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Journal articles on the topic 'Known and Zero-Day Attacks Detection'

Author: Grafiati

Published: 16 November 2024

Last updated: 31 July 2025

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1

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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Rodríguez, Eva, Pol Valls, Beatriz Otero, et al. "Transfer-Learning-Based Intrusion Detection Framework in IoT Networks." Sensors 22, no. 15 (2022): 5621. http://dx.doi.org/10.3390/s22155621.

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Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine learning (ML), especially deep Learning (DL), to improve the detection of cyberattacks. DL-based IDSs require balanced datasets with large amounts of labeled data; however, there is a lack of such large collections in IoT networks. This paper proposes an efficient intrusion detection framework based on transfer learning (TL), knowledge transfer, and model refinement, for the effec

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Liang, Kai, Chuanfeng Li, and Qiong Duan. "SAEDF: A Synthetic Anomaly-Enhanced Detection Framework for Detection of Unknown Network Attacks." Information Technology and Control 54, no. 2 (2025): 593–612. https://doi.org/10.5755/j01.itc.54.2.40247.

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Detecting unknown cyber-attacks (i.e., zero-day) is difficult because network environments change frequently and there are few labeled examples of anomalies. Traditional methods for detecting anomalies often struggle to handle unknown attack types and work effectively with complex, high-dimensional data. To overcome these problems, we propose a new approach called the synthetic attack-enhanced detection framework (SAEDF). SAEDF combines synthetic anomaly generation, flexible feature extraction, and unsupervised anomaly detection. The framework employs a model known as the adaptive and dynamic

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Sheikh, Zakir Ahmad, Yashwant Singh, Pradeep Kumar Singh, and Paulo J. Sequeira Gonçalves. "Defending the Defender: Adversarial Learning Based Defending Strategy for Learning Based Security Methods in Cyber-Physical Systems (CPS)." Sensors 23, no. 12 (2023): 5459. http://dx.doi.org/10.3390/s23125459.

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Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the other hand, rise in complexities, aiming for more powerful attacks and evasion from detections. The real-world applicability of CPS thus poses a question mark due to security infringements. Researchers have been developing new and robust techniques to enhance the security of these systems. Many techniques and security aspects are being considered to b

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Mala, V., and K. Meena. "Hybrid classification model to detect advanced intrusions using data mining techniques." International Journal of Engineering & Technology 7, no. 2.4 (2018): 10. http://dx.doi.org/10.14419/ijet.v7i2.4.10031.

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Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn ab

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Ali, Shamshair, Saif Ur Rehman, Azhar Imran, Ghazif Adeem, Zafar Iqbal, and Ki-Il Kim. "Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection." Electronics 11, no. 23 (2022): 3934. http://dx.doi.org/10.3390/electronics11233934.

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Many intrusion detection and prevention systems (IDPS) have been introduced to identify suspicious activities. However, since attackers are exploiting new vulnerabilities in systems and are employing more sophisticated advanced cyber-attacks, these zero-day attacks remain hidden from IDPS in most cases. These features have incentivized many researchers to propose different artificial intelligence-based techniques to prevent, detect, and respond to such advanced attacks. This has also created a new requirement for a comprehensive comparison of the existing schemes in several aspects ; after a t

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Das, Saikat, Mohammad Ashrafuzzaman, Frederick T. Sheldon, and Sajjan Shiva. "Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks." Algorithms 17, no. 3 (2024): 99. http://dx.doi.org/10.3390/a17030099.

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The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastructure. Machine-learning-based approaches have shown promise in developing intrusion detection systems (IDSs) for detecting cyber-attacks, such as DDoS. Herein, we present a solution to detect DDoS attacks through an ensemble-based machine learning approach that combines supervised and unsupervised machine learning ensemble frameworks. This comb

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Sugiyatno, Sugiyatno, and Didik Setiyadi. "Efektivitas Honeynet dalam Mendeteksi Serangan Siber." SATESI: Jurnal Sains Teknologi dan Sistem Informasi 4, no. 1 (2024): 37–42. https://doi.org/10.54259/satesi.v4i1.2658.

