M. A. Ajay Kumara

TL;DR: This paper proposed an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor, which outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%.

TL;DR: An advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS.

TL;DR: There is a need for an effective Android malware detection approach using the static features of the Android applications such as Standard Permissions with Application Programming Interface (API) calls, Non-standard Permission with API-calls, APIs with Standard and Nonstandard Permissions, and the Kullback-Leibler (KL) classifier.

TL;DR: A comprehensive experiment was conducted to perceive the best fit classifier among the chosen classifiers, including the Bayesian-Logistic-Regression, SPegasos, IB1, Bagging, Part, and J48 defined within the WEKA tool, and the overall best performance goes to SPegAsos with the highest accuracy, highest True Positive Rate (TPR), and lowest False positive Rate (FPR).

TL;DR: Recurrent Neural Network’s (RNN) capability to capture long-term features of time-series and sequential data is used to study the scope and effectiveness of RNNs to efficiently detect and analyze malware and benign based on their behaviour, i.e. system call sequences specifically.