Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks

With computing devices and the Internet being indispensable in people\u27s everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file co-existence, can provide valuable information in malware detection and make evasion harder. To better understand the properties of file-to-file relations, we construct the file co-existence graph. Resting on the constructed graph, we investigate the semantic relatedness among files, and leverage graph inference, active learning and graph representation learning for malware detection. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed learning paradigms. As machine learning-based detection systems become more widely deployed, the incentive for defeating them increases. Therefore, we go further insight into the arms race between adversarial malware attack and defense, and aim to enhance the security of machine learning-based malware detection systems. In particular, we first explore the adversarial attacks under different scenarios (i.e., different levels of knowledge the attackers might have about the targeted learning system), and define a general attack strategy to thoroughly assess the adversarial behaviors. Then, considering different skills and capabilities of the attackers, we propose the corresponding secure-learning paradigms to counter the adversarial attacks and enhance the security of the learning systems while not compromising the detection accuracy. We conduct a series of comprehensive experimental studies based on the real sample collections from Comodo Cloud Security Center and the promising results demonstrate the effectiveness of our proposed secure-learning models, which can be readily applied to other detection tasks

Backdoor detection systems for embedded devices

A system is said to contain a backdoor when it intentionally includes a means to trigger the execution of functionality that serves to subvert its expected security. Unfortunately, such constructs are pervasive in software and systems today, particularly in the firmware of commodity embedded systems and “Internet of Things” devices. The work presented in this thesis concerns itself with the problem of detecting backdoor-like constructs, specifically those present in embedded device firmware, which, as we show, presents additional challenges in devising detection methodologies. The term “backdoor”, while used throughout the academic literature, by industry, and in the media, lacks a rigorous definition, which exacerbates the challenges in their detection. To this end, we present such a definition, as well as a framework, which serves as a basis for their discovery, devising new detection techniques and evaluating the current state-of-the-art. Further, we present two backdoor detection methodologies, as well as corresponding tools which implement those approaches. Both of these methods serve to automate many of the currently manual aspects of backdoor identification and discovery. And, in both cases, we demonstrate that our approaches are capable of analysing device firmware at scale and can be used to discover previously undocumented real-world backdoors

Avaliação da viabilidade de modelos filogenéticos na classificação de aplicações maliciosas

Orientador: André Ricardo Abed GrégioTese (Doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 03/02/2023Inclui referências: p. 150-170Área de concentração: Ciência da ComputaçãoResumo: Milhares de códigos maliciosos são criados, modificados com apoio de ferramentas de automação e liberados diariamente na rede mundial de computadores. Entre essas ameaças, malware são programas projetados especificamente para interromper, danificar ou obter acesso não autorizado a um sistema ou dispositivo. Para facilitar a identificação e a categorização de comportamentos comuns, estruturas e outras características de malware, possibilitando o desenvolvimento de soluções de defesa, existem estratégias de análise que classificam malware em grupos conhecidos como famílias. Uma dessas estratégias é a Filogenia, técnica baseada na Biologia, que investiga o relacionamento histórico e evolutivo de uma espécie ou outro grupo de elementos. Além disso, a utilização de técnicas de agrupamento em conjuntos semelhantes facilita tarefas de engenharia reversa para análise de variantes desconhecidas. Uma variante se refere a uma nova versão de um código malicioso que é criada a partir de modificações de malware existentes. O presente trabalho investiga a viabilidade do uso de filogenias e de métodos de agrupamento na classificação de variantes de malware para plataforma Android. Inicialmente foram analisados 82 trabalhos correlatos para verificação de configurações de experimentos do estado da arte. Após esse estudo, foram realizados quatro experimentos para avaliar uso de métricas de similaridade e de algoritmos de agrupamento na classificação de variantes e na análise de similaridade entre famílias. Propôs-se então um Fluxo de Atividades para Agrupamento de malware com o objetivo de auxiliar na definição de parâmetros para técnicas de agrupamentos, incluindo métricas de similaridade, tipo de algoritmo de agrupamento a ser utilizado e seleção de características. Como prova de conceito, foi proposto o framework Androidgyny para análise de amostras, extração de características e classificação de variantes com base em medóides (elementos representativos médios de cada grupo) e características exclusivas de famílias conhecidas. Para validar o Androidgyny foram feitos dois experimentos: um comparativo com a ferramenta correlata Gefdroid e outro, com exemplares das 25 famílias mais populosas do dataset Androzoo.Abstract: Thousands of malicious codes are created, modified with the support of tools of automation and released daily on the world wide web. Among these threats, malware are programs specifically designed to interrupt, damage, or gain access unauthorized access to a system or device. To facilitate identification and categorization of common behaviors, structures and other characteristics of malware, enabling the development of defense solutions, there are analysis strategies that classify malware into groups known as families. One of these strategies is Phylogeny, a technique based on the Biology, which investigates the historical and evolutionary relationship of a species or other group of elements. In addition, the use of clustering techniques on similar sets facilitates reverse engineering tasks for analysis of unknown variants. a variant refers to a new version of malicious code that is created from modifications of existing malware. The present work investigates the feasibility of using phylogenies and methods of grouping in the classification of malware variants for the Android platform. Initially 82 related works were analyzed to verify experiment configurations of the state of the art. After this study, four experiments were carried out to evaluate the use of similarity measures and clustering algorithms in the classification of variants and in the similarity analysis between families. In addition to these experiments, a Flow of Activities for Malware grouping with five distinct phases. This flow has purpose of helping to define parameters for clustering techniques, including measures of similarity, type of clustering algorithm to be used and feature selection. After defining the flow of activities, the Androidgyny framework was proposed, a prototype for sample analysis, feature extraction and classification of variants based on medoids and unique features of known families. To validate Androidgyny were Two experiments were carried out: a comparison with the related tool Gefdroid and another with copies of the 25 most populous families in the Androzoo dataset