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Various cyberattack threats are sophisticated and reliable detection approaches, as complex and rampant as they are. One outstanding approach is the use of Honeynet, a network simulator that simulates real networks for analysis and detection purposes. This study aims to compare the effectiveness of Honeynet in detecting spyware with alternative detection methods. We conducted experiments where we implemented Honeynet in a simulated network environment that breaks the real network infrastructure. Other detection methods we reference include intrusion detection systems (IDS) based on hands and b

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Nkongolo, Mike, Jacobus Philippus van Deventer, and Sydney Mambwe Kasongo. "UGRansome1819: A Novel Dataset for Anomaly Detection and Zero-Day Threats." Information 12, no. 10 (2021): 405. http://dx.doi.org/10.3390/info12100405.

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This research attempts to introduce the production methodology of an anomaly detection dataset using ten desirable requirements. Subsequently, the article presents the produced dataset named UGRansome, created with up-to-date and modern network traffic (netflow), which represents cyclostationary patterns of normal and abnormal classes of threatening behaviours. It was discovered that the timestamp of various network attacks is inferior to one minute and this feature pattern was used to record the time taken by the threat to infiltrate a network node. The main asset of the proposed dataset is i

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Peppes, Nikolaos, Theodoros Alexakis, Evgenia Adamopoulou, and Konstantinos Demestichas. "The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers." Sensors 23, no. 2 (2023): 900. http://dx.doi.org/10.3390/s23020900.

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Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this conte

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Wang, Hui, Yifeng Wang, and Yuanbo Guo. "Unknown network attack detection method based on reinforcement zero-shot learning." Journal of Physics: Conference Series 2303, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2303/1/012008.

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Abstract With the increasing growth of zero-day attacks, traditional machine learning-based network intrusion detection systems (NIDS) are difficult to cope with a large amount of unknown network attacks without labeled data. To this end, this paper proposes a new unknown network attack detection method, which combines zero-shot learning algorithm with reinforcement learning algorithm. First, the feature vector in traffic data and the semantic vector in threat intelligence are encoded in the hidden space by variational autoencoder, so that the two modalities are matched in the hidden space, an

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Amamra, Abdelfattah, and Vincent Terrelonge. "Multiple Kernel Transfer Learning for Enhancing Network Intrusion Detection in Encrypted and Heterogeneous Network Environments." Electronics 14, no. 1 (2024): 80. https://doi.org/10.3390/electronics14010080.

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Conventional supervised machine learning is widely used for intrusion detection without packet payload inspection, showing good accuracy in detecting known attacks. However, these methods require large labeled datasets, which are scarce due to privacy concerns, and struggle with generalizing to real-world traffic and adapting to domain shifts. Additionally, they are ineffective against zero-day attacks and need frequent retraining, making them difficult to maintain in dynamic network environments. To overcome the limitations of traditional machine learning methods, we propose novel Determinist

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Subbarayalu, Venkatraman, and Maria Anu Vensuslaus. "An Intrusion Detection System for Drone Swarming Utilizing Timed Probabilistic Automata." Drones 7, no. 4 (2023): 248. http://dx.doi.org/10.3390/drones7040248.

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Unmanned aerial vehicles (UAVs), commonly known as drones, have found extensive applications across diverse sectors, such as agriculture, delivery, surveillance, and military. In recent times, drone swarming has emerged as a novel field of research, which involves multiple drones working in collaboration towards a shared objective. This innovation holds immense potential in transforming the way we undertake tasks, including military operations, environmental monitoring, and search and rescue missions. However, the emergence of drone swarms also brings new security challenges, as they can be su

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Emmah, Victor T., Chidiebere Ugwu, and Laeticia N. Onyejegbu. "An Enhanced Classification Model for Likelihood of Zero-Day Attack Detection and Estimation." European Journal of Electrical Engineering and Computer Science 5, no. 4 (2021): 69–75. http://dx.doi.org/10.24018/ejece.2021.5.4.350.

