Malware Detection and Analysis Tools

The huge amounts of data and information that need to be analyzed for possible malicious intent are one ofthe big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads.In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by usingmachine learning techniques to identify and classify unknown malware into their established families. Thissurvey paper gives an overview of the malware detection and analysis techniques and tools

MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network

Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method

Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.Comment: 30 pages, 9 figures, 7 tables, no where submitted ye

Automatic Malware Detection

The problem of automatic malware detection presents challenges for antivirus vendors. Since the manual investigation is not possible due to the massive number of samples being submitted every day, automatic malware classication is necessary. Our work is focused on an automatic malware detection framework based on machine learning algorithms. We proposed several static malware detection systems for the Windows operating system to achieve the primary goal of distinguishing between malware and benign software. We also considered the more practical goal of detecting as much malware as possible while maintaining a suciently low false positive rate. We proposed several malware detection systems using various machine learning techniques, such as ensemble classier, recurrent neural network, and distance metric learning. We designed architectures of the proposed detection systems, which are automatic in the sense that extraction of features, preprocessing, training, and evaluating the detection model can be automated. However, antivirus program relies on more complex system that consists of many components where several of them depends on malware analysts and researchers. Malware authors adapt their malicious programs frequently in order to bypass antivirus programs that are regularly updated. Our proposed detection systems are not automatic in the sense that they are not able to automatically adapt to detect the newest malware. However, we can partly solve this problem by running our proposed systems again if the training set contains the newest malware. Our work relied on static analysis only. In this thesis, we discuss advantages and drawbacks in comparison to dynamic analysis. Static analysis still plays an important role, and it is used as one component of a complex detection system.The problem of automatic malware detection presents challenges for antivirus vendors. Since the manual investigation is not possible due to the massive number of samples being submitted every day, automatic malware classication is necessary. Our work is focused on an automatic malware detection framework based on machine learning algorithms. We proposed several static malware detection systems for the Windows operating system to achieve the primary goal of distinguishing between malware and benign software. We also considered the more practical goal of detecting as much malware as possible while maintaining a suciently low false positive rate. We proposed several malware detection systems using various machine learning techniques, such as ensemble classier, recurrent neural network, and distance metric learning. We designed architectures of the proposed detection systems, which are automatic in the sense that extraction of features, preprocessing, training, and evaluating the detection model can be automated. However, antivirus program relies on more complex system that consists of many components where several of them depends on malware analysts and researchers. Malware authors adapt their malicious programs frequently in order to bypass antivirus programs that are regularly updated. Our proposed detection systems are not automatic in the sense that they are not able to automatically adapt to detect the newest malware. However, we can partly solve this problem by running our proposed systems again if the training set contains the newest malware. Our work relied on static analysis only. In this thesis, we discuss advantages and drawbacks in comparison to dynamic analysis. Static analysis still plays an important role, and it is used as one component of a complex detection system

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