Results 31 to 40 of about 29,821 (199)

A Data Mining Classification Approach for Behavioral Malware Detection

Journal of Computer Networks and Communications, 2016
Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.
Monire Norouzi, Alireza Souri, Majid Samad Zamini   +2 more
doaj   +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

Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions

Applied Sciences, 2021
Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections.
Minki Kim, Daehan Kim, Changha Hwang, Seongje Cho, Sangchul Han, Minkyu Park   +5 more
doaj   +1 more source

Detecting obfuscated malware using reduced opcode set and optimised runtime trace [PDF]

, 2016
The research presented, investigates the optimal set of operational codes (opcodes) that create a robust indicator of malicious software (malware) and also determines a program’s execution duration for accurate classification of benign and malicious ...
McLaughlin, Kieran, O'Kane, Philip, Sezer, Sakir   +2 more
core   +1 more source

Learning and Classification of Malware Behavior [PDF]

, 2008
Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection.
Rieck K., Holz T., Willems C., Dussel P., Laskov P.   +4 more
openaire   +2 more sources

A Hybrid Approach for Android Malware Detection and Family Classification.

International Journal of Interactive Multimedia and Artificial Intelligence, 2021
With the increase in the popularity of mobile devices, malicious applications targeting Android platform have greatly increased. Malware is coded so prudently that it has become very complicated to identify.
Meghna Dhalaria, Ekta Gandotra
doaj   +1 more source

Unveiling Zeus

, 2013
Malware family classification is an age old problem that many Anti-Virus (AV) companies have tackled. There are two common techniques used for classification, signature based and behavior based.
Alrawi, Omar, Mohaisen, Abedelaziz
core   +1 more source

An Efficient Malware Classification Method Based on the AIFS-IDL and Multi-Feature Fusion

Information, 2022
In recent years, the presence of malware has been growing exponentially, resulting in enormous demand for efficient malware classification methods.
Xuan Wu, Yafei Song
doaj   +1 more source

Android Malware Family Classification Based on Resource Consumption over Time

, 2017
The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years.
Aniello, Leonardo, Baldoni, Roberto, Ciccotelli, Claudio, Massarelli, Luca, Querzoni, Leonardo, Ucci, Daniele   +5 more
core   +1 more source

MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification

IEEE Access
Malware classification with supervised learning requires a large dataset, which needs an expensive and time-consuming labeling process. In this paper, we explore the efficacy of self-supervised learning techniques for malware classification.
Setia Juli Irzal Ismail, Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi   +4 more
doaj   +1 more source