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The growing threat to sensitive information stored in computer systems and devices is becoming alarming. This is as a result of the proliferation of different malware created on a daily basis to cause zero-day attacks. Most of the malware whose signatures are known can easily be detected and blocked, however, the unknown malwares are the most dangerous. In this paper a zero-day vulnerability model based on deep-reinforcement learning is presented. The technique employs a Monte Carlo Based Pareto Rule (Deep-RL-MCB-PR) approach that exploits a reward learning and training feature with sparse fea

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Yao, Wenbin, Longcan Hu, Yingying Hou, and Xiaoyong Li. "A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for IoT." Sensors 23, no. 8 (2023): 4141. http://dx.doi.org/10.3390/s23084141.

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Network intrusion detection technology is key to cybersecurity regarding the Internet of Things (IoT). The traditional intrusion detection system targeting Binary or Multi-Classification can detect known attacks, but it is difficult to resist unknown attacks (such as zero-day attacks). Unknown attacks require security experts to confirm and retrain the model, but new models do not keep up to date. This paper proposes a Lightweight Intelligent NIDS using a One-Class Bidirectional GRU Autoencoder and Ensemble Learning. It can not only accurately identify normal and abnormal data, but also identi

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José, Tomás Martínez Garre, Gil Pérez Manuel, and Ruiz Martínez Antonio. "A Novel Machine Learning-Based Approach for the Detection of SSH Botnet Infection." Future Generation Computer Systems 115 (February 1, 2021): 387–96. https://doi.org/10.1016/j.future.2020.09.004.

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Botnets are causing severe damages to users, companies, and governments through information theft, abuse of online services, DDoS attacks, etc. Although significant research is being made to detect them and mitigate their effect, they are exponentially increasing due to new zero-day attacks, a variation of their behavior, and obfuscation techniques. High Interaction Honeypots (HIH) are the only honeypots able to capture attacks and log all the information generated by attackers when setting up a botnet. The data generated is being processed using Machine Learning (ML) techniques for detection

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Mehedy, Hasan MD. "Combating Evolving Threats: A Signature-Anomaly Based Hybrid Intrusion Detection System for Smart Homes with False Positive Mitigation." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 403–11. http://dx.doi.org/10.22214/ijraset.2024.61393.

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Abstract: As people are looking for a more comfortable life, IoT applications are coming to play. Smart home system is one of the most popular IoT applications in the last decade. A smart home network is crucial to function smart home system properly. Cyber attacks on a smart home network can damage a lot. Network intrusion detection and prevention system (NIDPS) is a good solution to protect against Cyber threat in smart home network. This research will implement hybrid NIDPS in smart home network by combining signature based and anomaly-detection based NIDPS. This hybrid NIDPS will prevent k

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Neuschmied, Helmut, Martin Winter, Branka Stojanović, Katharina Hofer-Schmitz, Josip Božić, and Ulrike Kleb. "APT-Attack Detection Based on Multi-Stage Autoencoders." Applied Sciences 12, no. 13 (2022): 6816. http://dx.doi.org/10.3390/app12136816.

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In the face of emerging technological achievements, cyber security remains a significant issue. Despite the new possibilities that arise with such development, these do not come without a drawback. Attackers make use of the new possibilities to take advantage of possible security defects in new systems. Advanced-persistent-threat (APT) attacks represent sophisticated attacks that are executed in multiple steps. In particular, network systems represent a common target for APT attacks where known or yet undiscovered vulnerabilities are exploited. For this reason, intrusion detection systems (IDS

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Hassnain, Muhammad, Ibrahim Ahmed Qureshi, and Ammar Haider. "Detection and Identification of Novel Attacks in Phishing using AI Algorithms." International Journal of Computer Science and Mobile Computing 14, no. 3 (2025): 20–27. https://doi.org/10.47760/ijcsmc.2025.v14i03.003.

