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Android Malware Classification Using Parallelized Machine Learning Methods
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Book Synopsis Android Malware Classification Using Parallelized Machine Learning Methods by : Lifan Xu
Download or read book Android Malware Classification Using Parallelized Machine Learning Methods written by Lifan Xu and published by . This book was released on 2016 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Android is the most popular mobile operating system with a market share of over 80%. Due to its popularity and also its open source nature, Android is now the platform most targeted by malware, creating an urgent need for effective defense mechanisms to protect Android-enabled devices. In this dissertation, we present a novel characterization and machine learning method for Android malware classification. We first present a method of dynamically analyzing and classifying Android applications as either malicious or benign based on their execution behaviors. We invent novel graph-based methods of characterizing an application's execution behavior that are inspired by traditional vector-based characterization methods. We show evidence that our graph-based techniques are superior to vector-based techniques for the problem of classifying malicious and benign applications. We also augment our dynamic analysis characterization method with a static analysis method which we call HADM, Hybrid Analysis for Detection of Malware. We first extract static and dynamic information, and convert this information into vector-based representations. It has been shown that combining advanced features derived by deep learning with the original features provides significant gains. Therefore, we feed each of the original dynamic and static feature vector sets to a Deep Neural Network (DNN) which outputs a new set of features. These features are then concatenated with the original features to construct DNN vector sets. Different kernels are then applied onto the DNN vector sets. We also convert the dynamic information into graph-based representations and apply graph kernels onto the graph sets. Learning results from various vector and graph feature sets are combined using hierarchical Multiple Kernel Learning (MKL) to build a final hybrid classifier. Graph-based characterization methods and their associated machine learning algorithm tend to yield better accuracy for the problem of malware detection. However, the graph-based machine learning techniques we use, i.e., graph kernels, are computationally expensive. Therefore, we also study the parallelization of graph kernels in this dissertation. We first present a fast sequential implementation of the graph kernel. Then, we explore two different parallelization schemes on the CPU and four different implementations on the GPU. After analyzing the advantages of each, we present a hybrid parallel scheme, which dynamically chooses the best parallel implementation to use based on characteristics of the problem. In the last chapter of this dissertation, we explore parallelizing deep learning on a novel architecture design, which may be prevalent in the future. Parallelization of deep learning methods has been studied on traditional CPU and GPU clusters. However, the emergence of Processing In Memory (PIM) with die-stacking technology presents an opportunity to speed up deep learning computation and reduce energy consumption by providing low-cost high-bandwidth memory accesses. PIM uses 3D die stacking to move computations closer to memory and therefore reduce data movement overheads. In this dissertation, we study the parallelization of deep learning methods on a system with multiple PIM devices. We select three representative deep learning neural network layers: the convolutional, pooling, and fully connected layers, and parallelize them using different schemes targeted to PIM devices.
Book Synopsis Malware Analysis Using Artificial Intelligence and Deep Learning by : Mark Stamp
Download or read book Malware Analysis Using Artificial Intelligence and Deep Learning written by Mark Stamp and published by Springer Nature. This book was released on 2020-12-20 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
Book Synopsis Android Malware Detection using Machine Learning by : ElMouatez Billah Karbab
Download or read book Android Malware Detection using Machine Learning written by ElMouatez Billah Karbab and published by Springer Nature. This book was released on 2021-07-10 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Book Synopsis Android Malware Detection and Adversarial Methods by : Weina Niu
Download or read book Android Malware Detection and Adversarial Methods written by Weina Niu and published by Springer Nature. This book was released on with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Android Malware Detection and Classification Using Machine Learning Techniques by : Satyajit Padalkar
Download or read book Android Malware Detection and Classification Using Machine Learning Techniques written by Satyajit Padalkar and published by . This book was released on 2014 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Android is popular mobile operating system and there are multiple marketplaces for android applications. Most of these market places allow applications to be signed using self-signed certificates. Due to this practice there exists little or very limited control over the kind of applications that are being distributed. Also advancement of android root kits is making it increasingly easier to repackage existing android applications with malicious code. Conventional signature based techniques fail to detect these malwares. So detection and classification of android malwares is a very difficult problem to solve.
