Android Malware Detection Through Permission And App Component Analysis Using Machine Learning Algorithms

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Android Malware Detection Through Permission and App Component Analysis Using Machine Learning Algorithms

Author : Keyur Milind Kulkarni
Publisher :
ISBN 13 :
Total Pages : 77 pages
Book Rating : 4.:/5 (115 download)

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Book Synopsis Android Malware Detection Through Permission and App Component Analysis Using Machine Learning Algorithms by : Keyur Milind Kulkarni

Download or read book Android Malware Detection Through Permission and App Component Analysis Using Machine Learning Algorithms written by Keyur Milind Kulkarni and published by . This book was released on 2018 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improvement in technology has inevitably altered the tactic of criminals to thievery. In recent times, information is the real commodity and it is thus subject to theft as any other possessions: cryptocurrency, credit card numbers, and illegal digital material are on the top. If globally available platforms for smartphones are considered, the Android open source platform (AOSP) emerges as a prevailing contributor to the market and its popularity continues to intensify. Whilst it is beneficiary for users, this development simultaneously makes a prolific environment for exploitation by immoral developers who create malware or reuse software illegitimately acquired by reverse engineering. Android malware analysis techniques are broadly categorized into static and dynamic analysis. Many researchers have also used feature-based learning to build and sustain working security solutions. Although Android has its base set of permissions in place to protect the device and resources, it does not provide strong enough security framework to defend against attacks. This thesis presents several contributions in the domain of security of Android applications and the data within these applications. First, a brief survey of threats, vulnerability and security analysis tools for the AOSP is presented. Second, we develop and use a genre extraction algorithm for Android applications to check the availability of those applications in Google Play Store. Third, an algorithm for extracting unclaimed permissions is proposed which will give a set of unnecessary permissions for applications under examination. Finally, machine learning aided approaches for analysis of Android malware were adopted. Features including permissions, APIs, content providers, broadcast receivers, and services are extracted from benign (~2,000) and malware (5,560) applications and examined for evaluation. We create feature vector combinations using these features and feed these vectors to various classifiers. Based on the evaluation metrics of classifiers, we scrutinize classifier performance with respect to specific feature combination. Classifiers such as SVM, Logistic Regression and Random Forests spectacle a good performance whilst the dataset of combination of permissions and APIs records the maximum accuracy for Logistic Regression.

Malware Detection

Author : Mihai Christodorescu
Publisher : Springer Science & Business Media
ISBN 13 : 0387445994
Total Pages : 307 pages
Book Rating : 4.3/5 (874 download)

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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.

Android Malware Prediction by Permission Analysis and Data Mining

Author :
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Android Malware Prediction by Permission Analysis and Data Mining by :

Download or read book Android Malware Prediction by Permission Analysis and Data Mining written by and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, smartphones have brought people's lives to a new high level. Smartphone applications, or Apps, are accelerating the process with many more functions getting developed, such as browsing the Internet, making payments, taking photos and share. However, the "Apps" are bringing potential vulnerability when they access private information from the phones, and mobile security has never been so much focused on like today. In this paper, we presented a novel Android Permission based malware detection technique. We first gather a huge set of both malware and benign Apps through web clawer and develop a tool to decompile Apps to source code and manifest files automatically. Then permissions with other information are extracted for each App, making up to a raw data set. Afterward, we apply data cleaning, dimension reduction and statical analysis to the raw data set. We find that the distribution of permissions for Apps shares a difference between malware dataset and benign dataset. Finally, we take advantage of machine learning algorithms, including Logistic Regression Model, Tree Model with Ensemble techniques, Neural Network and finally an ensemble model to find patterns and more valuable information. Other models are also discussed. Extended experiments using these various machine learning models are conducted in the end. From the results, we can see that our method generates a good accuracy, F-score and overall performance of malicious App prediction.

Android Malware Detection using Machine Learning

Author : ElMouatez Billah Karbab
Publisher : Springer Nature
ISBN 13 : 303074664X
Total Pages : 212 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Android Malware Detection using Machine Learning by : ElMouatez Billah Karbab

Download or read book Android Malware Detection using Machine Learning written by ElMouatez Billah Karbab and published by Springer Nature. This book was released on 2021-07-10 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Qian Han
Publisher : No Starch Press
ISBN 13 : 1718503318
Total Pages : 330 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis The Android Malware Handbook by : Qian Han

Download or read book The Android Malware Handbook written by Qian Han and published by No Starch Press. This book was released on 2023-11-07 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Android Malware Detection and Adversarial Methods

Author : Weina Niu
Publisher : Springer Nature
ISBN 13 : 9819714591
Total Pages : 197 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Android Malware Detection and Adversarial Methods by : Weina Niu

Download or read book Android Malware Detection and Adversarial Methods written by Weina Niu and published by Springer Nature. This book was released on with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Android Malware Classification Using Parallelized Machine Learning Methods

Author : Lifan Xu
Publisher :
ISBN 13 : 9781369115284
Total Pages : 132 pages
Book Rating : 4.1/5 (152 download)

