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All Journal Mikailalsys Journal of Advanced Engineering International International Journal of Education, Management, and Technology
Onyeanisi, Onyinyechukwu Prisca
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Intelligent Incident Response Systems Using Machine Learning Joseph, Jennifer E; Aleke, Ngozi Tracy; Onyeanisi, Onyinyechukwu Prisca
Mikailalsys Journal of Advanced Engineering International Vol 2 No 1 (2025): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v2i1.4540

Abstract

The increasing complexity and volume of cyber threats have placed significant pressure on traditional incident response (IR) systems, necessitating the adoption of more advanced technologies to detect, analyze, and mitigate attacks efficiently. One such technology is machine learning (ML), which offers the potential to transform incident response by automating threat detection, prioritizing incidents, and dynamically adjusting responses based on evolving attack patterns. This paper explores the integration of machine learning into intelligent incident response systems, focusing on its applications, benefits, and challenges. Through an in-depth examination of machine learning techniques—such as supervised learning, unsupervised learning, deep learning, and reinforcement learning—we highlight how these models can enhance various stages of incident response, including detection, triage, automated remediation, and post-incident analysis. Additionally, we discuss case studies showcasing the effectiveness of ML in real-world IR scenarios and identify key challenges, such as data quality, adversarial attacks, and model interpretability. The paper also proposes potential future directions, including hybrid ML models, human-in-the-loop systems, and advances in explainable AI, to further improve the reliability and transparency of ML-driven IR systems. Ultimately, this research aims to provide a comprehensive understanding of how machine learning can augment incident response efforts and enhance cybersecurity resilience in the face of increasingly sophisticated threats.
Deep Learning Based Intrusion Detection System for Network Security in IoT System Joseph, Jennifer E; Aleke, Ngozi Tracy; Onyeanisi, Onyinyechukwu Prisca
International Journal of Education, Management, and Technology Vol 3 No 1 (2025): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v3i1.4539

Abstract

The Internet of Things (IoT) has grown rapidly, leading to unparalleled connectivity and vast amounts of data. Anomaly detection plays a crucial role in identifying unusual behavior that deviates from the system's normal operation, enabling the swift detection and resolution of these anomalies. The integration of artificial intelligence (AI) with IoT significantly improves the effectiveness of anomaly detection, enhancing the performance, dependability, and security of IoT systems. AI-powered anomaly detection methods can recognize a wide array of threats within IoT environments, such as brute force attacks, buffer overflows, injection attacks, replay attacks, Distributed Denial of Service (DDoS) attacks, SQL injection, and backdoor threats. Intelligent Intrusion Detection Systems (IDS) are essential for IoT devices, as they help monitor networks for intrusions or anomalies. With the increasing adoption of IoT across various industries and its extensive attack surface, it offers more opportunities for malicious actors to exploit vulnerabilities. This paper reviews existing literature on anomaly detection in IoT systems using machine learning and deep learning approaches. It discusses the challenges associated with detecting intrusions and anomalies in IoT environments, emphasizing the rise in attacks. Recent advancements in machine learning and deep learning techniques for anomaly detection in IoT networks are examined, and the paper concludes that there is a need for further enhancement of these systems through the use of diverse datasets, real-time testing, and scalability improvements.
Co-Authors Aleke, Ngozi Tracy Joseph, Jennifer E