Book Synopsis An Analysis of Android Malware Detection Using Tree Learning Techniques by : Kyler D. Dickey
Download or read book An Analysis of Android Malware Detection Using Tree Learning Techniques written by Kyler D. Dickey and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Android malware is a growing threat, coinciding with the increasing adoption of the Android platform. Malware detection methods used to maintain user privacy and system integrity are increasingly becoming the subject of research. Many new methods studied employ learning algorithms to detect malicious programs. This study investigates the use of byte and opcode frequency features as inputs for tree-based machine learning methods. The algorithm is optimized to reduce overfitting given input hyperparameter combinations and is tuned using cross-validation procedures. Lastly, the study deliberates on possible avenues for future research to gather more concrete evidence for the efficacy and cost-effectiveness of such a system in a productive environment, emphasizing the need for more strenuous testing processes.
Book Synopsis Android Malware Detection Through Permission and App Component Analysis Using Machine Learning Algorithms by : Keyur Milind Kulkarni
Download or read book Android Malware Detection Through Permission and App Component Analysis Using Machine Learning Algorithms written by Keyur Milind Kulkarni and published by . This book was released on 2018 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improvement in technology has inevitably altered the tactic of criminals to thievery. In recent times, information is the real commodity and it is thus subject to theft as any other possessions: cryptocurrency, credit card numbers, and illegal digital material are on the top. If globally available platforms for smartphones are considered, the Android open source platform (AOSP) emerges as a prevailing contributor to the market and its popularity continues to intensify. Whilst it is beneficiary for users, this development simultaneously makes a prolific environment for exploitation by immoral developers who create malware or reuse software illegitimately acquired by reverse engineering. Android malware analysis techniques are broadly categorized into static and dynamic analysis. Many researchers have also used feature-based learning to build and sustain working security solutions. Although Android has its base set of permissions in place to protect the device and resources, it does not provide strong enough security framework to defend against attacks. This thesis presents several contributions in the domain of security of Android applications and the data within these applications. First, a brief survey of threats, vulnerability and security analysis tools for the AOSP is presented. Second, we develop and use a genre extraction algorithm for Android applications to check the availability of those applications in Google Play Store. Third, an algorithm for extracting unclaimed permissions is proposed which will give a set of unnecessary permissions for applications under examination. Finally, machine learning aided approaches for analysis of Android malware were adopted. Features including permissions, APIs, content providers, broadcast receivers, and services are extracted from benign (~2,000) and malware (5,560) applications and examined for evaluation. We create feature vector combinations using these features and feed these vectors to various classifiers. Based on the evaluation metrics of classifiers, we scrutinize classifier performance with respect to specific feature combination. Classifiers such as SVM, Logistic Regression and Random Forests spectacle a good performance whilst the dataset of combination of permissions and APIs records the maximum accuracy for Logistic Regression.
Book Synopsis Android Malware Detection Using Category-based Machine Learning Classifiers by : Huda Ali Alatwi
Download or read book Android Malware Detection Using Category-based Machine Learning Classifiers written by Huda Ali Alatwi and published by . This book was released on 2016 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Android malware growth has been increasing dramatically along with increasing of the diversity and complicity of their developing techniques. Machine learning techniques are the current methods to model patterns of static features and dynamic behaviors of Android malware. Whereas the accuracy rates of the classifiers increase with increasing the quality of the features, we relate between the apps' features and the features that are needed to deliver the category's functionality. Differently, our classification approach defines legitimate static features for benign apps under a specific category as opposite to identifying malicious patterns. We utilize the features of the top rated apps in a specific category to learn a malware detection classifier for the given category. Android apps stores organize apps into different categories; For example, Google play store organizes apps into 26 categories such as: Health and Fitness, News and Magazine, Music and Audio, etc. Each category has its distinct functionality which means the apps under a specific category are similar in their static and dynamic features. In general, benign apps under a certain category tend to share a common set of features. On the contrary, malicious apps tend to request abnormal features, less or more than what are common for the category that they belong to. This study proposes category-based machine learning classifiers to enhance the performance of classification models at detecting malicious apps under a certain category. The intensive machine learning experiments proved that category-based classifiers report a remarkable higher average performance compared to non-category based."--Abstract.
