Improving The Effectiveness Of Automatic Dynamic Android Malware Analysis PDF Download
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Author | : 沈穎志 |
Publisher | : |
Total Pages | : |
Release | : 2013 |
Genre | : |
ISBN | : |
Download Improving the Effectiveness of Automatic Dynamic Android Malware Analysis Book in PDF, ePub and Kindle
Author | : Leonardo De La Rosa |
Publisher | : |
Total Pages | : 118 |
Release | : 2018 |
Genre | : |
ISBN | : 9780438595927 |
Download Improving the Effectiveness and Efficiency of Dynamic Malware Analysis Using Machine Learning Book in PDF, ePub and Kindle
The malware threat landscape is constantly evolving, with upwards of one million new variants being released every day. Traditional approaches for detecting and classifying malware usually contain brittle handcrafted heuristics that quickly become outdated and can be exploited by nefarious actors. As a result, it is necessary to change the way software security is managed by using advanced analytics (i.e., machine learning) and significantly more automation to develop adaptable malware analysis engines that correctly identify, categorize, and characterize malware. ☐ In this dissertation, we introduce a next-generation sandbox that leverages machine learning to create an adaptive malware analysis platform. This intelligent environment considerably extends the capabilities of Cuckoo, an open-source malware analysis sandbox, and significantly optimizes the resources dedicated to the dynamic analysis of malware. ☐ Dynamic analysis allows security analysts to collect information about the behavior of malicious samples in an isolated environment. However, running malware in a sandbox is time-consuming and computationally expensive. This technique extracts information from malware without executing it and is orders of magnitude faster than dynamic analysis. Nevertheless, for some malware it may still be necessary to use dynamic-based features to produce better classifications and characterizations. ☐ With our system, we were successful in identifying the simplest characterizations required to accurately classify malware. This is an important feature because it allows us to determine the subset of samples that is truly different, and requires very expensive dynamic characterization. When dynamic analysis is imperative, our system also estimates the minimum amount of time required to accurately detect and classify malware. As a result, our intelligent analysis platform can reallocate the time saved to analyzing files that require longer execution times and produce actionable intelligence for our system. Finally, by leveraging the speed of static analysis, our system induces highly accurate machine learning models for malware capability detection, removing the need to perform dynamic analysis to identify high-level functionalities of malicious code.
Author | : Mihai Christodorescu |
Publisher | : Springer Science & Business Media |
Total Pages | : 307 |
Release | : 2007-03-06 |
Genre | : Computers |
ISBN | : 0387445994 |
Download Malware Detection Book in PDF, ePub and Kindle
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.
Author | : ElMouatez Billah Karbab |
Publisher | : Springer Nature |
Total Pages | : 212 |
Release | : 2021-07-10 |
Genre | : Computers |
ISBN | : 303074664X |
Download Android Malware Detection using Machine Learning Book in PDF, ePub and Kindle
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 | : Xuxian Jiang |
Publisher | : Springer Science & Business Media |
Total Pages | : 50 |
Release | : 2013-06-13 |
Genre | : Computers |
ISBN | : 1461473942 |
Download Android Malware Book in PDF, ePub and Kindle
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.
