9th International Conference on Electronic Technology and Information Science (ICETIS). IEEE, 2024. http://dx.doi.org/10.1109/icetis61828.2024.10593684.

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Radhakrishnan, Kiran, Rajeev R. Menon, and Hiran V. Nath. "A survey of zero-day malware attacks and its detection methodology." In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929620.

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