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Convolutional Neural Networks For Malware Classification
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Book Synopsis Convolutional Neural Networks for Malware Classification by : Daniel Gibert Llauradó
Download or read book Convolutional Neural Networks for Malware Classification written by Daniel Gibert Llauradó and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: According to AV vendors malicious software has been growing exponentially last years. One of the main reasons for these high volumes is that in order to evade detection, malware authors started using polymorphic and metamorphic techniques. As a result, traditional signature-based approaches to detect malware are being insufficient against new malware and the categorization of malware samples had become essential to know the basis of the behavior of malware and to fight back cybercriminals. During the last decade, solutions that fight against malicious software had begun using machine learning approaches. Unfortunately, there are few opensource datasets available for the academic community. One of the biggest datasets available was released last year in a competition hosted on Kaggle with data provided by Microsoft for the Big Data Innovators Gathering (BIG 2015). This thesis presents two novel and scalable approaches using Convolutional Neural Networks (CNNs) to assign malware to its corresponding family. On one hand, the first approach makes use of CNNs to learn a feature hierarchy to discriminate among samples of malware represented as gray-scale images. On the other hand, the second approach uses the CNN architecture introduced by Yoon Kim [12] to classify malware samples according their x86 instructions. The proposed methods achieved an improvement of 93.86% and 98,56% with respect to the equal probability benchmark.
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 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 Artificial Intelligence Techniques for Advanced Computing Applications by : D. Jude Hemanth
Download or read book Artificial Intelligence Techniques for Advanced Computing Applications written by D. Jude Hemanth and published by Springer Nature. This book was released on 2020-07-23 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features a collection of high-quality research papers presented at the International Conference on Advanced Computing Technology (ICACT 2020), held at the SRM Institute of Science and Technology, Chennai, India, on 23–24 January 2020. It covers the areas of computational intelligence, artificial intelligence, machine learning, deep learning, big data, and applications of artificial intelligence in networking, IoT and bioinformatics
Book Synopsis Data Engineering and Intelligent Computing by : Vikrant Bhateja
Download or read book Data Engineering and Intelligent Computing written by Vikrant Bhateja and published by Springer Nature. This book was released on 2021-05-04 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features a collection of high-quality, peer-reviewed papers presented at the Fourth International Conference on Intelligent Computing and Communication (ICICC 2020) organized by the Department of Computer Science and Engineering and the Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India, on 18–20 September 2020. The book is organized in two volumes and discusses advanced and multi-disciplinary research regarding the design of smart computing and informatics. It focuses on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide practical solutions to a number of problems in society, the environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.
Book Synopsis Advances in Computer Science and Ubiquitous Computing by : James J. Park
Download or read book Advances in Computer Science and Ubiquitous Computing written by James J. Park and published by Springer. This book was released on 2017-12-19 with total page 1520 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the combined proceedings of the 12th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2017) and the 9th International Conference on Computer Science and its Applications (CSA2017), both held in Taichung, Taiwan, December 18 - 20, 2017. The aim of these two meetings was to promote discussion and interaction among academics, researchers and professionals in the field of ubiquitous computing technologies. These proceedings reflect the state of the art in the development of computational methods, involving theory, algorithms, numerical simulation, error and uncertainty analysis and novel applications of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing. James J. (Jong Hyuk) Park received Ph.D. degrees in Graduate School of Information Security from Korea University, Korea and Graduate School of Human Sciences from Waseda University, Japan. From December, 2002 to July, 2007, Dr. Park had been a research scientist of R&D Institute, Hanwha S&C Co., Ltd., Korea. From September, 2007 to August, 2009, He had been a professor at the Department of Computer Science and Engineering, Kyungnam University, Korea. He is now a professor at the Department of Computer Science and Engineering and Department of