Results 41 to 50 of about 1,405 (158)

Mal-Detect: An intelligent visualization approach for malware detection

Journal of King Saud University: Computer and Information Sciences, 2022
Recent outbreaks of pandemics have deepened the adoption and use of IT-based systems. This development has led to an exponential increase in cyberattacks caused by malware.
Olorunjube James Falana, Adesina Simon Sodiya, Saidat Adebukola Onashoga, Biodun Surajudeen Badmus   +3 more
doaj   +1 more source

MalGrid: Visualization of Binary Features in Large Malware Corpora

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), 2022
The number of malware is constantly on the rise. Though most new malware are modifications of existing ones, their sheer number is quite overwhelming. In this paper, we present a novel system to visualize and map millions of malware to points in a 2-dimensional (2D) spatial grid. This enables visualizing relationships within large malware datasets that
Mohammed, Tajuddin Manhar, Nataraj, Lakshmanan, Chikkagoudar, Satish, Chandrasekaran, Shivkumar, Manjunath, B. S.   +4 more
openaire   +2 more sources

Mission Aware Cyber‐Physical Security

Systems Engineering, Volume 29, Issue 2, Page 354-367, March 2026.
ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis, Bryan Carter, Cody H. Fleming, Carl R. Elks   +3 more
wiley   +1 more source

Securing the Unseen: A Comprehensive Exploration Review of AI‐Powered Models for Zero‐Day Attack Detection

Expert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam, Nuruzzaman Faruqui, Akm Azad, Mohammad Ali Moni   +3 more
wiley   +1 more source

Android Malware Familial Classification Based on DEX File Section Features

IEEE Access, 2020
The rapid proliferation of Android malware is challenging the classification of the Android malware family. The traditional static method for classification is easily affected by the confusion and reinforcement, while the dynamic method is expensive in ...
Yong Fang, Yangchen Gao, Fan Jing, Lei Zhang   +3 more
doaj   +1 more source

MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network.

International Journal of Interactive Multimedia and Artificial Intelligence, 2022
Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical ...
Mahendra Deore, Uday Kulkarni
doaj   +1 more source

A Machine Learning Framework for Detecting and Preventing Cyber‐Attacks in Industrial Cyber‐Physical Systems

Engineering Reports, Volume 8, Issue 1, January 2026.
Proposed cyber physical system security framework. ABSTRACT The increasing adoption of cyber‐physical systems (CPS) in Industry 4.0 has heightened vulnerability to cyber threats. This study proposes a machine learning–based intrusion detection framework, DBID‐Net, to effectively identify and prevent attacks in CPS environments. The framework integrates
Anurag Sinha, Rejuwan Shamim, Mihika Mahendra, Trupti Mohota, G. Madhukar Rao, Mohammad Nadeem Ahmed, Mohammad Rashid Hussain, Satyam Solanki, Tanish Ranjan, Bhaskar, Mini Kumari, Karan Dixit, Vyom Modi, Syed Immamul Ansarullah, Mohd Asif Shah   +14 more
wiley   +1 more source

Transfer Learning for Image-Based Malware Detection for IoT

Sensors, 2023
The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household.
Pratyush Panda, Om Kumar C U, Suguna Marappan, Suresh Ma, Manimurugan S, Deeksha Veesani Nandi   +5 more
doaj   +1 more source

Explainable AI With Imbalanced Learning Strategies for Blockchain Transaction Fraud Detection

Engineering Reports, Volume 8, Issue 1, January 2026.
Research methodology pipeline for blockchain fraud detection. ABSTRACT Blockchain networks now support billions of dollars in daily transactions, making reliable and transparent fraud detection essential for maintaining user trust and financial stability.
Ahmed Abbas Jasim Al‐Hchaimi, M. A. Khalifa, Walid El‐Shafai   +2 more
wiley   +1 more source

Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification

Engineering Reports, Volume 8, Issue 1, January 2026.
Overview of the proposed RF–ViT ensemble for multi‐class malware classification. Textual (BoW/byte‐frequency) and visual representations are combined via a product rule, achieving improved accuracy and robustness over individual models. ABSTRACT Malware remains a significant concern for modern digital systems, increasing the need for reliable and ...
Fadi Makarem, Rida El Chall, Abed Ellatif Samhat, Khouloud Samrouth, Nader Bakir   +4 more
wiley   +1 more source