Android Malware Detection and Adversarial Methods
Author | : Weina Niu |
Publisher | : Springer Nature |
Total Pages | : 197 |
Release | : |
Genre | : |
ISBN | : 9819714591 |
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Author | : Weina Niu |
Publisher | : Springer Nature |
Total Pages | : 197 |
Release | : |
Genre | : |
ISBN | : 9819714591 |
Author | : Sapna Malik |
Publisher | : Anchor Academic Publishing |
Total Pages | : 48 |
Release | : 2017-12 |
Genre | : Computers |
ISBN | : 3960672047 |
The smartphone has rapidly become an extremely prevalent computing platform, with just over 115 million devices sold in the third quarter of 2011, a 15% increase over the 100 million devices sold in the first quarter of 2011, and a 111% increase over the 54 million devices sold in the first quarter of 2010. Android in particular has seen even more impressive growth, with the devices sold in the third quarter of 2011 (60.5 million) almost triple the devices sold in the third quarter of 2010 (20.5 million), and an associated doubling of market share. This popularity has not gone unnoticed by malware authors. Despite the rapid growth of the Android platform, there are already well-documented cases of Android malware, such as DroidDream, which was discovered in over 50 applications on the official Android market in March 2011. Furthermore, it is found that Android’s built-in security features are largely insufficient, and that even non malicious programs can (unintentionally) expose confidential information. A study of 204,040 Android applications conducted in 2011 found 211 malicious applications on the official Android market and alternative marketplaces. The problem of using a machine learning-based classifier to detect malware presents the challenge: Given an application, we must extract some sort of feature representation of the application. To address this problem, we extract a heterogeneous feature set, and process each feature independently using multiple kernels.We train a One-Class Support Vector Machine using the feature set we get to classify the application as a benign or malware accordingly.
Author | : Weina Niu |
Publisher | : Springer |
Total Pages | : 0 |
Release | : 2024-05-19 |
Genre | : Computers |
ISBN | : 9789819714582 |
The rise of Android malware poses a significant threat to users’ information security and privacy. Malicious software can inflict severe harm on users by employing various tactics, including deception, personal information theft, and device control. To address this issue, both academia and industry are continually engaged in research and development efforts focused on detecting and countering Android malware. This book is a comprehensive academic monograph crafted against this backdrop. The publication meticulously explores the background, methods, adversarial approaches, and future trends related to Android malware. It is organized into four parts: the overview of Android malware detection, the general Android malware detection method, the adversarial method for Android malware detection, and the future trends of Android malware detection. Within these sections, the book elucidates associated issues, principles, and highlights notable research. By engaging with this book, readers will gain not only a global perspective on Android malware detection and adversarial methods but also a detailed understanding of the taxonomy and general methods outlined in each part. The publication illustrates both the overarching model and representative academic work, facilitating a profound comprehension of Android malware detection.
Author | : ElMouatez Billah Karbab |
Publisher | : Springer Nature |
Total Pages | : 212 |
Release | : 2021-07-10 |
Genre | : Computers |
ISBN | : 303074664X |
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.
Author | : Husnain Rafiq |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : |
ISBN | : |
Author | : Weina Niu |
Publisher | : Springer Nature |
Total Pages | : 197 |
Release | : |
Genre | : |
ISBN | : 9819714591 |
Author | : Tony Thomas |
Publisher | : CRC Press |
Total Pages | : 191 |
Release | : 2022-12-30 |
Genre | : Computers |
ISBN | : 1000824977 |
The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities. The attacks, which escalated after the COVID-19 pandemic, proved there is great importance in protecting Android mobile devices from malware attacks. Intelligent Mobile Malware Detection will teach users how to develop intelligent Android malware detection mechanisms by using various graph and stochastic models. The book begins with an introduction to the Android operating system accompanied by the limitations of the state-of-the-art static malware detection mechanisms as well as a detailed presentation of a hybrid malware detection mechanism. The text then presents four different system call-based dynamic Android malware detection mechanisms using graph centrality measures, graph signal processing and graph convolutional networks. Further, the text shows how most of the Android malware can be detected by checking the presence of a unique subsequence of system calls in its system call sequence. All the malware detection mechanisms presented in the book are based on the authors' recent research. The experiments are conducted with the latest Android malware samples, and the malware samples are collected from public repositories. The source codes are also provided for easy implementation of the mechanisms. This book will be highly useful to Android malware researchers, developers, students and cyber security professionals to explore and build defense mechanisms against the ever-evolving Android malware.
Author | : Lifan Xu |
Publisher | : |
Total Pages | : 132 |
Release | : 2016 |
Genre | : |
ISBN | : 9781369115284 |
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.
Author | : Shivi Garg |
Publisher | : CRC Press |
Total Pages | : 190 |
Release | : 2023-08-17 |
Genre | : Computers |
ISBN | : 1000924467 |
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.
Author | : Shiva Darshan, S.L. |
Publisher | : IGI Global |
Total Pages | : 451 |
Release | : 2023-09-26 |
Genre | : Computers |
ISBN | : 1668486687 |
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.