Interdisciplinary Bio IT Materials, Seoul National University of Science and Technology (SeoulTech), Korea. Dr. Park has published about 200 research papers in international journals and conferences. He has been serving as chair, program committee, or organizing committee chair for many international conferences and workshops. He is a steering chair of international conferences – MUE, FutureTech, CSA, CUTE, UCAWSN, World IT Congress-Jeju. He is editor-in-chief of Human-centric Computing and Information Sciences (HCIS) by Springer, The Journal of Information Processing Systems (JIPS) by KIPS, and Journal of Convergence (JoC) by KIPS CSWRG. He is Associate Editor / Editor of 14 international journals including JoS, JNCA, SCN, CJ, and so on. In addition, he has been serving as a Guest Editor for international journals by some publishers: Springer, Elsevier, John Wiley, Oxford Univ. press, Emerald, Inderscience, MDPI. He got the best paper awards from ISA-08 and ITCS-11 conferences and the outstanding leadership awards from IEEE HPCC-09, ICA3PP-10, IEE ISPA-11, PDCAT-11, IEEE AINA-15. Furthermore, he got the outstanding research awards from the SeoulTech, 2014. His research interests include IoT, Human-centric Ubiquitous Computing, Information Security, Digital Forensics, Vehicular Cloud Computing, Multimedia Computing, etc. He is a member of the IEEE, IEEE Computer Society, KIPS, and KMMS. Vincenzo Loia (BS ‘85, MS ‘87, PhD ‘89) is Full Professor of Computer Science. His research interests include Intelligent Agents, Ambient intelligence, Computational Intelligence. Currently he is Founder & Editor-in-chief of “Ambient Intelligence and Humanized Computing”, and Co-Editor-in-Chief of “Softcomputing”, Springer-Verlag. He is Chair of the Task Forces “Intelligent Agents” and “Ambient Intelligence” IEEE CIS ETTC. He has been Chair the Emergent Technical Committe "Emergent Technology", IEEE CIS Society and Vice-Chair of Intelligent Systems Applications Technical Committee. He has been author of more than 200 scientific works, Editor/co-editor of 4 Books, 64 journal papers, 25 book chapters, and 100 conference papers. He is Senior member of the IEEE, Associate Editor of IEEE Transactions on Industrial Informatics, and Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems. Many times reviewers for national and international projects, Dr. Loia is active in the research domain of agents, ambient intelligence, computational intelligence, smartgrids, distributed platform for enrich added value. Gangman Yi in Computer Sciences at Texas A&M University, USA in 2007, and doctorate in Computer Sciences at Texas A&M University, USA in 2011. In May 2011, he joined System S/W group in Samsung Electronics, Suwon, Korea. He joined the Department of Computer Science & Engineering, Gangneung-Wonju National University, Korea, since March 2012. Dr. Yi has been researched in an interdisciplinary field of researches. His research focuses especially on the development of computational methods to improve understanding of biological systems and its big data. Dr. Yi actively serves as a managing editor and reviewer for international journals, and chair of international conferences and workshops. Yunsick Sung received his B.S. degree in division of electrical and computer engineering from Pusan National University, Busan, Korea, in 2004, his M.S. degree in computer engineering from Dongguk University, Seoul, Korea, in 2006, and his Ph.D. degree in game engineering from Dongguk University, Seoul, Korea, in 2012. He was employed as a member of the researcher at Samsung Electronics between 2006 and 2009. He was the plural professor at Shinheung College in 2009 and at Dongguk University in 2010. His main research interests are many topics in brain-computer Interface, programming by demonstration, ubiquitous computing and reinforcement learning. His Journal Service Experiences is Associate Editor at Human-centric Computing and Information Sciences, Springer (2015- Current).
Book Synopsis Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation by : Suresh Balusamy
Download or read book Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation written by Suresh Balusamy and published by Springer Nature. This book was released on 2020-10-27 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 4th International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3 2019, which was held in Coimbatore, India, in December 2019. The 9 papers presented in this volume were carefully reviewed and selected from 38 submissions. They were organized in topical sections named: computational intelligence; cyber security; and computational models.
Book Synopsis Deep Learning Applications for Cyber Security by : Mamoun Alazab
Download or read book Deep Learning Applications for Cyber Security written by Mamoun Alazab and published by Springer. This book was released on 2019-08-14 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Book Synopsis Guide to Convolutional Neural Networks by : Hamed Habibi Aghdam
Download or read book Guide to Convolutional Neural Networks written by Hamed Habibi Aghdam and published by Springer. This book was released on 2017-05-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.