Book Synopsis Mobile OS Vulnerabilities by : Shivi Garg
Download or read book Mobile OS Vulnerabilities written by Shivi Garg and published by CRC Press. This book was released on 2023-08-17 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is book offers in-depth analysis of security vulnerabilities in different mobile operating systems. It provides methodology and solutions for handling Android malware and vulnerabilities and transfers the latest knowledge in machine learning and deep learning models towards this end. Further, it presents a comprehensive analysis of software vulnerabilities based on different technical parameters such as causes, severity, techniques, and software systems’ type. Moreover, the book also presents the current state of the art in the domain of software threats and vulnerabilities. This would help analyze various threats that a system could face, and subsequently, it could guide the securityengineer to take proactive and cost-effective countermeasures. Security threats are escalating exponentially, thus posing a serious challenge to mobile platforms. Android and iOS are prominent due to their enhanced capabilities and popularity among users. Therefore, it is important to compare these two mobile platforms based on security aspects. Android proved to be more vulnerable compared to iOS. The malicious apps can cause severe repercussions such as privacy leaks, app crashes, financial losses (caused by malware triggered premium rate SMSs), arbitrary code installation, etc. Hence, Android security is a major concern amongst researchers as seen in the last few years. This book provides an exhaustive review of all the existing approaches in a structured format. The book also focuses on the detection of malicious applications that compromise users' security and privacy, the detection performance of the different program analysis approach, and the influence of different input generators during static and dynamic analysis on detection performance. This book presents a novel method using an ensemble classifier scheme for detecting malicious applications, which is less susceptible to the evolution of the Android ecosystem and malware compared to previous methods. The book also introduces an ensemble multi-class classifier scheme to classify malware into known families. Furthermore, we propose a novel framework of mapping malware to vulnerabilities exploited using Android malware’s behavior reports leveraging pre-trained language models and deep learning techniques. The mapped vulnerabilities can then be assessed on confidentiality, integrity, and availability on different Android components and sub-systems, and different layers.
Book Synopsis Image-based Android Malware Detection and Classification with Convolutional Neural Networks by : Eric J. Barbin
Download or read book Image-based Android Malware Detection and Classification with Convolutional Neural Networks written by Eric J. Barbin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of machine learning for detecting and classifying malware is becoming increasingly popular amongst cybersecurity researchers. Unlike traditional methods which depend on known malware signatures and features hand-crafted by cybersecurity domain experts, machine learning techniques can perform detection and classification on previously unseen samples. With deep learning (DL) methods specifically, the manual process of feature extraction is replaced with a deep neural network (DNN) capable of performing feature learning and classification. Current research shows that techniques borrowed from the field of computer vision are particularly effective, where malware binaries are represented as images and processed through a Convolutional Neural Network (CNN) to perform classification. While this area of research is gaining interest, there are few standard datasets available and until recently, most research has been conducted against small and private datasets making it difficult to compare existing research, reproduce results, and develop new methodologies. Additionally, much of the research in this domain predominantly focuses on Microsoft Windows malware, making it difficult to significantly advance malware detection and classification research as it relates to other platforms. However, as the use of mobile devices and services continues to grow, so does the interest in developing malware for mobile platforms. Therefore, this work aims to expand current research related to image-based malware detection and classification with CNNs to achieve state-of-the-art results against a dataset comprised of malware developed for the Android operating system (OS).
Book Synopsis Deployable Machine Learning for Security Defense by : Gang Wang
Download or read book Deployable Machine Learning for Security Defense written by Gang Wang and published by Springer Nature. This book was released on 2021-09-24 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected and extended papers from the Second International Workshop on Deployable Machine Learning for Security Defense, MLHat 2021, held in August 2021. Due to the COVID-19 pandemic the conference was held online. The 6 full papers were thoroughly reviewed and selected from 7 qualified submissions. The papers are organized in topical sections on machine learning for security, and malware attack and defense.
Book Synopsis Security Vetting of Android Applications Using Graph Based Deep Learning Approaches by : Prabesh Poudel
Download or read book Security Vetting of Android Applications Using Graph Based Deep Learning Approaches written by Prabesh Poudel and published by . This book was released on 2021 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Along with the immense popularity of Android applications, the Android ecosystem is under constant threat of malware attacks. This issue warrants developing efficient tools to detect malware apps. There is a large body of work in the literature that has applied static analysis for malware detection. For instance, one popular idea has been to extract API-calls from the app code and then to use those API-calls as artifacts to train machine learning models to classify malware and benign apps. However, most of this line of work does not incorporate the true execution sequence of the API-calls, and thus misses out to capture a potentially rich signature. Furthermore, while evaluating the vetting accuracy, many of the prior work report their primary results on a randomly selected test set that are not spatially consistent (malware percentage in the test set approximating real-world scenario) and/or temporally consistent (having correct time split of train and test data) which artificially inflates the performance of the model. In this thesis, we explore if tracking the true sequence of the API-calls improves the effectiveness of the vetting process and present results ranging from testing on a random test set to a spatially and temporally consistent test set. We perform deep learning-based malware classification using a graph that we name API sequence graph which preserves the true sequence of API calls. The experiments show that our best performing model achieves AuPRC ranging from 0.977 to 0.86 and an F1-score of 0.955 to 0.83 depending on the consistency of the test set. The results show that our best-performing model, based on the true sequence of API calls, outperforms a quasi-sequence-based model.