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Phishing attacks pose a significant threat to cybersecurity, exploiting human vulnerabilities to compromise sensitive information and undermine trust in digital communication. Currently, anti-phishing techniques that have been predominantly researched and used in software products include list-based and web-content based approaches. While these techniques provide excellent accuracy against previously known phishing attacks, they offer subpar accuracy against zero-day or novel attacks. Acknowledging this gap, our project, “Detection and Identification of Novel Attacks in Phishing using Artifici

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Venu Gopal Bitra, Ajay Kumar, Seshagiri Rao, Prakash, and Md. Shakeel Ahmed. "Comparative analysis on intrusion detection system using machine learning approach." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 2555–62. http://dx.doi.org/10.30574/wjarr.2024.21.3.0983.

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The increasing popularity of online data storage and access has raised concerns about security and privacy in the face of growing online threats. However, with the rise of online threats, security and privacy have become major concerns. Intrusion detection systems (IDS) play an important role in protecting data integrity by identifying and quarantining records in the event of unexpected changes. Anomaly-based IDS, which uses machine learning-based approach and algorithms, is an effective way to detect known and unknown attacks, including zero-day attacks. The proposed project is used to create

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Venu, Gopal Bitra, Kumar Ajay, Rao Seshagiri, Prakash, and Shakeel Ahmed Md. "Comparative analysis on intrusion detection system using machine learning approach." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 2555–62. https://doi.org/10.5281/zenodo.14182003.

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The increasing popularity of online data storage and access has raised concerns about security and privacy in the face of growing online threats. However, with the rise of online threats, security and privacy have become major concerns. Intrusion detection systems (IDS) play an important role in protecting data integrity by identifying and quarantining records in the event of unexpected changes. Anomaly-based IDS, which uses machine learning-based approach and algorithms, is an effective way to detect known and unknown attacks, including zero-day attacks. The proposed project is used to create

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Kamal, Hesham, and Maggie Mashaly. "Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques." Future Internet 16, no. 12 (2024): 481. https://doi.org/10.3390/fi16120481.

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Network and cloud environments must be fortified against a dynamic array of threats, and intrusion detection systems (IDSs) are critical tools for identifying and thwarting hostile activities. IDSs, classified as anomaly-based or signature-based, have increasingly incorporated deep learning models into their framework. Recently, significant advancements have been made in anomaly-based IDSs, particularly those using machine learning, where attack detection accuracy has been notably high. Our proposed method demonstrates that deep learning models can achieve unprecedented success in identifying

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Merugu, Akshay, Hrishikesh Goud Chagapuram, and Rahul Bollepalli. "Spam Email Detection Using Convolutional Neural Networks: An Empirical Study." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 981–91. http://dx.doi.org/10.22214/ijraset.2023.56143.

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Abstract: This study leverages Convolutional Neural Networks (CNNs); a state-of-the-art deep learning architecture primarily used in image analysis, and adapts it for the detection of phishing emails. By treating email content as multi-dimensional data, we employ CNNs to extract meaningful features and patterns from email headers, text, and attachments. Our approach not only identifies known phishing templates but also has the capability to detect emerging and zero-day phishing attacks

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Sk, Mr Shafiulilah. "AI-Driven Network Intrusion Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1481–86. https://doi.org/10.22214/ijraset.2025.67539.

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In the evolving landscape of network security, conventional Intrusion Detection Systems (IDS) often fall short in addressing sophisticated and novel cyber threats. It provides an advanced approach to Network Intrusion Detection by leveraging Generative Adversarial Networks (GANs) to enhance detection accuracy and adaptability. The proposed system integrates GANs to generate synthetic attack patterns and improve anomaly detection capabilities. By training a GAN with diverse network traffic data, our method not only detects known threats but also identifies previously unseen attack vectors with

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Agrawal, Kavita, Suresh Chittineni, P.V.G. D. Prasad Reddy, and Subhadra Kompella. "Intrusion Detection for Cyber Security: A Comparative Study of Machine Learning, Deep Learning and Transfer Learning Methods." International Journal of Microsystems and IoT 2, no. 1 (2024): 483–91. https://doi.org/10.5281/zenodo.10665195.

