Swinburne University of Technology — Publications

Cyber Code Intelligence for Android Malware Detection

Article

Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families' malware may remain undetected until the classifier gains specialized knowledge. The most existing ML-based methods will take a long time to learn new malware families in the latest malware family landscape. Existing ML-based Android malware detection and classification methods struggle with the fast evolution of the malware landscape, particularly in terms of the emergence of zero-day malware families and limited representation of single-view features. In this article, a new multiview feature intelligence (MFI) framework is developed to learn the representation of a targeted capability from known malware families for recognizing unknown and evolving malware with the same capability. The new framework performs reverse engineering to extract multiview heterogeneous features, including semantic string features, API call graph features, and smali opcode sequential features. It can learn the representation of a targeted capability from known malware families through a series of processes of feature analysis, selection, aggregation, and encoding, to detect unknown Android malware with shared target capability. We create a new dataset with ground-truth information regarding capability. Many experiments are conducted on the new dataset to evaluate the performance and effectiveness of the new method. The results demonstrate that the new method outperforms three state-of-the-art methods, including: 1) Drebin; 2) MaMaDroid; and 3) N -opcode, when detecting unknown Android malware with targeted capabilities.

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Cyber Code Intelligence for Android Malware Detection

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Publications

Cyber Code Intelligence for Android Malware Detection

Tracking control for network-based T-S fuzzy systems with asynchronous constraints

Synchronization of two coupled Hindmarsh-Rose neurons

Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances

Distributed Fixed-Time Optimization for Second-Order Nonlinear Multiagent Systems: State and Output Feedback Designs

Variance-Constrained Distributed Filtering for Time-Varying Systems With Multiplicative Noises and Deception Attacks Over Sensor Networks

Fixed-Time Cooperative Control for First-Order Multi-Agent Systems

Study of Future Land Use Prediction Based on Machine Learning and Markov Chain

Dynamic Event-Triggered Distributed Coordination Control and its Applications: A Survey of Trends and Techniques

Decentralized Dynamic Event-Triggered Communication and Active Suspension Control of In-Wheel Motor Driven Electric Vehicles with Dynamic Damping

Synchronization criteria for Lur'e complex dynamical networks with coupling delays

Delayed non-fragile H∞ control for offshore steel jacket platforms

Fixed-time pinning-controlled synchronization for coupled delayed neural networks with discontinuous activations

Impulsive control of time-delay systems using delayed impulse and its application to impulsive master–slave synchronization

Repetitive Control: Basic Concept, Fundamental Theory, and Practical Applications

Model-free subspace-based control scheme for grid-connected voltage source inverters

Overlapping mode-dependent H&lt;inf&gt;&amp;#x221E;&lt;/inf&gt; filtering of discrete-time Markov jump linear systems

Railway Virtual Coupling: A Survey of Emerging Control Techniques

Robust stability analysis of linear systems with time-varying delay and nonlinear perturbations

InforTest: Informer-Based Testing for Applications in the Internet of Robotic Things

Overlapping mode-dependent H<inf>∞</inf> filtering of discrete-time Markov jump linear systems