Detecting malware with information complexity

Malware concealment is the predominant strategy for malware propagation. Black hats create variants of malware based on polymorphism and metamorphism. Malware variants, by definition, share some information. Although the concealment strategy alters this information, there are still patterns on the software. Given a zoo of labelled malware and benign-ware, we ask whether a suspect program is more similar to our malware or to our benign-ware. Normalized Compression Distance (NCD) is a generic metric that measures the shared information content of two strings. This measure opens a new front in the malware arms race, one where the countermeasures promise to be more costly for malware writers, who must now obfuscate patterns as strings qua strings, without reference to execution, in their variants. Our approach classifies disk-resident malware with 97.4% accuracy and a false positive rate of 3%. We demonstrate that its accuracy can be improved by combining NCD with the compressibility rates of executables using decision forests, paving the way for future improvements. We demonstrate that malware reported within a narrow time frame of a few days is more homogeneous than malware reported over two years, but that our method still classifies the latter with 95.2% accuracy and a 5% false positive rate. Due to its use of compression, the time and computation cost of our method is nontrivial. We show that simple approximation techniques can improve its running time by up to 63%. We compare our results to the results of applying the 59 anti-malware programs used on the VirusTotal website to our malware. Our approach outperforms each one used alone and matches that of all of them used collectively

Detecting Malware with Information Complexity

This work focuses on a specific front of the malware detection arms-race, namely the detection of persistent, disk-resident malware. We exploit normalised compression distance (NCD), an information theoretic measure, applied directly to binaries. Given a zoo of labelled malware and benign-ware, we ask whether a suspect program is more similar to our malware or to our benign-ware. Our approach classifies malware with 97.1% accuracy and a false positive rate of 3%. We achieve our results with off-the-shelf compressors and a standard machine learning classifier and without any specialised knowledge. An end-user need only collect a zoo of malware and benign-ware and then can immediately apply our techniques. We apply statistical rigour to our experiments and our selection of data. We demonstrate that accuracy can be optimised by combining NCD with the compressibility rates of the executables. We demonstrate that malware reported within a more narrow time frame of a few days is more homogenous than malware reported over a longer one of two years but that our method still classifies the latter with 95.2% accuracy and a 5% false positive rate. Due to the use of compression, the time and computation cost of our method is non-trivial. We show that simple approximation techniques can improve the time complexity of our approach by up to 63%. We compare our results to the results of applying the 59 anti-malware programs used on the VirusTotal web site to our malware. Our approach does better than any single one of them as well as the 59 used collectively

Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, yet Effective Time Series CNN-Based Approach

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on detecting malicious applications that are spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. Next, to address the challenge of stealthy malware detection at the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based approach to accurately detect stealthy malware trace at run-time using branch instructions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that using state-of-the-art ML-based malware detection methods is not effective in detecting stealthy malware samples since the captured HPC data not only represents malware but also carries benign applications’ microarchitectural data. The experimental results demonstrate that with the aid of our novel intelligent approach, stealthy malware can be detected at run-time with 94% detection performance on average with only one HPC feature, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively

Survey of Machine Learning Techniques for Malware Analysis

Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies for keeping pace with the speed of development of novel malware. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis. We systematize surveyed papers according to their objectives (i.e., the expected output, what the analysis aims to), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of problems concerning the datasets used in considered works, and finally introduce the novel concept of malware analysis economics, regarding the study of existing tradeoffs among key metrics, such as analysis accuracy and economical costs