A comprehensive survey on deep learning based malware detection techniques

M Gopinath, SC Sethuraman - Computer Science Review, 2023 - Elsevier
Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …

A systematic literature review of android malware detection using static analysis

Y Pan, X Ge, C Fang, Y Fan - IEEE Access, 2020 - ieeexplore.ieee.org
Android malware has been in an increasing trend in recent years due to the pervasiveness
of Android operating system. Android malware is installed and run on the smartphones …

DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network

SI Imtiaz, S ur Rehman, AR Javed, Z Jalil, X Liu… - Future Generation …, 2021 - Elsevier
Android smartphones are being utilized by a vast majority of users for everyday planning,
data exchanges, correspondences, social interaction, business execution, bank …

Extensible android malware detection and family classification using network-flows and API-calls

L Taheri, AFA Kadir, AH Lashkari - … Carnahan conference on …, 2019 - ieeexplore.ieee.org
Android OS-based mobile devices have attracted numerous end-users since they are
convenient to work with and offer a variety of features. As a result, Android has become one …

Security analysis of IoT devices by using mobile computing: a systematic literature review

B Liao, Y Ali, S Nazir, L He, HU Khan - IEEE Access, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) devices are operating in various domains like healthcare
environment, smart cities, smart homes, transportation, and smart grid system. These …

[HTML][HTML] Kronodroid: time-based hybrid-featured dataset for effective android malware detection and characterization

A Guerra-Manzanares, H Bahsi, S Nõmm - Computers & Security, 2021 - Elsevier
Android malware evolution has been neglected by the available data sets, thus providing a
static snapshot of a non-stationary phenomenon. The impact of the time variable has not had …

Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach

S Chen, M Xue, L Fan, S Hao, L Xu, H Zhu, B Li - computers & security, 2018 - Elsevier
The evolution of mobile malware poses a serious threat to smartphone security. Today,
sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via …

DroidEncoder: Malware detection using auto-encoder based feature extractor and machine learning algorithms

H Bakır, R Bakır - Computers and Electrical Engineering, 2023 - Elsevier
Android Malware detection became a hot topic over the last several years. Although
considerable studies have been conducted utilizing machine learning-based methods, little …

Deep learning for image-based mobile malware detection

F Mercaldo, A Santone - Journal of Computer Virology and Hacking …, 2020 - Springer
Current anti-malware technologies in last years demonstrated their evident weaknesses due
to the signature-based approach adoption. Many alternative solutions were provided by the …

EntropLyzer: Android malware classification and characterization using entropy analysis of dynamic characteristics

DS Keyes, B Li, G Kaur, AH Lashkari… - … Privacy, and Security …, 2021 - ieeexplore.ieee.org
The unmatched threat of Android malware has tremendously increased the need for
analyzing prominent malware samples. There are remarkable efforts in static and dynamic …