Similarity-based Android malware detection using Hamming distance of static binary features
In this paper, we develop four malware detection methods using Hamming distance to find
similarity between samples which are first nearest neighbors (FNN), all nearest neighbors …
similarity between samples which are first nearest neighbors (FNN), all nearest neighbors …
Towards predictive analysis of android vulnerability using statistical codes and machine learning for IoT applications
J Cui, L Wang, X Zhao, H Zhang - Computer Communications, 2020 - Elsevier
Abstract Recently, the Internet of Things (IoT) technology is used for several applications for
exchanging information among various devices. The intelligent IoT based system utilizes an …
exchanging information among various devices. The intelligent IoT based system utilizes an …
Fuzzy Bayesian learning for cyber threat hunting in industrial control systems
K Marsh, SE Gharghasheh - Handbook of big data analytics and forensics, 2022 - Springer
Threat hunting involves actively searching for cybersecurity threats in a system or network,
as opposed to passively detecting threats based on previously seen data. This is most …
as opposed to passively detecting threats based on previously seen data. This is most …
Behaviour-aware malware classification: Dynamic feature selection
Despite the continued advancements in security research, malware persists as being a
major threat in this digital age. Malware detection is a primary defence strategy for most …
major threat in this digital age. Malware detection is a primary defence strategy for most …