Results 141 to 150 of about 707,343 (186)

MALITE: Lightweight Malware Detection and Classification for Constrained Devices. [PDF]

IEEE Trans Emerg Top Comput
Anand S, Mitra B, Dey S, Rao A, Dhar R, Vaidya J.   +5 more
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. [PDF]

PLoS One
Nazim S, Alam MM, Rizvi SS, Mustapha JC, Hussain SS, Suud MM.   +5 more
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BERT ensemble based MBR framework for android malware detection. [PDF]

Sci Rep
Alsubaei FS, Almazroi AA, Atwa WS, Almazroi AA, Ayub N, Jhanjhi NZ.   +5 more
europepmc   +1 more source

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Harnessing LLMs for IoT Malware Detection: A Comparative Analysis of BERT and GPT-2

2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)
In recent years, the proliferation of Internet of Things (IoT) devices has introduced significant vulnerabilities in cybersecurity, particularly with the rise of sophisticated malware targeting these systems. Traditional detection methods, often based on
Marwan Omar, Hewa Majeed Zangana, Jamal N. Al-Karaki, Derek Mohammed   +3 more
openaire   +2 more sources

The Effect on Network Flows-Based Features and Training Set Size on Malware Detection

IEEE International Symposium on Network Computing and Applications, 2018
Although network flows have been used in areas such as network traffic analysis and botnet detection, not many works have used network flows-based features for malware detection.
J. Jiménez, K. Goseva-Popstojanova
semanticscholar   +1 more source

Towards a 2-hybrid Android malware detection test framework

2016 International Conference on Electronics, Communications and Computers (CONIELECOMP), 2016
Current pervasive usage of mobile devices around the world has rose big security and data protection concerns both into the application development process as into the data security field. Although the long way of development in PC security malware treatment in the computer science and industrial areas, mobile devices security research and development ...
Abraham Rodriguez-Mota, Ponciano Jorge Escamilla-Ambrosio, Salvador Morales-Ortega, Moises Salinas-Rosales, Eleazar Aguirre-Anaya   +4 more
openaire   +1 more source

Ultra-Lightweight Malware Detection of Android Using 2-Level Machine Learning

2016 3rd International Conference on Information Science and Control Engineering (ICISCE), 2016
As Android becoming the most popular smart phone operating system, malicious applications running on the Android platform appears very frequently and poses the major threat to the security of Android. Considering the resources of smart phone are severely limited, a stable, simple and quick malware detection method for Android is indispensable.
Li Ma, Yuexiang Yang, Xiaolei Wang, Jie He   +3 more
openaire   +1 more source

LAMD: Context-Driven Android Malware Detection and Classification with LLMs

2025 IEEE Security and Privacy Workshops (SPW)
The rapid growth of mobile applications has escalated Android mal ware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset biases, and limited explainability.
Xingzhi Qian, Xinran Zheng, Yiling He, Shuo Yang, Lorenzo Cavallaro   +4 more
semanticscholar   +1 more source

Dynamic Prototype Network Based on Sample Adaptation for Few-Shot Malware Detection

IEEE Transactions on Knowledge and Data Engineering, 2023
The continuous increase and spread of malware have caused immeasurable losses to social enterprises and even the country, especially unknown malware. Most existing methods use predefined class samples to train models, which cannot handle unknown malware ...
Yuhan Chai, Lei Du, Jing Qiu, Lihua Yin, Zhihong Tian   +4 more
semanticscholar   +1 more source

MalFSCIL: A Few-Shot Class-Incremental Learning Approach for Malware Detection

IEEE Transactions on Information Forensics and Security
The continuous evolution of malware is posing a serious threat to personal privacy, enterprise data security, and global network infrastructure. For example, attackers can use phishing emails, botnets, etc.
Yuhan Chai, Ximing Chen, Jing Qiu, Lei Du, Yanjun Xiao, Qiying Feng, Shoulin Ji, Z. Tian   +7 more
semanticscholar   +1 more source