Results 51 to 60 of about 134,256 (265)

Evaluation of Machine Learning Algorithms for Malware Detection

Italian National Conference on Sensors, 2023
This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose
M. Akhtar, Tao Feng
semanticscholar   +1 more source

Survey of Machine Learning Techniques for Malware Analysis [PDF]

, 2018
Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind ...
Aniello, Leonardo, Baldoni, Roberto, Ucci, Daniele   +2 more
core   +2 more sources

Identifying the Author Group of Malwares through Graph Embedding and Human-in-the-Loop Classification

Applied Sciences, 2021
Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and ...
Dong-Kyu Chae, Sung-Jun Park, Eujeanne Kim, Jiwon Hong, Sang-Wook Kim   +4 more
doaj   +1 more source

An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy, 2021
Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover ...
Jeyaprakash Hemalatha, S. A. Roseline, S. Geetha, Seifedien Kadry, R. Damaševičius   +4 more
semanticscholar   +1 more source

Python and Malware: Developing Stealth and Evasive Malware without Obfuscation [PDF]

Proceedings of the 18th International Conference on Security and Cryptography, 2021
To appear in SECRYPT ...
Koutsokostas, Vasilios, Patsakis, Constantinos   +1 more
openaire   +2 more sources

Packed malware variants detection using deep belief networks [PDF]

MATEC Web of Conferences, 2020
Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years.
Zhang Zhigang, Chang Chaowen, Han Peisheng, Zhang Hongtao   +3 more
doaj   +1 more source

IoT malware: An attribute-based taxonomy, detection mechanisms and challenges

Peer-to-Peer Networking and Applications, 2023
During the past decade, the Internet of Things (IoT) has paved the way for the ongoing digitization of society in unique ways. Its penetration into enterprise and day-to-day lives improved the supply chain in numerous ways.
Princy Victor, Arash Habibi Lashkari, Rongxing Lu, Tinshu Sasi, Pulei Xiong, Shahrear Iqbal   +5 more
semanticscholar   +1 more source

Malware Classification based on Call Graph Clustering [PDF]

, 2010
Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection.
Kinable, Joris, Kostakis, Orestis
core   +1 more source

Robust Intelligent Malware Detection Using Deep Learning

IEEE Access, 2019
Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks ...
Patlolla Sruthi, Sharath Singh, Mohammad Shoiab, Dheeraj Kumar, Lakkireddy Venkateswara Reddy, Amar Hameid Shnain, Deepti Sharma, Sorabh Lakhanpal   +7 more
semanticscholar   +1 more source

Securing Linux Cloud Environments: Privacy-Aware Federated Learning Framework for Advanced Malware Detection in Linux Clouds

IEEE Access
Cloud computing is integral to modern IT infrastructure, with Linux-based virtual machines (VMs) comprising 95% of public cloud environments. This widespread use makes Linux VMs a prime target for cyberattacks, particularly advanced malware designed for ...
Tom Landman, Nir Nissim
doaj   +1 more source