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16 articles for “Cyberattack”
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Research ArticleOpen accessIn the keywordsA Hybrid Grey Wolf Optimizer and Deep Transfer Learning-Based Intrusion Detection System for IoTKapil Shrivastava, Manish Tiwari, Prasun ChakrabartiThe rapid proliferation of Internet of Things (IoT) devices has driven significant advancements in connectivity and automation, but it has also introduced substantial cybersecurity risks, including intrusions and cyberattacks. To address these challenges, this study proposes a hybrid intrusion detection framework that combines advanced optimization techniques with ensemble deep learning to enhance detection accuracy and computational efficiency. The proposed framework … …Journal of Computer Science, Vol. 22, Issue 2 · 17 Feb 2026 · 1,506 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Optimized Multi-Layer Perceptron Framework for Detecting and Classifying IoT AttacksSanchit Vashisht, Shalli RaniThe Internet of Things (IoT) is revolutionizing industries by connecting billions of smart devices, enabling automation and information exchange. The expansion of IoT ecosystems has simultaneously increased the surface area for cyberattacks. These environments are particularly vulnerable to a wide range of threats, such as Distributed Denial-of-Service (DDoS), poisoning, brute-force SSH intrusions, and various network reconnaissance techniques. The dynamic nature … …Journal of Computer Science, Vol. 21, Issue 12 · 11 Feb 2026 · 1,333 accessesView PDFCite
Research ArticleOpen accessIn the keywordsDefense Methodologies against Advanced Persistent ThreatsPedro Ramos Brandao, Viktoriya LimonovaAdvanced Persistent Threats (APTs) are complex and sophisticated attacks. Their main aim is to obtain unauthorized access to financial and corporate data. As such, it is essential to devise and improve monitoring and defense strategies. This study is intended to analyze various defense methodologies proposed by numerous experts to find a possible solution. …American Journal of Applied Sciences, Vol. 18, Issue 1 · 8 Oct 2021 · 8,608 accessesView PDFCite
Research ArticleOpen accessIn the abstractMulti-Source Cyber Intrusion Detection Using Ensemble Machine LearningTaskeen Ali Khan, Sara Abbas, Biswaranjan Senapati et al.The increased usage of digital technologies across businesses has led to an increase in cybercrime. It is difficult for existing intrusion detection systems to detect highly skilled hacking attempts; however, machine learning has been suggested as a way around these drawbacks. The purpose of this study is to evaluate how well various machine learning algorithms identify and stop cyberattacks in … …Journal of Computer Science, Vol. 21, Issue 1 · 14 Dec 2024 · 5,696 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing SQL Injection Detection: A Machine Learning Approach Using Network Flow DataP. Vinoth, K. Muthamil Sudar, S. MuthukumarBackground: SQL injection is one of the cyberattacks which occurs in web application vulnerabilities. It targets the poor input validation and enables the attackers to inject malicious queries into the database fields in any application. It will grant access to unauthorized users and data modification, or they will take control over the complete database. To prevent this, there are several … …Journal of Computer Science, Vol. 22, Issue 8 · 18 Aug 2026 · 1,051 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnsemble Learning-Based Hybrid Explainable Intrusion Detection System With SMOTE for Enhanced Detection and Interpretability in CybersecurityMohammad Subhi Al-Batah, Taqieddin AldrousWith cyberattacks becoming increasingly sophisticated, there is a growing need for Intrusion Detection Systems (IDS) that are both accurate and interpretable. This study introduces a hybrid IDS framework that integrates Random Forest (RF) and Light Gradient Boosting Machine (LGBM) classifiers with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. To enhance transparency and trust, the system also incorporates … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,277 accessesView PDFCite
Research ArticleOpen accessIn the abstractIoT Anomaly Detection Using Picture Fuzzy Clustering ApproachFehmin Nadira Laskar, Vijo Arul Selvi M., Fokrul Alom Mazarbhuiya et al.Improving the effectiveness of security systems without slowing them down is a major challenge in cybersecurity. Many methods have been explored for detecting anomalous behaviour in network data, with fuzzy set-based approaches standing out for their potential. The Internet of Things (IoT) consists of many connected devices that constantly generate large amounts of data and perform tasks in real time. … …Journal of Computer Science, Vol. 22, Issue 3 · 6 Mar 2026 · 1,515 accessesView PDFCite
Review ArticleOpen accessIn the abstractDeepfake Technology: A Comprehensive Review of Trends, Applications, Ethical Concerns, and ChallengesBattula Thirumaleshwari Devi, Rajkumar RajasekaranThe digital age has fundamentally transformed how information is created and disseminated, raising critical concerns about the authenticity and trustworthiness of online content. Recent advances in Artificial Intelligence (AI), particularly deep learning, have given rise to deepfakes: highly realistic synthetic media generated by manipulating or replacing faces, voices, and actions in videos. While deepfake technology offers innovative applications across various … …Journal of Computer Science, Vol. 22, Issue 1 · 17 Feb 2026 · 4,861 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdaptive Cybersecurity: Dynamically Retrainable Firewalls for Real-Time Network ProtectionSina AhmadiThe growing complexity of cyberattacks has necessitated the evolution of firewall technologies from static models to adaptive, machine learning-driven systems. This research introduces “Dynamically Retrainable Firewalls”, which respond to emerging threats in real-time. Unlike traditional firewalls that rely on static rules to inspect traffic, these advanced systems leverage machine learning algorithms to analyze network traffic patterns dynamically and identify threats. … …Journal of Computer Science, Vol. 21, Issue 9 · 7 Nov 2025 · 2,558 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Succinct Review on Identification of Network Anomalies and Protection in Cyber-Physical SystemsShalini Kumari, Chander Prabha, Prakash Srivastava et al.Advanced cyberattacks outperform traditional threat detection methods in the rapidly expanding cybersecurity field. Advanced Machine Learning (ML) algorithms can detect network traffic and system problems using AI-based Anomaly Detection (AD) for cybersecurity in real-time. Signature-based systems may overlook new and subtle threats. This paper examines Artificial Intelligence (AI) driven AD systems' design, methodology, and efficacy. The process includes data preprocessing … …Journal of Computer Science, Vol. 21, Issue 6 · 29 May 2025 · 5,234 accessesView PDFCite