High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

Modelling Duqu 2.0 Malware using Attack Trees with Sequential Conjunction

In this paper we identify requirements for choosing a threat modelling formalisation for modelling sophisticated malware such as Duqu 2.0. We discuss the gaps in current formalisations and propose the use of Attack Trees with Sequential Conjunction when it comes to analysing complex attacks. The paper models Duqu 2.0 based on the latest information sourced from formal and informal sources. This paper provides a well structured model which can be used for future analysis of Duqu 2.0 and related attacks.

PageRank in Malware Categorization

In this paper, we propose a malware categorization method that models malware behavior in terms of instructions using PageRank. PageRank computes ranks of web pages based on structural information and can also compute ranks of instructions that represent the structural information of the instructions in malware analysis methods. Our malware categorization method uses the computed ranks as features in machine learning algorithms. In the evaluation, we compare the effectiveness of different PageRank algorithms and also investigate bagging and boosting algorithms to improve the categorization accuracy.

Detecting obfuscated malware using reduced opcode set and optimised runtime trace

The research presented, investigates the optimal set of operational codes (opcodes) that create a robust indicator of malicious software (malware) and also determines a program’s execution duration for accurate classification of benign and malicious software. The features extracted from the dataset are opcode density histograms, extracted during the program execution. The classifier used is a support vector machine and is configured to select those features to produce the optimal classification of malware over different program run lengths. The findings demonstrate that malware can be detected using dynamic analysis with relatively few opcodes.

IRP Poster - The Evolution of Malware

IRP poster for "The Evolution of Malware"

Function length as a tool for malware classification

The proliferation of malware is a serious threat to computer and information systems throughout the world. Antimalware companies are continually challenged to identify and counter new malware as it is released into the wild. In attempts to speed up this identification and response, many researchers have examined ways to efficiently automate classification of malware as it appears in the environment. In this paper, we present a fast, simple and scalable method of classifying Trojans based only on the lengths of their functions. Our results indicate that function length may play a significant role in classifying malware, and, combined with other features, may result in a fast, inexpensive and scalable method of malware classification.

Classification of malware based on string and function feature selection

Anti-malware software producers are continually challenged to identify and counter new malware as it is released into the wild. A dramatic increase in malware production in recent years has rendered the conventional method of manually determining a signature for each new malware sample untenable. This paper presents a scalable, automated approach for detecting and classifying malware by using pattern recognition algorithms and statistical methods at various stages of the malware analysis life cycle. Our framework combines the static features of function length and printable string information extracted from malware samples into a single test which gives classification results better than those achieved by using either feature individually. In our testing we input feature information from close to 1400 unpacked malware samples to a number of different classification algorithms. Using k-fold cross validation on the malware, which includes Trojans and viruses, along with 151 clean files, we achieve an overall classification accuracy of over 98%.

Differentiating malware from cleanware using behavioural analysis

This paper proposes a scalable approach for distinguishing malicious files from clean files by investigating the behavioural features using logs of various API calls. We also propose, as an alternative to the traditional method of manually identifying malware files, an automated classification system using runtime features of malware files. For both projects, we use an automated tool running in a virtual environment to extract API call features from executables and apply pattern recognition algorithms and statistical methods to differentiate between files. Our experimental results, based on a dataset of 1368 malware and 456 cleanware files, provide an accuracy of over 97% in distinguishing malware from cleanware. Our techniques provide a similar accuracy for classifying malware into families. In both cases, our results outperform comparable previously published techniques.

Mutual authentication with malware protection for a RFID system

Radio Frequency Identification (RFID) system is a remote identification technology which is taking the place of barcodes to become electronic tags of an object. However, its radio transmission nature is making it vulnerable in terms of security. Recently, research proposed that an RFID tag can contain malicious code which might spread viruses, worms and other exploits to middleware and back-end systems. This paper is proposing a framework which will provide protection from malware and ensure the data privacy of a tag. The framework will use a sanitization technique with a mutual authentication in the reader level. This will ensure that any malicious code in the tag is identified. If the tag is infected by malicious code it will stop execution of the code in the RFIF system. Here shared unique parameters are used for authentication. It will be capable of protecting an RFID system from denial of service (DOS) attack, forward security and rogue reader better than existing protocols. The framework is introducing a layer concept on a smart reader to reduce coupling between different tasks. Using this framework, the RFID system will be protected from malware and also the privacy of the tag will be ensured.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Smart RFID reader protocol for malware detection

Radio frequency identification (RFID) is a remote identification technique promises to revolutionize the way a specific object use to identify in our industry. However, large scale implementation of RFID sought for protection, against Malware threat, information privacy and un-traceability, for low cost RFID tag. In this paper, we propose a framework to provide privacy for tag data and to provide protection for RFID system from malware. In the proposed framework, malware infected tag is detected by analysing individual component of the RFID tag. It uses sanitization technique for analysing individual component. Here authentication based shared unique parameters is used as a method to protect privacy. This authentication protocol will be capable of handling forward and backward security and identifying rogue reader better than existing protocols. Using this framework, the RFID system will be protected from malware and the privacy of the tag will be ensured as well.

An integrated malware detection and classification system

This thesis is to develop effective and efficient methodologies which can be applied to continuously improve the performance of detection and classification on malware collected over an extended period of time. The robustness of the proposed methodologies has been tested on malware collected over 2003-2010.

Feature reduction to speed up malware classification

In statistical classification work, one method of speeding up the process is to use only a small percentage of the total parameter set available. In this paper, we apply this technique both to the classification of malware and the identification of malware from a set combined with cleanware. In order to demonstrate the usefulness of our method, we use the same sets of malware and cleanware as in an earlier paper. Using the statistical technique Information Gain (IG), we reduce the set of features used in the experiment from 7,605 to just over 1,000. The best accuracy obtained in the former paper using 7,605 features is 97.3% for malware versus cleanware detection and 97.4% for malware family classification; on the reduced feature set, we obtain a (best) accuracy of 94.6% on the malware versus cleanware test and 94.5% on the malware classification test. An interesting feature of the new tests presented here is the reduction in false negative rates by a factor of about 1/3 when compared with the results of the earlier paper. In addition, the speed with which our tests run is reduced by a factor of approximately 3/5 from the times posted for the original paper. The small loss in accuracy and improved false negative rate along with significant improvement in speed indicate that feature reduction should be further pursued as a tool to prevent algorithms from becoming intractable due to too much data.