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With the increasing frequency and sophistication of cyber-attacks, intrusion detection has become a critical cybersecurity component to ensure the resilience and trustworthiness of modern digital systems and networks. Several machine learning and deep learning algorithms have been used.  However, there is limited data on the comparative efficacy of these systems. We analyzed the usage of predefined machine learning algorithms (Logistic Regression, Decision Trees, Random Forest, Gaussian Naïve Bayes, Linear Support Vector Machine, and Gradient Boosting) and neural network centered dee

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Zhou, Ce, Yilun Liu, Weibin Meng, et al. "SRDC: Semantics-based Ransomware Detection and Classification with LLM-assisted Pre-training." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28566–74. https://doi.org/10.1609/aaai.v39i27.35080.

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In recent years, ransomware has emerged as a formidable data security threat, causing significant data privacy breaches that inflict substantial financial, reputational, and operational damages on society. Many studies employ dynamic feature analysis for ransomware detection. However, these methods utilize neither the internal semantic information (semantic information inherent in the features), nor external semantics (the wealth of existing knowledge and expert experience with regard to ransomware detection). Moreover, conventional methods rely on training data from known ransomware families,

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Odego, John Kennedy Otieno, Kennedy Odhiambo Ogada, and Dennis Mugambi Kaburu. "An Ontology-Based Approach for Zero-Day Information Security Threat Management." International Journal of Information Security and Privacy 19, no. 1 (2025): 1–21. https://doi.org/10.4018/ijisp.384606.

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Zero Day security threats are diverse and manifest in many forms. Despite the growing number of zero day attacks, very little information about the kind of threat and how to defend against the threats is known by information security professionals. Signature based techniques and statistical based techniques have been seen to be less effective in handling Zero-day security threats (ZDST) since they require a new threat signature and threat profile to be learnt each time, meaning new signatures and profiles cannot be detected and behavior-based approaches have always resulted in many false posit

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Getman, Aleksandr Igorevich, Maxim Nikolaevich Goryunov, Andrey Georgievich Matskevich, and Dmitry Aleksandrovich Rybolovlev. "A Comparison of a Machine Learning-Based Intrusion Detection System and Signature-Based Systems." Proceedings of the Institute for System Programming of the RAS 34, no. 5 (2022): 111–26. http://dx.doi.org/10.15514/ispras-2022-34(5)-7.

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The paper discusses the approach to the comparison of intrusion detection systems (IDS) that is based on several independent scenarios and comprehensive testing. This approach enabled to identify the advantages and disadvantages of the IDS based on machine learning methods (ML IDS), to identify the conditions under which ML IDS is able to outperform signature-based systems in terms of detection quality, to assess the practical applicability of ML IDS. The developed scenarios enabled to model the realization of both known attacks and a zero-day exploit. The conclusion is made about the advantag

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Bhaya, Wesam S., and Mustafa A. Ali. "Review on Malware and Malware Detection ‎Using Data Mining Techniques." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 25, no. 5 (2017): 1585–601. http://dx.doi.org/10.29196/jub.v25i5.104.

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Malicious software is any type of software or codes which hooks some: private information, data from the computer system, computer operations or(and) merely just to do malicious goals of the author on the computer system, without permission of the computer users. (The short abbreviation of malicious software is Malware). However, the detection of malware has become one of biggest issues in the computer security field because of the current communication infrastructures are vulnerable to penetration from many types of malware infection strategies and attacks. Moreover, malwares are variant and

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Khraisat, Gondal, Vamplew, Kamruzzaman, and Alazab. "A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks." Electronics 8, no. 11 (2019): 1210. http://dx.doi.org/10.3390/electronics8111210.