Book Synopsis Malware Analysis and Intrusion Detection in Cyber-Physical Systems by : Shiva Darshan, S.L.
Download or read book Malware Analysis and Intrusion Detection in Cyber-Physical Systems written by Shiva Darshan, S.L. and published by IGI Global. This book was released on 2023-09-26 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many static and behavior-based malware detection methods have been developed to address malware and other cyber threats. Even though these cybersecurity systems offer good outcomes in a large dataset, they lack reliability and robustness in terms of detection. There is a critical need for relevant research on enhancing AI-based cybersecurity solutions such as malware detection and malicious behavior identification. Malware Analysis and Intrusion Detection in Cyber-Physical Systems focuses on dynamic malware analysis and its time sequence output of observed activity, including advanced machine learning and AI-based malware detection and categorization tasks in real time. Covering topics such as intrusion detection systems, low-cost manufacturing, and surveillance robots, this premier reference source is essential for cyber security professionals, computer scientists, students and educators of higher education, researchers, and academicians.
Download or read book Cyber Malware written by Iman Almomani and published by Springer Nature. This book was released on 2023-11-08 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. The book equips readers with the necessary knowledge and techniques to successfully lower the risk against emergent malware attacks. Topics cover protections against malware using machine learning algorithms, Blockchain and AI technologies, smart AI-based applications, automated detection-based AI tools, forensics tools, and much more. The authors discuss theoretical, technical, and practical issues related to cyber malware attacks and defense, making it ideal reading material for students, researchers, and developers.
Book Synopsis Malware Detection by : Priyanka Nandal
Download or read book Malware Detection written by Priyanka Nandal and published by Anchor Academic Publishing. This book was released on 2017-12 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the present work the behavior of malicious software is studied, the security challenges are understood, and an attempt is made to detect the malware behavior automatically using dynamic approach. Various classification techniques are studied. Malwares are then grouped according to these techniques and malware with unknown characteristics are clustered into an unknown group. The classifiers used in this research are k-Nearest Neighbors (kNN), J48 Decision Tree, and n-grams.
Book Synopsis Investigating Suspected Background Processes in Android Malware Classification Through Dynamic Automated Reverse Engineering and Semi-automated Debugging by : Laya Taheri
Download or read book Investigating Suspected Background Processes in Android Malware Classification Through Dynamic Automated Reverse Engineering and Semi-automated Debugging written by Laya Taheri and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Android malware detection is one of the enthusiastic research domains in recent years. Despite researchers’ admirable attempts in malware detection, malicious applications keep becoming resistant every year. Attackers develop sophisticated Apps to conceal malicious intentions on the background to be tolerant against naive malware detection methodologies.To fill the gap in the lack of background malware analysis, we present the novel 3-layered malware analysis framework. We designate the proposed framework with the assistance of automated reverse-engineering and dynamic semi-automated Debugging methods. Our APK repository samples are divided into two groups, based on the existence of particular background processes in their source files. We use two separate activation procedures that differ for each group. Here, we generate our Android malware captured dataset consisted of static features, such as permissions, Intents, and metrics and dynamic features, such as network traffic and background services. Finally, we utilize two machine learning models to evaluate our framework. We have aggregated our APK repository samples from two resources, CICAndMal2017 [30]-CICInvesAndMal2019 [39] and Android Wake Lock Research. Through the evaluation experiments of the proposed framework, we have succeeded in achieving 85% accuracy and 88% precision in classifying malware categories and benign samples with Random-Forest model.
Book Synopsis Android Malware Classification Using K-means Clustering Algorithm by : Nur Syafiqah Khalid
Download or read book Android Malware Classification Using K-means Clustering Algorithm written by Nur Syafiqah Khalid and published by . This book was released on 2016 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt:
