SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System [PDF]
IEEE Access, 2018 For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer's attention.
Saba Arshad +5 more
doaj +3 more sources
A Holistic Intelligent Cryptojacking Malware Detection System
IEEE AccessRecent statistics indicate a continuous rise in cryptojacking malware. This malware covertly exploits users’ device resources to mine cryptocurrencies, such as Bitcoin, without their knowledge or consent.
Hadeel A. Almurshid +3 more
doaj +2 more sources
An Insight into the Machine-Learning-Based Fileless Malware Detection [PDF]
, 2023 In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware.
Ahmad, Tahir +5 more
core +5 more sources
Malware detection and classification using low-level features [PDF]
, 2023 Nowadays, computers and computer systems are involved in most areas of our lives. Employees and users of manufacturing and transportation, banking and healthcare, education, and entertainment rely on computers and networks which allow for better, faster,
Banin, Sergii
core +1 more source
A Deep Dive inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores [PDF]
, 2022 peer reviewedMachine learning (ML) advances have been extensively explored for implementing large-scale malware detection. When reported in the literature, performance evaluation of ML-based detectors generally focuses on highlighting the ratio of ...
DAOUDI, Nadia +3 more
core +1 more source
Adaptive secure malware efficient machine learning algorithm for healthcare data
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed +8 more
wiley +1 more source
Toward Hardware-Assisted Malware Detection Utilizing Explainable Machine Learning: A Survey
IEEE Access, 2023 Hardware joined the battle against malware by introducing secure boot architectures, malware-aware processors, and trusted platform modules. Hardware performance indicators, power profiles, and side channel information can be leveraged at run-time via ...
Yehya Nasser, Mohamad Nassar
doaj +1 more source
Application of Distance Metric Learning to Automated Malware Detection
IEEE Access, 2021 Distance metric learning aims to find the most appropriate distance metric parameters to improve similarity-based models such as $k$ -Nearest Neighbors or $k$ -Means. In this paper, we apply distance metric learning to the problem of malware detection.
Martin Jurecek, Robert Lorencz
doaj +1 more source
Android Malware Detection Using BERT [PDF]
, 2022 peer reviewedIn this paper, we propose two empirical studies to (1) detect Android malware and (2) classify Android malware into families. We rst (1) reproduce the results of MalBERT using BERT models learning with Android application's manifests ...
Le Traon, Yves, +9 more
core +1 more source
A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic
IEEE Access, 2020 Because of the characteristic of openness and flexibility, Android has become the most popular mobile platform. However, it has also become the most targeted system by mobile malware.
Jiayin Feng +4 more
doaj +1 more source