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Android Malware Detection Through Permission And App Component Analysis Using Machine Learning Algorithms
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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 Malware Detection by : Mihai Christodorescu
Download or read book Malware Detection written by Mihai Christodorescu and published by Springer Science & Business Media. This book was released on 2007-03-06 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.
Book Synopsis Security in Computer and Information Sciences by : Erol Gelenbe
Download or read book Security in Computer and Information Sciences written by Erol Gelenbe and published by Springer. This book was released on 2018-07-13 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the thoroughly refereed proceedings of the First International ISCIS Security Workshop 2018, Euro-CYBERSEC 2018, held in London, UK, in February 2018. The 12 full papers presented together with an overview paper were carefully reviewed and selected from 31 submissions. Security of distributed interconnected systems, software systems, and the Internet of Things has become a crucial aspect of the performance of computer systems. The papers deal with these issues, with a specific focus on societally critical systems such as health informatics systems, the Internet of Things, energy systems, digital cities, digital economy, mobile networks, and the underlying physical and network infrastructures.
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:
Download or read book Android Malware written by Xuxian Jiang and published by Springer Science & Business Media. This book was released on 2013-06-13 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile devices, such as smart phones, have achieved computing and networking capabilities comparable to traditional personal computers. Their successful consumerization has also become a source of pain for adopting users and organizations. In particular, the widespread presence of information-stealing applications and other types of mobile malware raises substantial security and privacy concerns. Android Malware presents a systematic view on state-of-the-art mobile malware that targets the popular Android mobile platform. Covering key topics like the Android malware history, malware behavior and classification, as well as, possible defense techniques.
Book Synopsis Android Malware Prediction by Permission Analysis and Data Mining by :
Download or read book Android Malware Prediction by Permission Analysis and Data Mining written by and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, smartphones have brought people's lives to a new high level. Smartphone applications, or Apps, are accelerating the process with many more functions getting developed, such as browsing the Internet, making payments, taking photos and share. However, the "Apps" are bringing potential vulnerability when they access private information from the phones, and mobile security has never been so much focused on like today. In this paper, we presented a novel Android Permission based malware detection technique. We first gather a huge set of both malware and benign Apps through web clawer and develop a tool to decompile Apps to source code and manifest files automatically. Then permissions with other information are extracted for each App, making up to a raw data set. Afterward, we apply data cleaning, dimension reduction and statical analysis to the raw data set. We find that the distribution of permissions for Apps shares a difference between malware dataset and benign dataset. Finally, we take advantage of machine learning algorithms, including Logistic Regression Model, Tree Model with Ensemble techniques, Neural Network and finally an ensemble model to find patterns and more valuable information. Other models are also discussed. Extended experiments using these various machine learning models are conducted in the end. From the results, we can see that our method generates a good accuracy, F-score and overall performance of malicious App prediction.
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 The Android Malware Handbook by : Qian Han
Download or read book The Android Malware Handbook written by Qian Han and published by No Starch Press. This book was released on 2023-11-07 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by machine-learning researchers and members of the Android Security team, this all-star guide tackles the analysis and detection of malware that targets the Android operating system. This groundbreaking guide to Android malware distills years of research by machine learning experts in academia and members of Meta and Google’s Android Security teams into a comprehensive introduction to detecting common threats facing the Android eco-system today. Explore the history of Android malware in the wild since the operating system first launched and then practice static and dynamic approaches to analyzing real malware specimens. Next, examine machine learning techniques that can be used to detect malicious apps, the types of classification models that defenders can implement to achieve these detections, and the various malware features that can be used as input to these models. Adapt these machine learning strategies to the identifica-tion of malware categories like banking trojans, ransomware, and SMS fraud. You’ll: Dive deep into the source code of real malware Explore the static, dynamic, and complex features you can extract from malware for analysis Master the machine learning algorithms useful for malware detection Survey the efficacy of machine learning techniques at detecting common Android malware categories The Android Malware Handbook’s team of expert authors will guide you through the Android threat landscape and prepare you for the next wave of malware to come.
