Results 91 to 100 of about 46,889 (202)

Malware Clustering Using Family Dependency Graph

IEEE Access, 2019
Malware brings a major security threat on the Internet today. It is not surprising that much research has concentrated on detecting malware. Unfortunately, the current malware detection approaches suffer from ineffective detection of new malware samples.
Binlin Cheng, Qiang Tong, Jianhong Wang, Wenhui Tian   +3 more
doaj   +1 more source

An Android Malicious Code Detection Method Based on Improved DCA Algorithm

Entropy, 2017
Recently, Android malicious code has increased dramatically and the technology of reinforcement is increasingly powerful. Due to the development of code obfuscation and polymorphic deformation technology, the current Android malicious code static ...
Chundong Wang, Zhiyuan Li, Liangyi Gong, Xiuliang Mo, Hong Yang, Yi Zhao   +5 more
doaj   +1 more source

Static analysis framework for permission-based dataset generation and android malware detection using machine learning

EURASIP Journal on Information Security
Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques.
Amarjyoti Pathak, Th. Shanta Kumar, Utpal Barman   +2 more
doaj   +1 more source

A study of the relationship of malware detection mechanisms using Artificial Intelligence

ICT Express
Implementation of malware detection using Artificial Intelligence (AI) has emerged as a significant research theme to combat evolving various types of malwares.
Jihyeon Song, Sunoh Choi, Jungtae Kim, Kyungmin Park, Cheolhee Park, Jonghyun Kim, Ikkyun Kim   +6 more
doaj   +1 more source

Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar

Future Internet
We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an
Naonobu Okazaki, Shotaro Usuzaki, Tsubasa Waki, Hyoga Kawagoe, Mirang Park, Hisaaki Yamaba, Kentaro Aburada   +6 more
doaj   +1 more source

Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures

Applied Computational Intelligence and Soft Computing, 2014
Malware detection done at the network infrastructure level is still an open research problem ,considering the evolution of malwares and high detection accuracy needed to detect these threats.
Ismahani Ismail, Sulaiman Mohd Nor, Muhammad Nadzir Marsono   +2 more
doaj   +1 more source

Hybrid Input Model Using Multiple Features From Surface Analysis for Malware Detection

IEEE Access
Many malware detection models have been proposed to protect computers from the ever- increasing number of malware attacks. The features that are obtained from surface analysis and machine learning are often used for malware detection.
Mamoru Mimura, Satoki Kanno
doaj   +1 more source

MIDALF—multimodal image and audio late fusion for malware detection

EURASIP Journal on Information Security
Malware detection remains a critical challenge in cybersecurity due to the rapid evolution of sophisticated malware and adversarial threats. Traditional detection systems struggle to adapt to dynamic malware behavior and are particularly vulnerable to ...
Setia Juli Irzal Ismail, Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi   +4 more
doaj   +1 more source

GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection

IEEE Access
The sophistication of Android malware poses significant threats to user security and privacy. Traditional detection methods struggle with rapid malware evolution and benign application diversity, leading to high false positive rates and limited ...
Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki   +3 more
doaj   +1 more source

Detection of unseen malware threats using generative adversarial networks and deep learning models

Scientific Reports
The fast advancement of malware makes it an urgent problem for cybersecurity, as perpetrators consistently devise obfuscation methods to avoid detection.
Chirag Joshi, Jashvant Kumar, Gaurav Kumawat   +2 more
doaj   +1 more source