Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Android Malware Prediction By Permission Analysis And Data Mining
Download Android Malware Prediction By Permission Analysis And Data Mining full books in PDF, epub, and Kindle. Read online Android Malware Prediction By Permission Analysis And Data Mining ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
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.
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.
Book Synopsis Data Mining Heuristic-based Malware Detection for Android Applications by : Naser Peiravian
Download or read book Data Mining Heuristic-based Malware Detection for Android Applications written by Naser Peiravian and published by . This book was released on 2013 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Google Android mobile phone platform is one of the dominant smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications (Apps). The popularity of Android platform draws attention of many developers which also attracts the attention of cybercriminals to develop different kinds of malware to be inserted into the Google Android Market or other third party markets as safe applications. In this thesis, we propose to combine permission, API (Application Program Interface) calls and function calls to build a Heuristic based framework for the detection of malicious Android Apps. In our design, the permission is extracted from each App's profile information and the APIs are extracted from the packed App file by using packages and classes to represent API calls. By using permissions, API calls and function calls as features to characterize each of Apps, we can develop a classifier by data mining techniques to identify whether an App is potentially malicious or not. An inherent advantage of our method is that it does not need to involve any dynamic tracking of the system calls but only uses simple static analysis to find system functions from each App. calls are always present for mobile Apps. Experiments on real-world Apps with more than 1200 malwares and 1200 benign samplses validate the algorithm performance.
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%.
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.
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.
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:
Download or read book Malware Diagnosis written by Bohyun Suh and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Android mobile phone has rapidly become popular and irreplaceable. The open-source Android platform allows developers to innovate the Android market in various ways, but also raises significant issues with various malicious apps, such as device malfunction, personal information leak, or financial loss. Yet, it is difficult to detect malicious apps by a human or obtain explicit information about suspicious apps. To solve the problem, many studies have come up with some frameworks. However, many frameworks have constraints such as only running on PC and manual data processing. In this thesis, we propose the Malware Diagnosis framework for deep learning-based malware detection using weighted permission. It is designed to be more practical to use with better performance in detecting malware apps. To increase the accuracy of the framework, we apply a ranking-based approach to permissions to generate weights that are derived from the ranking based on the number of permission used from malware and benign apps. As a tool, we develop MD (Malware Diagnosis) Assistant, an Android app that performs automated data extraction from installed apps and provides a prediction rate by running a deep learning model on an Android device. We then present experimental observations that show the effectiveness of our framework on detecting malware apps.
Book Synopsis Machine Intelligence and Soft Computing by : Debnath Bhattacharyya
Download or read book Machine Intelligence and Soft Computing written by Debnath Bhattacharyya and published by Springer Nature. This book was released on 2021-01-20 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at the International Conference on Machine Intelligence and Soft Computing (ICMISC 2020), held jointly by Vignan’s Institute of Information Technology, Visakhapatnam, India and VFSTR Deemed to be University, Guntur, AP, India during 03-04 September 2020. Topics covered in the book include the artificial neural networks and fuzzy logic, cloud computing, evolutionary algorithms and computation, machine learning, metaheuristics and swarm intelligence, neuro-fuzzy system, soft computing and decision support systems, soft computing applications in actuarial science, soft computing for database deadlock resolution, soft computing methods in engineering, and support vector machine.
Book Synopsis Mitigating Android Application Risk Through Permission-based Analysis and Risk Assessment Technique by :
Download or read book Mitigating Android Application Risk Through Permission-based Analysis and Risk Assessment Technique written by and published by . This book was released on 2019 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this era, smartphones intrude upon our daily lives due to their computational capabilities and ease of use and carry. Almost everything can be accessed easily via smartphones such as emails, online banking, and health records. Therefore, a vast amount of valuable data ranging from personal information (e.g., text messages and contacts) to company information when companies utilize a bring your own device (BYOD) policy, are stored and handled by smartphones. This improvement and capabilities on smartphones encourage malware developers to develop more advanced attacks that are able to steal users' private information and cause financial losses to victims. Among different smartphone platforms, Android has become the most prevalent and fastest growing platform. Android devices, of which millions are in use today, are attractive due to their availability, lower cost, and open source philosophy. Thus, the popularity of Android has encouraged developers to create more applications to serve Android users. On the other hand, Android has become an attractive target for adversaries who construct different types of malicious applications and use different social engineering approaches to attract users to download and trust their applications. Importantly, malicious applications usually request permissions that are not related to their main functionality in order to access sensitive information or resources. Most users grant the requested permissions without understanding the potential harm of those applications and how the requested permissions can be misused to disclose private information. Built-in security mechanisms in the Android operating system (OS) provide various levels of protection for data and applications. One of these mechanisms is the permission model, which enables secure access to sensitive information and devices' resources. However, the Android permission model does not have specific