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The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Cl

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Agrawal, K., S. Chittineni, P.V.G. D. Prasad Reddy, and K. Subhadra. "Intrusion Detection for CyberSecurity: A Comparative Study of Machine Learning, Deep Learning and Transfer Learning Methods." International Journal of Microsystems and IoT 2, no. 7 (2024): 1050–58. https://doi.org/10.5281/zenodo.13332556.

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Abstract—With the increasing frequency and sophistication of cyber-attacks, intrusion detection has become a critical cybersecurity component to ensure the resilience and trustworthiness of modern digital systems and networks. Several machine learning and deep learning algorithms have been used.  However, there is limited data on the comparative efficacy of these systems. We analyzed the usage of predefined machine learning algorithms (Logistic Regression, Decision Trees, Random Forest, Gaussian Naïve Bayes, Linear Support Vector Machine, and Gradient Boosting) and neural netwo

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Marison, Sihol, Silvanus Silvanus, and Rudi Rusdiah. "AI-BASED ALGORITHMS FOR NETWORK SECURITY: TRENDS, PER-FORMANCE, AND CHALLENGES." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 11, no. 2 (2025): 329–36. https://doi.org/10.33330/jurteksi.v11i2.3699.

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Abstract: The advancement of network security faces growing challenges as cyberattacks become more sophisticated. Traditional rule-based systems struggle with zero-day attacks and obfuscation techniques. This study examines the development trends of AI-based algo-rithms, particularly machine learning and deep learning, in threat detection. A literature review evaluates AI-driven approaches, including support vector machines, random for-est, deep neural networks, convolutional neural networks, and reinforcement learning. Findings show that AI enhances detection accuracy, adaptability, and reduc

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Rahman, Rizwan Ur, and Deepak Singh Tomar. "Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set." International Journal of Digital Crime and Forensics 13, no. 6 (2021): 1–27. http://dx.doi.org/10.4018/ijdcf.20211101.oa6.

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Web bots are destructive programs that automatically fill the web form and steal the data from web sites. According to numerous web bot traffic reports, web bots traffic comprises of more than fifty percent of the total web traffic. An effective guard against the stealing of the data from web sites and automated web form is to identify and confirm the human user presence on web sites. In this paper, an efficient k-Nearest Neighbor algorithm using hierarchical clustering for web bot detection is proposed. Proposed technique exploits a novel taxonomy of web bot features known as Biostatistics Fe

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P. Arul, Et al. "Predicting the Attacks in IoT Devices using DP Algorithm." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 164–68. http://dx.doi.org/10.17762/ijritcc.v11i11.9133.

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The fundamental goal of this study is to predict cyber-attacks before they occur and to protect the network. Most existing attack detection algorithms cannot identify zero day attacks because they lack previously known data patterns to predict the threat, which is one of the biggest issues in the existing approaches. This research work offers a novel prediction method based on Gaussian regression that identifies cyber-attacks utilizing a unique dual data pattern categorization technique with no false positives. To improve the accuracy of the prediction and to reduce the prediction time consump

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Dr.R.Venkatesh, Kavitha S, Dr Uma Maheswari N,. "Network Anomaly Detection for NSL-KDD Dataset Using Deep Learning." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 821–27. http://dx.doi.org/10.17762/itii.v9i2.419.

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Deep learning based intrusion detection cyber security methods gained increased popularity. The essential element to provide protection to the ICT infrastructure is the intrusion detection systems (IDSs). Intelligent solutions are necessary to control the complexity and increase in the new attack types. The intelligent system (DL/ML) has been widely used with its benefits to effectively deal with complex and great dimensional data. The IDS has various attack types like known, unknown, zero day attacks are attractive to and detected using unsupervised machine learning techniques. A novel method

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Othman, Trifa S., and Saman M. Abdullah. "An Intelligent Intrusion Detection System for Internet of Things Attack Detection and Identification Using Machine Learning." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 11, no. 1 (2023): 126–37. http://dx.doi.org/10.14500/aro.11124.