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Book Synopsis Android Malware Classification Using Parallelized Machine Learning Methods by : Lifan Xu

Download or read book Android Malware Classification Using Parallelized Machine Learning Methods written by Lifan Xu and published by . This book was released on 2016 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Android Malware and Analysis

Author : Ken Dunham
Publisher : CRC Press
ISBN 13 : 1482252198
Total Pages : 246 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Android Malware and Analysis by : Ken Dunham

Download or read book Android Malware and Analysis written by Ken Dunham and published by CRC Press. This book was released on 2014-10-24 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid growth and development of Android-based devices has resulted in a wealth of sensitive information on mobile devices that offer minimal malware protection. This has created an immediate need for security professionals that understand how to best approach the subject of Android malware threats and analysis. In Android Malware and Analysis, Ken Dunham, renowned global malware expert and author, teams up with international experts to document the best tools and tactics available for analyzing Android malware. The book covers both methods of malware analysis: dynamic and static. This tactical and practical book shows you how to use to use dynamic malware analysis to check the behavior of an application/malware as it has been executed in the system. It also describes how you can apply static analysis to break apart the application/malware using reverse engineering tools and techniques to recreate the actual code and algorithms used. The book presents the insights of experts in the field, who have already sized up the best tools, tactics, and procedures for recognizing and analyzing Android malware threats quickly and effectively. You also get access to an online library of tools that supplies what you will need to begin your own analysis of Android malware threats. Tools available on the book’s site include updated information, tutorials, code, scripts, and author assistance. This is not a book on Android OS, fuzz testing, or social engineering. Instead, it is about the best ways to analyze and tear apart Android malware threats. After reading the book, you will be able to immediately implement the tools and tactics covered to identify and analyze the latest evolution of Android threats. Updated information, tutorials, a private forum, code, scripts, tools, and author assistance are available at AndroidRisk.com for first-time owners of the book.

Mobile OS Vulnerabilities

Author : Shivi Garg
Publisher : CRC Press
ISBN 13 : 1000924513
Total Pages : 150 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Mobile OS Vulnerabilities by : Shivi Garg

Download or read book Mobile OS Vulnerabilities written by Shivi Garg and published by CRC Press. This book was released on 2023-08-17 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Proceedings of ICRIC 2019

Author : Pradeep Kumar Singh
Publisher : Springer Nature
ISBN 13 : 3030294072
Total Pages : 897 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Proceedings of ICRIC 2019 by : Pradeep Kumar Singh

Download or read book Proceedings of ICRIC 2019 written by Pradeep Kumar Singh and published by Springer Nature. This book was released on 2019-11-21 with total page 897 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents high-quality, original contributions (both theoretical and experimental) on software engineering, cloud computing, computer networks & internet technologies, artificial intelligence, information security, and database and distributed computing. It gathers papers presented at ICRIC 2019, the 2nd International Conference on Recent Innovations in Computing, which was held in Jammu, India, in March 2019. This conference series represents a targeted response to the growing need for research that reports on and assesses the practical implications of IoT and network technologies, AI and machine learning, cloud-based e-Learning and big data, security and privacy, image processing and computer vision, and next-generation computing technologies.

Real-Time Analytics

Author : Byron Ellis
Publisher : John Wiley & Sons
ISBN 13 : 1118838025
Total Pages : 432 pages
Book Rating : 4.1/5 (188 download)

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Book Synopsis Real-Time Analytics by : Byron Ellis

Download or read book Real-Time Analytics written by Byron Ellis and published by John Wiley & Sons. This book was released on 2014-06-23 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.

Applied Learning Algorithms for Intelligent IoT

Author : Pethuru Raj Chelliah
Publisher : CRC Press
ISBN 13 : 1000461351
Total Pages : 369 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Applied Learning Algorithms for Intelligent IoT by : Pethuru Raj Chelliah

Download or read book Applied Learning Algorithms for Intelligent IoT written by Pethuru Raj Chelliah and published by CRC Press. This book was released on 2021-10-28 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights. This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.

Significant Permission Identification for Android Malware Detection

Author : Lichao Sun
Publisher :
ISBN 13 :
Total Pages : 61 pages
Book Rating : 4.:/5 (957 download)

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Book Synopsis Significant Permission Identification for Android Malware Detection by : Lichao Sun

Download or read book Significant Permission Identification for Android Malware Detection written by Lichao Sun and published by . This book was released on 2016 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: A recent report indicates that a newly developed malicious app for Android is introduced every 11 seconds. To combat this alarming rate of malware creation, we need a scalable malware detection approach that is effective and efficient. In this thesis, we introduce SigPID, a malware detection system based on permission analysis to cope with the rapid increase in the number of Android malware. Instead of analyzing all 135 Android permissions, our approach applies 3-level pruning by mining the permission data to identify only significant permissions that can be effective in distinguishing benign and malicious apps. Based on the identified significant permissions, SigPID utilizes classification algorithms to classify different families of malware and benign apps. Our evaluation finds that only 25% of permissions (34 out of 135 permissions) are significant. We then compare the performance of our approach, using only 25% of all permissions, against a baseline approach that analyzes all permissions. The results indicate that when Support Vector Machine (SVM) is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which are about the same as those produced by the baseline approach. We also show that SigPID is effective when used with 67 other commonly used supervised learning approaches. We find that 55 out of 67 algorithms can achieve F-measure of at least 85%, while the average running time can be reduced by 85.6\% compared with the baseline approach. When we compare the detection effectiveness of SigPID to those of other approaches, SigPID can detect 96.54% of malware in the data set while other approaches detect 3.99% to 96.41%.