professional standards that developers need to follow when they declare their applications' permissions. Consequently, this gives unscrupulous developers the flexibility to request permissions that are not related to their applications' main functionality. It is unfortunate that most users grant the requested permissions without understanding the potential harm of those applications and how the requested permissions can be misused to disclose private information. Moreover, the Android permission model does not impede privilege escalation or information leakage. In other words, the permission model is not fine-grained enough to provide sufficient means to control an application's activities and specify what private information or resources are accessible to the application. Therefore, considerable effort is needed to ensure the Android OS security, which has led to significant interest among researchers to alleviate its threats. Several proposed solutions have been introduced to address these issues. However, many of those solutions have crippling limitations that may invalidate their results. Therefore, there is a need for more powerful and effective solutions to mitigate the security challenges that the Android permission model causes. Hence, this research proposes a Permission Usage and Risk Estimation for Android (PUREDroid), a risk assessment model that informs the user about the risk level of an application and its requested permissions to help users make the right or better decision about whether to grant or deny a requested permission. PUREDroid measures the risk associated with the requested permissions within an application based on the application category. By constructing an optimal set of permissions for each category, each permission within an application from the same category is assigned to one of three security risk levels: Low, Moderate, or High risk level. PUREDroid measures the security risk of the Android application by extracting some information from inspected applications, including permissions, intents and APIs, and utilizing several supervised machine learning models to assign risk scores. PUREDroid is evaluated on more than 23000 applications, including 17316 benign applications and 5739 malware applications belonging to seven different categories, which are Books & Reference, Education, Entertainment, Lifestyle, Music & Audio, Photography, and Tools. The performance and evaluation shows that PUREDroid is able to predict risks for the applications based on their categories with a high accuracy rate and low false positive rate depending on the application's category. This outstanding performance is achieved by utilizing Extreme Gradient Boosting algorithm, which provides the highest performance among all of the other machine learning algorithms.
Book Synopsis Data Science and Applications by : Satyasai Jagannath Nanda
Download or read book Data Science and Applications written by Satyasai Jagannath Nanda and published by Springer Nature. This book was released on with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Data Mining and Applications by : Guojun Gan
Download or read book Advanced Data Mining and Applications written by Guojun Gan and published by Springer. This book was released on 2018-12-28 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th International Conference on Advanced Data Mining and Applications, ADMA 2018, held in Nanjing, China in November 2018. The 23 full and 22 short papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers were organized in topical sections named: Data Mining Foundations; Big Data; Text and Multimedia Mining; Miscellaneous Topics.
Book Synopsis International Conference on Innovative Computing and Communications by : Ashish Khanna
Download or read book International Conference on Innovative Computing and Communications written by Ashish Khanna and published by Springer Nature. This book was released on 2021-08-28 with total page 893 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high-quality research papers presented at the Fourth International Conference on Innovative Computing and Communication (ICICC 2021), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on February 20–21, 2021. Introducing the innovative works of scientists, professors, research scholars, students and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Book Synopsis Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications by : L. Ashok Kumar
Download or read book Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications written by L. Ashok Kumar and published by Springer Nature. This book was released on 2020-03-12 with total page 943 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the complexity, and heterogeneity of the smart grid and the high volume of information to be processed, artificial intelligence techniques and computational intelligence appear to be some of the enabling technologies for its future development and success. The theme of the book is “Making pathway for the grid of future” with the emphasis on trends in Smart Grid, renewable interconnection issues, planning-operation-control and reliability of grid, real time monitoring and protection, market, distributed generation and power distribution issues, power electronics applications, computer-IT and signal processing applications, power apparatus, power engineering education and industry-institute collaboration. The primary objective of the book is to review the current state of the art of the most relevant artificial intelligence techniques applied to the different issues that arise in the smart grid development.
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 Emerging Technologies in Data Mining and Information Security by : Paramartha Dutta
Download or read book Emerging Technologies in Data Mining and Information Security written by Paramartha Dutta and published by Springer Nature. This book was released on 2022-09-28 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2022) held at Institute of Engineering & Management, Kolkata, India, during February 23–25, 2022. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT) and information security.
Book Synopsis Mobile and Wireless Technologies 2017 by : Kuinam J. Kim
Download or read book Mobile and Wireless Technologies 2017 written by Kuinam J. Kim and published by Springer. This book was released on 2017-06-14 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 4th International Conference on Mobile and Wireless Technology (ICMWT), held in Kuala Lumpur, Malaysia in June 2017, an event that provides researchers and practitioners from both academia and industry with a platform to keep them abreast of cutting-edge developments in the field. The peer-reviewed and accepted papers presented here address topics in a number of major areas: Mobile, Wireless Networks and Applications; Security in Mobile and Wireless; Mobile Data Management and Applications; Mobile Software; Multimedia Communications; Wireless Communications; and Services, Application and Business.
