Results 41 to 50 of about 31,501 (227)
, 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
Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications [PDF]
, 2018 Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces.
Argyriou, Marios +2 more
core +2 more sources
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 +5 more
core +1 more source
MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification
IEEE AccessMalware 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 +4 more
doaj +1 more source
IEEE Access, 2021 Numerous metamorphic and polymorphic malicious variants are generated automatically on a daily basis. In order to do that, malware vendors employ mutation engines that transform the code of a malicious program while retaining its functionality, aiming to
Ron Korine, Danny Hendler
doaj +1 more source
Classification of malware based on string and function feature selection
, 2010 Anti-malware software producers are continually challenged to identify and counter new malware as it is released into the wild. A dramatic increase in malware production in recent years has rendered the conventional method of manually determining a ...
Batten, Lynn +3 more
core +1 more source
Efficient Deep Learning Network With Multi-Streams for Android Malware Family Classification
IEEE Access, 2022 It is important to effectively detect, mitigate, and defend against Android malware attacks, because Android malware has long represented a major threat to Android app security.
Hyun-Il Kim +3 more
doaj +1 more source
Mission Aware Cyber‐Physical Security
Systems Engineering, EarlyView.ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis +3 more
wiley +1 more source
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
, 2017 In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA,
Chen, Aokun +7 more
core +1 more source
A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning
IEEE Access, 2019 Malware has become a major threat to cyberspace security, not only because of the increasing complexity of malware itself, but also because of the continuously created and produced malicious code.
Hangfeng Yang +4 more
doaj +1 more source