An Analysis of Android Malware Detection Using Tree Learning Techniques

Author : Kyler D. Dickey
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis An Analysis of Android Malware Detection Using Tree Learning Techniques by : Kyler D. Dickey

Download or read book An Analysis of Android Malware Detection Using Tree Learning Techniques written by Kyler D. Dickey and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Android malware is a growing threat, coinciding with the increasing adoption of the Android platform. Malware detection methods used to maintain user privacy and system integrity are increasingly becoming the subject of research. Many new methods studied employ learning algorithms to detect malicious programs. This study investigates the use of byte and opcode frequency features as inputs for tree-based machine learning methods. The algorithm is optimized to reduce overfitting given input hyperparameter combinations and is tuned using cross-validation procedures. Lastly, the study deliberates on possible avenues for future research to gather more concrete evidence for the efficacy and cost-effectiveness of such a system in a productive environment, emphasizing the need for more strenuous testing processes.

Static Analysis for Android Malware Detection Using Document Vectors

Author : Utkarsh Raghav
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Static Analysis for Android Malware Detection Using Document Vectors by : Utkarsh Raghav

Download or read book Static Analysis for Android Malware Detection Using Document Vectors written by Utkarsh Raghav and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Malware Analysis

Author : Monnappa K A
Publisher : Packt Publishing Ltd
ISBN 13 : 1788397525
Total Pages : 500 pages
Book Rating : 4.7/5 (883 download)

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Book Synopsis Learning Malware Analysis by : Monnappa K A

Download or read book Learning Malware Analysis written by Monnappa K A and published by Packt Publishing Ltd. This book was released on 2018-06-29 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand malware analysis and its practical implementation Key Features Explore the key concepts of malware analysis and memory forensics using real-world examples Learn the art of detecting, analyzing, and investigating malware threats Understand adversary tactics and techniques Book Description Malware analysis and memory forensics are powerful analysis and investigation techniques used in reverse engineering, digital forensics, and incident response. With adversaries becoming sophisticated and carrying out advanced malware attacks on critical infrastructures, data centers, and private and public organizations, detecting, responding to, and investigating such intrusions is critical to information security professionals. Malware analysis and memory forensics have become must-have skills to fight advanced malware, targeted attacks, and security breaches. This book teaches you the concepts, techniques, and tools to understand the behavior and characteristics of malware through malware analysis. It also teaches you techniques to investigate and hunt malware using memory forensics. This book introduces you to the basics of malware analysis, and then gradually progresses into the more advanced concepts of code analysis and memory forensics. It uses real-world malware samples, infected memory images, and visual diagrams to help you gain a better understanding of the subject and to equip you with the skills required to analyze, investigate, and respond to malware-related incidents. What you will learn Create a safe and isolated lab environment for malware analysis Extract the metadata associated with malware Determine malware's interaction with the system Perform code analysis using IDA Pro and x64dbg Reverse-engineer various malware functionalities Reverse engineer and decode common encoding/encryption algorithms Reverse-engineer malware code injection and hooking techniques Investigate and hunt malware using memory forensics Who this book is for This book is for incident responders, cyber-security investigators, system administrators, malware analyst, forensic practitioners, student, or curious security professionals interested in learning malware analysis and memory forensics. Knowledge of programming languages such as C and Python is helpful but is not mandatory. If you have written few lines of code and have a basic understanding of programming concepts, you’ll be able to get most out of this book.

Intelligent Computing & Optimization

Author : Pandian Vasant
Publisher : Springer Nature
ISBN 13 : 3030932478
Total Pages : 1020 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Intelligent Computing & Optimization by : Pandian Vasant

Download or read book Intelligent Computing & Optimization written by Pandian Vasant and published by Springer Nature. This book was released on 2021-12-30 with total page 1020 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the scientific results of the fourth edition of the International Conference on Intelligent Computing and Optimization which took place at December 30–31, 2021, via ZOOM. The conference objective was to celebrate “Compassion and Wisdom” with researchers, scholars, experts and investigators in Intelligent Computing and Optimization worldwide, to share knowledge, experience, innovation—marvelous opportunity for discourse and mutuality by novel research, invention and creativity. This proceedings encloses the original and innovative scientific fields of optimization and optimal control, renewable energy and sustainability, artificial intelligence and operational research, economics and management, smart cities and rural planning, meta-heuristics and big data analytics, cyber security and blockchains, IoTs and Industry 4.0, mathematical modelling and simulation, health care and medicine.