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 Android Malware and Analysis by : Ken Dunham
Download or read book Android Malware and Analysis written by Ken Dunham and published by CRC Press. This book was released on 2014-10-24 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid growth and development of Android-based devices has resulted in a wealth of sensitive information on mobile devices that offer minimal malware protection. This has created an immediate need for security professionals that understand how to best approach the subject of Android malware threats and analysis.In Android Malware and Analysis, K
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 150 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 Proceedings of ICRIC 2019 by : Pradeep Kumar Singh
Download or read book Proceedings of ICRIC 2019 written by Pradeep Kumar Singh and published by Springer Nature. This book was released on 2019-11-21 with total page 897 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents high-quality, original contributions (both theoretical and experimental) on software engineering, cloud computing, computer networks & internet technologies, artificial intelligence, information security, and database and distributed computing. It gathers papers presented at ICRIC 2019, the 2nd International Conference on Recent Innovations in Computing, which was held in Jammu, India, in March 2019. This conference series represents a targeted response to the growing need for research that reports on and assesses the practical implications of IoT and network technologies, AI and machine learning, cloud-based e-Learning and big data, security and privacy, image processing and computer vision, and next-generation computing technologies.
Book Synopsis Intelligent Systems Design and Applications by : Ajith Abraham
Download or read book Intelligent Systems Design and Applications written by Ajith Abraham and published by Springer. This book was released on 2019-04-13 with total page 1114 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent research on Intelligent Systems and Nature Inspired Computing. It presents 212 selected papers from the 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) and the 10th World Congress on Nature and Biologically Inspired Computing (NaBIC), which was held at VIT University, India. ISDA-NaBIC 2018 was a premier conference in the field of Computational Intelligence and brought together researchers, engineers and practitioners whose work involved intelligent systems and their applications in industry and the “real world.” Including contributions by authors from over 40 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
Book Synopsis Computer Security -- ESORICS 2012 by : Sara Foresti
Download or read book Computer Security -- ESORICS 2012 written by Sara Foresti and published by Springer. This book was released on 2012-08-19 with total page 911 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th European Symposium on Computer Security, ESORICS 2012, held in Pisa, Italy, in September 2012. The 50 papers included in the book were carefully reviewed and selected from 248 papers. The articles are organized in topical sections on security and data protection in real systems; formal models for cryptography and access control; security and privacy in mobile and wireless networks; counteracting man-in-the-middle attacks; network security; users privacy and anonymity; location privacy; voting protocols and anonymous communication; private computation in cloud systems; formal security models; identity based encryption and group signature; authentication; encryption key and password security; malware and phishing; and software security.
Book Synopsis Significant Permission Identification for Android Malware Detection by : Lichao Sun
Download or read book Significant Permission Identification for Android Malware Detection written by Lichao Sun and published by . This book was released on 2016 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: A recent report indicates that a newly developed malicious app for Android is introduced every 11 seconds. To combat this alarming rate of malware creation, we need a scalable malware detection approach that is effective and efficient. In this thesis, we introduce SigPID, a malware detection system based on permission analysis to cope with the rapid increase in the number of Android malware. Instead of analyzing all 135 Android permissions, our approach applies 3-level pruning by mining the permission data to identify only significant permissions that can be effective in distinguishing benign and malicious apps. Based on the identified significant permissions, SigPID utilizes classification algorithms to classify different families of malware and benign apps. Our evaluation finds that only 25% of permissions (34 out of 135 permissions) are significant. We then compare the performance of our approach, using only 25% of all permissions, against a baseline approach that analyzes all permissions. The results indicate that when Support Vector Machine (SVM) is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which are about the same as those produced by the baseline approach. We also show that SigPID is effective when used with 67 other commonly used supervised learning approaches. We find that 55 out of 67 algorithms can achieve F-measure of at least 85%, while the average running time can be reduced by 85.6\% compared with the baseline approach. When we compare the detection effectiveness of SigPID to those of other approaches, SigPID can detect 96.54% of malware in the data set while other approaches detect 3.99% to 96.41%.
Book Synopsis International Joint Conference CISIS’12-ICEUTE ́12-SOCO ́12 Special Sessions by : Álvaro Herrero
Download or read book International Joint Conference CISIS’12-ICEUTE ́12-SOCO ́12 Special Sessions written by Álvaro Herrero and published by Springer Science & Business Media. This book was released on 2012-08-23 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at CISIS 2012 and ICEUTE 2012, both conferences held in the beautiful and historic city of Ostrava (Czech Republic), in September 2012. CISIS aims to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a through peer-review process, the CISIS 2012 International Program Committee selected 30 papers which are published in these conference proceedings achieving an acceptance rate of 40%. In the case of ICEUTE 2012, the International Program Committee selected 4 papers which are published in these conference proceedings. The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the CISIS and ICEUTE conferences would not exist without their help.
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.
