Results 81 to 90 of about 707,343 (186)

Android Malware Detection Systems Review

Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2017
With the smartphones entering our lives, the number of smartphones continues to increase day by day. The reason why smartphones are in so demand is that people can easily do what they want.
Ömer Kiraz, İbrahim Alper Doğru
doaj  

Embedding-Driven Synthetic Malware Generation with Autoencoders and Cluster-Tangent Diffusion

Applied Sciences
Malware has become increasingly sophisticated over the years, with zero-day attacks emerging at an alarming pace. Effective detection and analysis demand real malware samples, which are expensive and skill-dependent to extract.
Gunnika Kapoor, Sathvika Nadipalli, Fabio Di Troia   +2 more
doaj   +1 more source

An Intelligent Spam Detection Model Based on Artificial Immune System

Information, 2019
Spam emails, also known as non-self, are unsolicited commercial or malicious emails, sent to affect either a single individual or a corporation or a group of people. Besides advertising, these may contain links to phishing or malware hosting websites set
Abdul Jabbar Saleh, Asif Karim, Bharanidharan Shanmugam, Sami Azam, Krishnan Kannoorpatti, Mirjam Jonkman, Friso De Boer   +6 more
doaj   +1 more source

Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized Smoothing

IEEE Access
Deep learning-based malware detectors have been shown to be susceptible to adversarial malware examples, i.e. malware examples that have been deliberately manipulated in order to avoid detection.
Daniel Gibert, Giulio Zizzo, Quan Le, Jordi Planes   +3 more
doaj   +1 more source

Leveraging Large Language Models for Multi-Domain Malware and Vulnerability Detection

Indonesian Journal of Computer Science
This study presents the application of deep learning methodologies, particularly leveraging GPT-2, to enhance various aspects of cybersecurity, including source code vulnerability detection, malware detection, and mobile malware security.
Attila Magyar
semanticscholar   +1 more source

PDF Malware Detection: Toward Machine Learning Modeling With Explainability Analysis

IEEE Access
The Portable Document Format (PDF) is one of the most widely used file types, thus fraudsters insert harmful code into victims’ PDF documents to compromise their equipment.
G. M. S. Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker   +3 more
semanticscholar   +1 more source

Binary Code Analysis for Cybersecurity: A Systematic Review of Forensic Techniques in Vulnerability Detection and Anti-Evasion Strategies

IEEE Access
Binary code analysis is essential in modern cybersecurity, examining compiled program outputs to identify vulnerabilities, detect malware, and ensure software security compliance.
Haseeb Javed, Farman Ali, Babar Shah, Daehan Kwak   +3 more
doaj   +1 more source

Empowering Security with Machine Learning for Ransomware and Malware Detection

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Due to the increase and frequency of network attacks, network security protection needs to be established[1]. The project focuses on using machine learning technology to detect ransomware and malware and aims to improve computer security.
Panuganti Ravi1,
semanticscholar   +1 more source

Lightweight DDoS Attack Detection Using Bayesian Space-Time Correlation

IEEE Access
DDoS attacks are still one of the primary sources of problems on the Internet and continue to cause significant financial losses for organizations. To mitigate their impact, detection should preferably occur close to the attack origin, e.g., at home ...
Gabriel Mendonca, Rosa M. M. Leao, Edmundo De Souza E. Silva, Don Towsley   +3 more
doaj   +1 more source

Microarchitectural Malware Detection via Translation Lookaside Buffer (TLB) Events

Journal of Cybersecurity and Privacy
Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification.
Cristian Agredo, Daniel F. Koranek, Christine M. Schubert Kabban, Jose A. Gutierrez del Arroyo, Scott R. Graham   +4 more
doaj   +1 more source