Android Malware Detection And Classification Using Machine Learning Techniques
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Android Malware Detection And Classification Using Machine Learning Techniques
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Author : Satyajit Padalkar
language : en
Publisher:
Release Date : 2014
Android Malware Detection And Classification Using Machine Learning Techniques written by Satyajit Padalkar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
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.
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.
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.
Image Based Android Malware Detection And Classification With Convolutional Neural Networks
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Author : Eric J. Barbin
language : en
Publisher:
Release Date : 2023
Image Based Android Malware Detection And Classification With Convolutional Neural Networks written by Eric J. Barbin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Android (Electronic resource) categories.
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).
Challenges In Information Communication And Computing Technology
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Author : V. Sharmila
language : en
Publisher: CRC Press
Release Date : 2024-12-10
Challenges In Information Communication And Computing Technology written by V. Sharmila and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-10 with Computers categories.
This book explores the critical challenges and emerging trends in Information, Communication, and Computing Technology (ICCT). It provides a comprehensive overview of the key issues facing these rapidly evolving fields, from data security and privacy to advancements in artificial intelligence, communication networks, and quantum computing. Through in-depth analysis and expert perspectives, this volume aims to shed light on the complexities of ICCT and offer innovative solutions for researchers, practitioners, and students. Building on its exploration of challenges in ICCT, this book delves into several core areas. These include the development and deployment of secure and efficient communication networks, the ethical implications and technical hurdles of artificial intelligence and machine learning, and the promise and complexity of quantum computing. The book also addresses the management of big data, highlighting both its potential and the challenges of ensuring data privacy and security. Additionally, it examines the role of sustainability in computing, advocating for greener technologies and practices. The findings presented in this volume emphasize the need for interdisciplinary approaches and innovative thinking to address these challenges, offering insights that are both practical and forward-looking. This book is intended for a diverse audience that includes researchers, practitioners, and students in the fields of Information, Communication, and Computing Technology (ICCT). It is particularly valuable for academics and professionals seeking to deepen their understanding of current challenges and emerging trends in these areas. Additionally, policymakers, industry leaders, and technologists will find the book's insights useful for informing decisions and strategies in the development and implementation of advanced technologies. Whether you are a seasoned expert or a newcomer to the field, this book provides valuable perspectives that can enhance your knowledge and contribute to your work in ICCT. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND)] 4.0 license.
Frontiers In Ai And Computational Technologies
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Author : Ahmed J. Obaid
language : en
Publisher: Springer Nature
Release Date : 2025-05-19
Frontiers In Ai And Computational Technologies written by Ahmed J. Obaid and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-19 with Computers categories.
This book presents recent research on the application of Artificial Intelligence and Computational Technologies, as discussed at the 2nd International Conference on Emerging Trends in AI and Computational Technologies. The contributions in this volume highlight the advancements in AI fields and computational technologies such as machine learning, generative intelligence, and language-based models and illustrate their transformative impact on their applications. This proceeding, comprising many techniques that focus on using AI in many fields such as Climate Change and Biodiversity Conservation, Sustainability and Environmental Impact, Education and Learning Techniques, Smart City Planning and Management, and Agriculture for Sustainable Food Production, also introduces innovative methodologies and presents the findings derived from their original research. Theoretical and empirical studies featured in this book employ a range of computational techniques, including deep learning frameworks, data-driven analysis, and optimization algorithms. These approaches enhance the performance and efficiency of various applications in sectors like healthcare, finance, and entertainment. Additionally, the book explores the ethical implications of deploying AI technologies and provides strategies to navigate the associated risks. The chapters discuss essential topics such as methodology, validation, and the implications of AI in real-world scenarios, making this book a vital resource for understanding and leveraging the power of Artificial Intelligence and Computational Technologies in contemporary applications.
Recent Advances In Material Manufacturing And Machine Learning
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Author : Rajiv Gupta
language : en
Publisher: CRC Press
Release Date : 2023-05-26
Recent Advances In Material Manufacturing And Machine Learning written by Rajiv Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-26 with Technology & Engineering categories.
The role of manufacturing in a country’s economy and societal development has long been established through their wealth generating capabilities. To enhance and widen our knowledge of materials and to increase innovation and responsiveness to ever-increasing international needs, more in-depth studies of functionally graded materials/tailor-made materials, recent advancements in manufacturing processes and new design philosophies are needed at present. The objective of this volume is to bring together experts from academic institutions, industries and research organizations and professional engineers for sharing of knowledge, expertise and experience in the emerging trends related to design, advanced materials processing and characterization, and advanced manufacturing processes.
Emerging Technologies In Computing
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Author : Mahdi H. Miraz
language : en
Publisher: Springer Nature
Release Date : 2021-11-03
Emerging Technologies In Computing written by Mahdi H. Miraz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-03 with Computers categories.
This book constitutes the refereed conference proceedings of the 4th International Conference on Emerging Technologies in Computing, iCEtiC 2021, held in August 2021. Due to VOVID-19 pandemic the conference was helt virtually. The 15 revised full papers were reviewed and selected from 44 submissions and are organized in topical sections covering Information and Network Security; Cloud, IoT and Distributed Computing; AI, Expert Systems and Big Data Analytics
Computing Science Communication And Security
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Author : Nirbhay Chaubey
language : en
Publisher: Springer Nature
Release Date : 2022-07-01
Computing Science Communication And Security written by Nirbhay Chaubey and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-01 with Computers categories.
This book constitutes revised selected papers of the Third International Conference on Computing Science, Communication and Security, COMS2 2022, held in Gandhinagar, India, in February 2022. Due to the COVID-19 pandemic the conference was held virtually. The 22 full papers were thoroughly reveiwed and selected from 143 submissions. The papers present ideas, and research results on the aspects of computing science, network communication, and security.