Availability and Reliability Analysis of Computer Software Systems Considering Maintenance and Security Issues

Ph.DDOCTOR OF PHILOSOPH

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Hierarchical associative classifier (HAC) for malware detection from the large and imbalanced gray list

National Science Foundation of China [10771176]; US National Science Foundation [IIS-0546280]; IBMNowadays, numerous attacks made by the malware (e.g., viruses, backdoors, spyware, trojans and worms) have presented a major security threat to computer users. Currently, the most significant line of defense against malware is anti-virus products which focus on authenticating valid software from a whitelist, blocking invalid software from a blacklist, and running any unknown software (i.e., the gray list) in a controlled manner. The gray list, containing unknown software programs which could be either normal or malicious, is usually authenticated or rejected manually by virus analysts. Unfortunately, along with the development of the malware writing techniques, the number of file samples in the gray list that need to be analyzed by virus analysts on a daily basis is constantly increasing. The gray list is not only large in size, but also has an imbalanced class distribution where malware is the minority class. In this paper, we describe our research effort on building automatic, effective, and interpretable classifiers resting on the analysis of Application Programming Interfaces (APIs) called by Windows Portable Executable (PE) files for detecting malware from the large and imbalanced gray list. Our effort is based on associative classifiers due to their high interpretability as well as their capability of discovering interesting relationships among API calls. We first adapt several different post-processing techniques of associative classification, including rule pruning and rule re-ordering, for building effective associative classifiers from large collections of training data. In order to help the virus analysts detect malware from the imbalanced gray list, we then develop the Hierarchical Associative Classifier (HAC). HAC constructs a two-level associative classifier to maximize precision and recall of the minority (malware) class: in the first level, it uses high precision rules of majority (benign file samples) class and low precision rules of minority class to achieve high recall; and in the second level, it ranks the minority class files and optimizes the precision. Finally, since our case studies are based on a large and real data collection obtained from the Anti-virus Lab of Kingsoft corporation, including 8,000,000 malware, 8,000,000 benign files, and 100,000 file samples from the gray list, we empirically examine the sampling strategy to build the classifiers for such a large data collection to avoid over-fitting and achieve great effectiveness as well as high efficiency. Promising experimental results demonstrate the effectiveness and efficiency of the HAC classifier. HAC has already been incorporated into the scanning tool of Kingsoft's Anti-Virus software

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation