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All Journal BULLET : Jurnal Multidisiplin Ilmu Journal of Informatics and Interactive Technology (JIITE)
Haroon Arif
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Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Advancements in Detection and Mitigation: Fortifying Against APTs - A Comprehensive Review Aashesh Kumar; Muhammad Fahad; Haroon Arif; Hafiz Khawar Hussain
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 1 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

Organizations' cyber security posture is severely challenged by Advanced Persistent Threats (APTs), necessitating a multifaceted defense strategy. Traditional methods, machine learning, artificial intelligence (AI), behavioral analytics, real-time monitoring, incident response, collaborative defense mechanisms, endpoint security enhancements, network segmentation and access control, encryption, data protection, and user training and awareness are just a few of the strategies and advancements in APT detection and mitigation that are examined in this review article. Every tactic is thoroughly reviewed, emphasizing its value in thwarting APT attacks and offering best practices for execution. By utilizing these cutting-edge methods and encouraging cooperation amongst enterprises, it is feasible to improve defenses against APTs and lessen the likelihood that they will affect vital assets and data.
Enhancing IOT Security: A review of Machine Learning-Driven Approaches to Cyber Threat Detection: Enhancing IOT Security: A review of Machine Learning-Driven Approaches to Cyber Threat Detection Ali, Misbah; Aamir Raza; Malik Arslan Akram; Haroon Arif; Aamir Ali
Journal of Informatics and Interactive Technology Vol. 2 No. 1 (2025): April
Publisher : ACSIT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63547/jiite.v2i1.64

Abstract

Internet Of Things (IOT) is rapidly adopted and implemented across various industries. The fast growth of IOT devices poses a risk, as these devices are ideal targets to be breached and exploited. However, given the heterogeneous nature and resource limitations of IOT networks, the traditional security mechanisms often fail to provide the required security. This study investigates recent IOT security breaches and showcases vulnerabilities exploited by attackers, as well as their impact on consumer, industrial, and healthcare IOT systems. The proposed solutions through ML and DL-driven security are summarized for adaptive threat detection, anomaly-based intrusion prevention, and intelligent risk mitigation. We also analyzed different approaches based on ML and DL to identify and prevent cyber-attacks as an effective solution. These ML and DL – based research papers have been reviewed within the IEEE repository and the publications span from 2020 to 2024, ensuring current literature on IOT security. The results highlight that security models based on ML and DL techniques improve resilience against IOT by allowing real-time detection of attacks, reducing the volume of false positives, and adapting to new threats. Furthermore, this work identifies the existing barriers to the adoption of ML/DL technologies for IOT security and emphasizes the potential areas for future research that may solidify the overall security framework for IOT ecosystems.
Co-Authors Aamir Ali Aamir Raza Aashesh Kumar Ali, Misbah Hafiz Khawar Hussain Malik Arslan Akram Muhammad Fahad