Results 101 to 110 of about 3,386 (193)

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

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

DCEL: Classifier Fusion Model for Android Malware Detection

The rapid growth of mobile applications, the popularity of the Android system and its openness have attracted many hackers and even criminals, who are creating lots of Android malware.
Wang, X, Zhao, J, Xu, X, Jiang, S
core   +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

Machine learning classification for advanced malware detection [PDF]

This introductory document discusses topics related to malware detection via the application of machine learning algorithms. It is intended as a supplement to the published work submitted (a complete list of which can be found in Table 1) and outlines ...
Di Troia, Fabio
core  

EDSSR: a secure and power-aware opportunistic routing scheme for WSNs

Scientific Reports
Motivated by the pivotal role of routing in Wireless Sensor Networks (WSNs) and the prevalent security vulnerabilities arising from existing protocols, this research tackles the inherent challenges of securing WSNs.
Ruili Yang, Manoj A. Patil, Potu Narayana, D. Jayaram, K. Keerthi, N. SudhakarYadav, Premkumar Chithaluru, Sunil Kumar, Diaa Salama Abd Elminaam, Deema Mohammed Alsekait   +9 more
doaj   +1 more source

PromptSAM+: Malware Detection based on Prompt Segment Anything Model

Machine learning and deep learning (ML/DL) have been extensively applied in malware detection, and some existing methods demonstrate robust performance.
Liu, Yichen, Li, Ning, Wei, Xingyuan, Sun, Degang, Li, Ce, Wang, Yan   +5 more
core  

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

Malware Detection Using RNA Encoding and Convolutional Neural Networks on the Malicious Network Dataset. [PDF]

F1000Res
Fitian Rashid O, Ali Abd S, Al-Shahwani H.   +2 more
europepmc   +1 more source

OmBNNet: a resource-efficient FPGA-based obfuscated malware detection method using binarized neural network. [PDF]

Sci Rep
Das K, Borah BB, Bhattacharyya DK, Kalita J.   +3 more
europepmc   +1 more source