Results 61 to 70 of about 2,699 (152)

TRIMODAL FUSION FRAMEWORK FOR LAYER 2 NETWORK FORENSICS: INTEGRATING CNN, TRANSFORMER, AND BERT EMBEDDINGS FOR REAL-TIME MALWARE DETECTION IN RAW ETHERNET TRAFFIC.

EPRA International Journal of Research & Development (IJRD)
This paper presents a multimodal deep learning–based malware detection framework for Layer 2 traffic. The approach integrates Convolutional Neural Networks (CNNs) for spatial packet analysis, Transformers for temporal flow modeling, and BERT embeddings for semantic threat intelligence.
openaire   +1 more source

Peer Review #2 of "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) (v0.2)"

, 2021
  +5 more sources

A BERT and PSO framework for Android malware detection using real permissions and API calls

Discover Computing
The rapid expansion of mobile connectivity and the global reliance on smartphones have positioned Android as the leading platform, driven by its affordability and open source framework.
Abhinandan Banik, Jyoti Prakash Singh
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

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

OPTISTACK: A Hybrid Ensemble Learning and XAI-Based Approach for Malware Detection in Compressed Files

IEEE Access
The increasing reliance on compressed file formats for data storage and transmission has made them attractive vectors for malware propagation, as their structural complexity enables evasion of conventional detection mechanisms.
Khaled Mahmud Sujon, Rohayanti Binti Hassan, M. Abdullah-Al-Wadud, Jia Uddin   +3 more
doaj   +1 more source

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

Future Internet
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran, Annalisa Appice, Donato Malerba   +2 more
doaj   +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

RNN-based detection of IoT malware using diverse feature engineering methods. [PDF]

Sci Rep
Abd-Ellah MK, Alsayed NA, Elkomy OM, El-Hady WM.   +3 more
europepmc   +1 more source