Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu Journal of Informatics and Interactive Technology (JIITE)
Haroon Arif
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Future Horizons: AI-Enhanced Threat Detection in Cloud Environments: Unveiling Opportunities for Research Haroon Arif; Aashesh Kumar; Muhammad Fahad; Hafiz Khawar Hussain
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 1 (2024): International Journal of Multidisciplinary Sciences and Arts, Article January 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v2i2.3452

Abstract

In this extensive and comprehensive review paper, we delve into the dynamic landscape of artificial intelligence (AI)-enhanced threat detection within cloud environments. The evolution of this field, from traditional methodologies to the seamless integration of AI, is meticulously explored, providing a nuanced understanding of the transformative potential of AI in bolstering cyber security measures. The exploration encompasses a myriad of crucial aspects, offering readers a holistic view of the subject. The revolutionary impact of AI is scrutinized, emphasizing its role in reshaping the conventional paradigms of threat detection and response. The paper meticulously addresses current challenges in cloud security, providing insights into the multifaceted nature of contemporary threats and how AI serves as a robust defense mechanism. As we navigate through the intricacies of this field, the review paper sheds light on ongoing research prospects, presenting a roadmap for future endeavors. Real-world case studies are examined to illustrate the practical applications of AI-enhanced threat detection, offering valuable lessons and perspectives for decision-makers, researchers, and practitioners in the realm of cyber security. Ethical considerations are given due attention, as the integration of AI in threat detection raises important questions surrounding privacy, bias, and accountability. By analyzing current trajectories and emerging technologies, the article provides readers with a forward-looking perspective, helping them anticipate the evolving landscape of cyber security. In addition to exploring the technological facets, the paper emphasizes the importance of a collaborative approach and ongoing adaptation. The interconnected nature of threats in the digital realm necessitates a collective effort from industry experts, researchers, and policymakers. The review paper advocates for a holistic strategy that integrates AI technologies with human expertise to create a resilient defense against the ever-evolving landscape of cyber threats.
Navigating the Uncharted Waters: Exploring Challenges and Opportunities in Block chain-Enabled Cloud Computing for Future Research Aashesh Kumar; Muhammad Fahad; Haroon Arif; Hafiz Khawar Hussain
BULLET : Jurnal Multidisiplin Ilmu Vol. 2 No. 6 (2023): BULLET : Jurnal Multidisiplin Ilmu (INPRESS)
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This article offers a thorough examination of the prospects, challenges, and future directions of block chain-enabled cloud computing, navigating a field that is still relatively new. The study explores the intricacies of the integration, from laying forth fundamental ideas to analyzing the present situation. Case studies shed light on effective implementations in the fields of finance, healthcare, supply chains, and other areas, offering insightful guidance for future initiatives. Examined are the transformative potential, security consequences, and regulatory considerations, highlighting the importance of sound frameworks and responsible development. Important factors including cooperation, sustainability, privacy, user education, regulatory clarity, and ongoing research are outlined in the roadmap for the future. In summary, the paper predicts a revolutionary digital future where block chain technology and cloud computing combine to foster creativity, cooperation, and resilience.
Co-Authors Aamir Ali Aamir Raza Aashesh Kumar Ali, Misbah Hafiz Khawar Hussain Malik Arslan Akram Muhammad Fahad