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7 articles for “cyberattack detection”
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Cyberattack Detection and Prevention Using Empowering AI Tools
Abstract: With more organizations entering the digital transformation sphere, the opportunities and risks in cyberspace have increased and gone up in levels of sophistication and occurrence. Many of these developments are attributed to the limits of existing cyber security solutions where addressing new threats requires advanced detection technologies and techniques. Cyber threats gained a new meaning and dimension with artificial intelligence (AI) coming into play in ways that supplement security systems …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 1–7 Read article
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Enhancing LAN Security Using Machine Learning
Abstract: The modern Local Area Network (LAN) is a critical component of any organization's infrastructure, facilitating communication, resource sharing, and access to the wider internet. However, this connectivity also brings inherent security risks. Traditional security measures, relying on signature-based detection and rule-based systems, are increasingly struggling to keep pace with the evolving sophistication of cyberattacks. This is where Machine Learning (ML) offers a powerful alternative, enabling proactive threat detection and enhanced …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 07–16 Read article
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AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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Ramifications of Artificial Intelligence and Cyber Security
Abstract: Artificial intelligence (AI) has pros and cons for cyber security: AI can improve network security, anti-malware, and fraud detection. AI can simulate cyberattacks, automate responses, and analyse enormous databases. AI-powered phishing and deepfakes are cyber risks. AI can potentially be attacked and become a liability for corporations. AI has transformed cyber security, bringing both new opportunities and challenges. AI-powered tools discover abnormalities faster, automate threat responses, and improve threat detection …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 1, 2025 · pp. 40–45 Read article
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DDoS Detection Using Cascade Correlation for Improving Network Resources in Cloud Environment
Abstract: Intrusion detection is critical for protecting network security from emerging cyber threats. This study describes a unique intrusion detection system (IDS) based on the Random Forest algorithm. Random Forests are used as an effective classifier to identify patterns linked with malevolent behaviour. This technique uses Random Forests to improve the accuracy and efficiency of intrusion detection systems. The suggested methodology's value is shown by its performance on the benchmark KDD …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 17–22 Read article
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A Security Investigation Survey of Ransomware Detection and Avoidance Strategies for IoT Networks
Abstract: The internet of things (IoT) refers to the interconnection of a large number of distinct physical objects, which in turn makes possible a variety of services and applications. Because the IoT sector is developing at such a rapid rate, ensuring its safety ought to be a high concern. At this time, ransomware attacks constitute the biggest danger to IoT posed by cyberattacks. Ransomware is software that blocks access to or …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 2, 2023 · pp. 14–23 Read article
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Securing Web Applications: A Machine Learning Approach for SQL Injection Threats
Abstract: The rapid evolution and widespread adoption of the internet have significantly transformed the world, leading to an increased number of cyberattacks. Cybersecurity has become one of the most critical challenges for society, incurring substantial financial losses annually. This research focuses on SQL injection attacks, the specific threat to web applications, aiming to detect malicious queries designed to exploit vulnerabilities and access sensitive data. In recent years, the frequency of SQLi …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 16–22 Read article