Machine learning aided malware detection for secure and smart manufacturing: a comprehensive analysis of the state of the art

S Rani, K Tripathi, A Kumar - International Journal on Interactive Design …, 2023 - Springer
In the last decade, the number of computer malware has grown rapidly. Currently,
cybercriminals typically use malicious software (malware) as a means of attacking industrial …

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 …

DTMIC: Deep transfer learning for malware image classification

S Kumar, B Janet - Journal of Information Security and Applications, 2022 - Elsevier
In the ever-changing cyber threat landscape, evolving malware threats demand a new
technique for their detection. This paper puts forward a strategy for distinguishing malware …

[HTML][HTML] Android mobile malware detection using machine learning: A systematic review

J Senanayake, H Kalutarage, MO Al-Kadri - Electronics, 2021 - mdpi.com
With the increasing use of mobile devices, malware attacks are rising, especially on Android
phones, which account for 72.2% of the total market share. Hackers try to attack …

A novel permission-based Android malware detection system using feature selection based on linear regression

DÖ Şahin, OE Kural, S Akleylek, E Kılıç - Neural Computing and …, 2023 - Springer
With the developments in mobile and wireless technology, mobile devices have become an
important part of our lives. While Android is the leading operating system in market share, it …

A novel machine learning approach for android malware detection based on the co-existence of features

E Odat, QM Yaseen - IEEE Access, 2023 - ieeexplore.ieee.org
This paper proposes a machine learning model based on the co-existence of static features
for Android malware detection. The proposed model assumes that Android malware …

Malbertv2: Code aware bert-based model for malware identification

A Rahali, MA Akhloufi - Big Data and Cognitive Computing, 2023 - mdpi.com
To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-
malware software, as well as firewalls, require frequent updates and proactive …

Self-supervised vision transformers for malware detection

S Seneviratne, R Shariffdeen, S Rasnayaka… - IEEE …, 2022 - ieeexplore.ieee.org
Malware detection plays a crucial role in cyber-security with the increase in malware growth
and advancements in cyber-attacks. Previously unseen malware which is not determined by …

Deep learning feature exploration for android malware detection

N Zhang, Y Tan, C Yang, Y Li - Applied Soft Computing, 2021 - Elsevier
Android mobile devices and applications are widely deployed and used in industry and
smart city. Malware detection is one of the most powerful and effective approaches to …

Hybrid sequence‐based Android malware detection using natural language processing

N Zhang, J Xue, Y Ma, R Zhang… - International Journal of …, 2021 - Wiley Online Library
Android platform has been the target of attackers due to its openness and increasing
popularity. Android malware has explosively increased in recent years, which poses serious …