Analysis And Classification Of Android Malware
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Analysis And Classification Of Android Malware
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Author : Kimberly Tam
language : en
Publisher:
Release Date : 2016
Analysis And Classification Of Android Malware written by Kimberly Tam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Android Malware Classification Using Parallelized Machine Learning Methods
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Author : Lifan Xu
language : en
Publisher:
Release Date : 2016
Android Malware Classification Using Parallelized Machine Learning Methods written by Lifan Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
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.
Malware Analysis Using Artificial Intelligence And Deep Learning
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Author : Mark Stamp
language : en
Publisher: Springer Nature
Release Date : 2020-12-20
Malware Analysis Using Artificial Intelligence And Deep Learning written by Mark Stamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-20 with Computers categories.
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.
Android Malware Detection Using Machine Learning
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Author : ElMouatez Billah Karbab
language : en
Publisher: Springer Nature
Release Date : 2021-07-10
Android Malware Detection Using Machine Learning written by ElMouatez Billah Karbab and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-10 with Computers categories.
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.
The Android Malware Handbook
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Author : Qian Han
language : en
Publisher: No Starch Press
Release Date : 2023-11-07
The Android Malware Handbook written by Qian Han and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-07 with Computers categories.
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.
Mobile Forensic Investigations A Guide To Evidence Collection Analysis And Presentation
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Author : Lee Reiber
language : en
Publisher: McGraw Hill Professional
Release Date : 2015-11-22
Mobile Forensic Investigations A Guide To Evidence Collection Analysis And Presentation written by Lee Reiber and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-22 with Computers categories.
This in-depth guide reveals the art of mobile forensics investigation with comprehensive coverage of the entire mobile forensics investigation lifecycle, from evidence collection through advanced data analysis to reporting and presenting findings. Mobile Forensics Investigation: A Guide to Evidence Collection, Analysis, and Presentation leads examiners through the mobile forensics investigation process, from isolation and seizure of devices, to evidence extraction and analysis, and finally through the process of documenting and presenting findings. This book gives you not only the knowledge of how to use mobile forensics tools but also the understanding of how and what these tools are doing, enabling you to present your findings and your processes in a court of law. This holistic approach to mobile forensics, featuring the technical alongside the legal aspects of the investigation process, sets this book apart from the competition. This timely guide is a much-needed resource in today’s mobile computing landscape. Notes offer personal insights from the author's years in law enforcement Tips highlight useful mobile forensics software applications, including open source applications that anyone can use free of charge Case studies document actual cases taken from submissions to the author's podcast series Photographs demonstrate proper legal protocols, including seizure and storage of devices, and screenshots showcase mobile forensics software at work Provides you with a holistic understanding of mobile forensics
Investigating Suspected Background Processes In Android Malware Classification Through Dynamic Automated Reverse Engineering And Semi Automated Debugging
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Author : Laya Taheri
language : en
Publisher:
Release Date : 2020
Investigating Suspected Background Processes In Android Malware Classification Through Dynamic Automated Reverse Engineering And Semi Automated Debugging written by Laya Taheri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
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.
Android Malware Detection And Adversarial Methods
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Author : Weina Niu
language : en
Publisher: Springer Nature
Release Date : 2024-05-23
Android Malware Detection And Adversarial Methods written by Weina Niu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-23 with Computers categories.
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.
Android Malware Detection And Forensics Based On Api Calls
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Author : Arpitaben Shah
language : en
Publisher:
Release Date : 2016
Android Malware Detection And Forensics Based On Api Calls written by Arpitaben Shah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
In recent world, mobile devices play an important role towards immense information sharing. As mobile smartphones become more widespread and powerful, they store more personal data and may leak it carelessly or maliciously. Research shows that Android is widely used operating system among many smartphones. The growth of Android users infatuates attackers to target more Android smartphone devices by using malicious software. To defend against expansion of Android malwares, researchers propose many analysis, detection and classification techniques. This paper introduces a dynamic analysis approach to intercept API calls at runtime, extract logs, and analyze them. It helps to understand runtime behavior of installed applications and use of API calls for malicious purpose. By using this method, analysts may get to know if the application is benign or malicious by comparing its actual behavior and expected behavior. This research will offer essential help to malware researchers to quickly understand the activities and internal workings of unknown applications.
Metadroid
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Author : Yan Li
language : en
Publisher:
Release Date : 2016
Metadroid written by Yan Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with G1 (Smartphone) categories.
The Android system is the most widely used mobile system in the world and the user requirement is still increasing. According to [37], Android dominated the market with a 79.8% in 2013 Q2, 84.8% in 2014 Q2 and 82.8% share in 2015 Q2. Compared to other mobile systems, Android dominates most of the market and has the largest number of mobile users. Based on the research work [44] from PulseSecure.net, the number of new Android malware samples have been dramatically increased from 3809 in 2011, 214327 in 2012, 1192035 in 2013, 1548129 in 2014 and in the 2015 Q1 the new malicious sample is 440267. The approximate total value during the entire year of 2015 was a greater total than 2014. With that in mind, malware has always been the most pressing concern for the mobile application market. There are a number of analysis tools and architectures used for malware detection including static analysis, dynamic analysis, sandbox analysis and manually dissection. Consequently, all of the analysis approaches are time consuming. In order to effectively use our limited time and human resources, we present a lightweight pre-filtering tool(METADroid) which could be used to pre-classify the apps before the more expensive traditional static, dynamic analysis. The system includes whole 381 features and we analyzed their effectiveness for triaging. It will give you feedback of each apk in a short period of time and provide valuable prediction. Our experiments based on more than 158000 Android Applications collected from 8 markets around the globe.