Book Synopsis Mastering Machine Learning for Penetration Testing by : Chiheb Chebbi
Download or read book Mastering Machine Learning for Penetration Testing written by Chiheb Chebbi and published by Packt Publishing Ltd. This book was released on 2018-06-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Download or read book Cyber Security written by Xiaochun Yun and published by Springer. This book was released on 2019-01-01 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the refereed proceedings of the 15th International Annual Conference on Cyber Security, CNCERT 2018, held in Beijing, China, in August 2018. The 14 full papers presented were carefully reviewed and selected from 53 submissions. The papers cover the following topics: emergency response, mobile internet security, IoT security, cloud security, threat intelligence analysis, vulnerability, artificial intelligence security, IPv6 risk research, cybersecurity policy and regulation research, big data analysis and industrial security.
Book Synopsis Applications of Artificial Intelligence for Smart Technology by : Swarnalatha, P.
Download or read book Applications of Artificial Intelligence for Smart Technology written by Swarnalatha, P. and published by IGI Global. This book was released on 2020-10-30 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: As global communities are attempting to transform into more efficient and technologically-advanced metropolises, artificial intelligence (AI) has taken a firm grasp on various professional fields. Technology used in these industries is transforming by introducing intelligent techniques including machine learning, cognitive computing, and computer vision. This has raised significant attention among researchers and practitioners on the specific impact that these smart technologies have and what challenges remain. Applications of Artificial Intelligence for Smart Technology is a pivotal reference source that provides vital research on the implementation of advanced technological techniques in professional industries through the use of AI. While highlighting topics such as pattern recognition, computational imaging, and machine learning, this publication explores challenges that various fields currently face when applying these technologies and examines the future uses of AI. This book is ideally designed for researchers, developers, managers, academicians, analysts, students, and practitioners seeking current research on the involvement of AI in professional practices.
Book Synopsis Confluence of AI, Machine, and Deep Learning in Cyber Forensics by : Misra, Sanjay
Download or read book Confluence of AI, Machine, and Deep Learning in Cyber Forensics written by Misra, Sanjay and published by IGI Global. This book was released on 2020-12-18 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.
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 Advanced Computing and Intelligent Technologies by : Monica Bianchini
Download or read book Advanced Computing and Intelligent Technologies written by Monica Bianchini and published by Springer Nature. This book was released on 2021-07-21 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2021) held at NCR New Delhi, India, during March 20–21, 2021, jointly organized by Galgotias University, India, and Department of Information Engineering and Mathematics Università Di Siena, Italy. It discusses emerging topics pertaining to advanced computing, intelligent technologies, and networks including AI and machine learning, data mining, big data analytics, high-performance computing network performance analysis, Internet of things networks, wireless sensor networks, and others. The book offers a valuable asset for researchers from both academia and industries involved in advanced studies.
Book Synopsis Deep Convolutional Neural Networks for the Classification of the EMBER Malware Dataset by : Anudeep Nallamothu
Download or read book Deep Convolutional Neural Networks for the Classification of the EMBER Malware Dataset written by Anudeep Nallamothu and published by . This book was released on 2018 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growing number of computer users across the world, security issues are growing exponentially. There is an imbalance in the pace of growing security issues and companies coming up with solutions. In May 2017, more than 400,000 computer systems in Telefonia and UK's National Health System were attacked by WannaCry Malware. Attackers and malware developers are using advanced malware techniques and vulnerabilities in the operating system to gain control over the victim's computer. They are coming up with new techniques and strategies to hide the malicious code and infect the targets. Anti-Virus scanners help to solve the detection of malware to some extent, but they fail to function when a new class of malware is presented. Therefore, we need a method of automating malware detection. So we are trying to apply a machine learning technique called Convolutional Neural Networks (CNNs) to accomplish the goal of automating malware detection. In recent years, applying machine learning to malware data has drawn much attention. In the past, researchers have used CNNs on malware binaries (Nataraj et al. 2011) and malware windows PE files. In this thesis, the CNN technique is applied to statistically extracted features from Windows Malware PE files. We use the EMBER labeled benchmark dataset in this work. Results show that our model outperforms the LightGBM and MalConv models.
Book Synopsis Artificial Intelligence for Cybersecurity by : Mark Stamp
Download or read book Artificial Intelligence for Cybersecurity written by Mark Stamp and published by Springer Nature. This book was released on 2022-07-15 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.