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The usability and scalability of Internet of things (IoT) technology are expanding in such a way that they facilitate human living standards. However, they increase the vulnerabilities and attack vectors over IoT networks as well. Thus, more security challenges could be expected and encountered, and more security services and solutions should be provided. Although many security techniques propose and promise good solutions for that intrusion detection systems IDSs still considered the best. Many works proposed machine learning (ML)-based IDSs for IoT attack detection and classification. Nevert

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Lakhdhar, Yosra, Slim Rekhis, and Noureddine Boudriga. "A Context-based Defense Model for Assessing Cyber Systems' Ability To Defend Against Known And Unknown Attack Scenarios." JUCS - Journal of Universal Computer Science 25, no. (9) (2019): 1066–88. https://doi.org/10.3217/jucs-025-09-1066.

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Presently, attackers succeed to damage different cyber systems no matter whether cyber security solutions are implemented or not. This fact can be explained by the information insufficiency regarding the attack environment and the deployed solutions, in addition to the predominant use of pre-built cyber attack databases, making the supervised system incapable of defending itself against zero-day attacks. We present in this paper an enhanced cyber defense model to assess the effectiveness of the deployed security solutions to defend against potential generated attack scenarios under various con

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Dange, Varsha, Soham Phadke, Tilak Solunke, Sidhesh Marne, Snehal Suryawanshi, and Om Surase. "Weighted Multiclass Intrusion Detection System." ITM Web of Conferences 57 (2023): 01009. http://dx.doi.org/10.1051/itmconf/20235701009.

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Attackers are continuously coming up with new attack strategies since cyber security is a field that is continually changing. As a result, it’s important to update and enhance the system frequently to ensure its efficiency against fresh threats. Unauthorised entry, usage, or manipulation of a computer system or network by a person or programme is referred to as an intrusion. There are numerous ways for an incursion to happen, including using software flaws, phishing scams, or social engineering techniques. A realistic solution to handle the risks brought on by the interconnectedness and intero

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BOBROVNIKOVA, KIRA, MARIIA KAPUSTIAN, and DMYTRO DENYSIUK. "RESEARCH OF MACHINE LEARNING BASED METHODS FOR CYBERATTACKS DETECTION IN THE INTERNET OF THINGS INFRASTRUCTURE." Computer systems and information technologies, no. 3 (April 14, 2022): 110–15. http://dx.doi.org/10.31891/csit-2021-5-15.

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The growing demand for IoT devices is accelerating the pace of their production. In an effort to accelerate the launch of a new device and reduce its cost, manufacturers often neglect to comply with cybersecurity requirements for these devices. The lack of security updates and transparency regarding the security status of IoT devices, as well as unsafe deployment on the Internet, makes IoT devices the target of cybercrime attacks. Quarterly reports from cybersecurity companies show a low level of security of the Internet of Things infrastructure. Considering the widespread use of IoT devices n

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Khraisat, Ansam, Iqbal Gondal, Peter Vamplew, Joarder Kamruzzaman, and Ammar Alazab. "Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine." Electronics 9, no. 1 (2020): 173. http://dx.doi.org/10.3390/electronics9010173.

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Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines

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M.R., Amal, and Venkadesh P. "Review of Cyber Attack Detection: Honeypot System." Webology 19, no. 1 (2022): 5497–514. http://dx.doi.org/10.14704/web/v19i1/web19370.

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The number of connected devices in the network is growing day by day, and as the number of linked devices grows, so will the number of cyberattacks. All devices connected to the Internet has become a target of cyberattacks as network attack methods have developed. As a result, the security of network data cannot be neglected. To handle the future threats in this way, we employ honeypots, which are conceptual framework traps designed to block unauthorized access to both PCs and data. Every day, a large number of people access the internet throughout the world. Honeypot, also known as Intrusion

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