Author | : Utkarsh Raghav |
Publisher | : |
Total Pages | : 0 |
Release | : 2023 |
Genre | : |
ISBN | : |
Download Static Analysis for Android Malware Detection Using Document Vectors Book in PDF, ePub and Kindle
The prevalence of smart mobile devices has led to an upsurge in malware that targets mobile platforms. The dominant market player in the sector, Android OS, has been a favourite target for malicious actors. Various feature engineering techniques are used in the current machine learning and deep learning approaches for Android malware detection. In order to correctly identify dependable features, feature engineering for Android malware detection using multiple AI algorithms requires a particular level of expertise in Android malware and the platform itself. The majority of these engineered features are initially extracted by applying different static and dynamic analysis approaches. These allow researchers to obtain various types of information from Android application packages (APKs), such as required permissions, opcode sequences and control flow graphs, to name a few. This information is used (as is or in vectorised form) for training supervised learning models. Researchers have also applied Natural Language Processing techniques to the features extracted from APKs. In order to automatically create feature vectors that can describe the data included in Android manifests and Dalvik executable files inside an APK, this study focused on developing a novel method that uses static analysis and the NLP technique of document embeddings. We designed a system that takes Android APK files as input documents and generates the feature embeddings. This system removes the need for manual identification & extraction of features. We use these embeddings to train various Android Malware detection models to experimentally evaluate the effectiveness of these automatically generated features. The experiments were done by training and evaluating 5 different supervised learning models. We did our experiments on APKs from two well-known datasets, DREBIN and AndroZoo. We trained and validated our models with 4000 files (training set). We had kept separate 700 files (test set) which were not used during training and validation. We used our trained models to predict the classes of the unseen file embeddings from the test set. The automatically generated features allowed training of robust detection models. The Android malware detection models performed best with Android manifest file embeddings concatenated with Dalvik executable file embeddings, with some of the models achieving Precision, Recall and Accuracy values above 99% consistently during development and over 97% against unseen file embeddings. The prediction accuracy of the detection model trained on our automatically generated features was equivalent to the accuracy achieved by one of the most cited research works known as DREBIN, which was 94%. We also provided a simple method to directly utilise the file present in Android APK to create feature embeddings without scouring through Android application files to identify reliable features. The resulting system can be further improved against new emerging threats and be better trained by just gathering more samples.
Author | : G. De Giacomo |
Publisher | : IOS Press |
Total Pages | : 3122 |
Release | : 2020-09-11 |
Genre | : Computers |
ISBN | : 164368101X |
Download ECAI 2020 Book in PDF, ePub and Kindle
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Author | : Weina Niu |
Publisher | : Springer Nature |
Total Pages | : 197 |
Release | : |
Genre | : |
ISBN | : 9819714591 |
Download Android Malware Detection and Adversarial Methods Book in PDF, ePub and Kindle
Author | : Kumar, Raghvendra |
Publisher | : IGI Global |
Total Pages | : 502 |
Release | : 2023-11-09 |
Genre | : Computers |
ISBN | : 1668493195 |
Download Risk Detection and Cyber Security for the Success of Contemporary Computing Book in PDF, ePub and Kindle
With the rapid evolution of technology, identifying new risks is a constantly moving target. The metaverse is a virtual space that is interconnected with cloud computing and with companies, organizations, and even countries investing in virtual real estate. The questions of what new risks will become evident in these virtual worlds and in augmented reality and what real-world impacts they will have in an ever-expanding internet of things (IoT) need to be answered. Within continually connected societies that require uninterrupted functionality, cyber security is vital, and the ability to detect potential risks and ensure the security of computing systems is crucial to their effective use and success. Proper utilization of the latest technological advancements can help in developing more efficient techniques to prevent cyber threats and enhance cybersecurity. Risk Detection and Cyber Security for the Success of Contemporary Computing presents the newest findings with technological advances that can be utilized for more effective prevention techniques to protect against cyber threats. This book is led by editors of best-selling and highly indexed publications, and together they have over two decades of experience in computer science and engineering. Featuring extensive coverage on authentication techniques, cloud security, and mobile robotics, this book is ideally designed for students, researchers, scientists, and engineers seeking current research on methods, models, and implementation of optimized security in digital contexts.
Author | : Jagdish Chand Bansal |
Publisher | : Springer Nature |
Total Pages | : 511 |
Release | : 2020-04-09 |
Genre | : Technology & Engineering |
ISBN | : 9811533253 |
Download Communication and Intelligent Systems Book in PDF, ePub and Kindle
This book gathers selected research papers presented at the International Conference on Communication and Intelligent Systems (ICCIS 2019), organised by Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, India and Rajasthan Technical University, Kota, India on 9–10 November 2019. This book presents a collection of state-of-the-art research work involving cutting-edge technologies for communication and intelligent systems. Over the past few years, advances in artificial intelligence and machine learning have sparked new research efforts around the globe, which explore novel ways of developing intelligent systems and smart communication technologies. The book presents single- and multi-disciplinary research on these themes in order to make the latest results available in a single, readily accessible source.