Android Malware Detection And Forensics Based On Api Calls
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Android Malware Detection And Forensics Based On Api Calls
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Author : Arpitaben Shah
language : en
Publisher:
Release Date : 2016
Android Malware Detection And Forensics Based On Api Calls written by Arpitaben Shah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
In recent world, mobile devices play an important role towards immense information sharing. As mobile smartphones become more widespread and powerful, they store more personal data and may leak it carelessly or maliciously. Research shows that Android is widely used operating system among many smartphones. The growth of Android users infatuates attackers to target more Android smartphone devices by using malicious software. To defend against expansion of Android malwares, researchers propose many analysis, detection and classification techniques. This paper introduces a dynamic analysis approach to intercept API calls at runtime, extract logs, and analyze them. It helps to understand runtime behavior of installed applications and use of API calls for malicious purpose. By using this method, analysts may get to know if the application is benign or malicious by comparing its actual behavior and expected behavior. This research will offer essential help to malware researchers to quickly understand the activities and internal workings of unknown applications.
Intelligent Mobile Malware Detection
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Author : Tony Thomas
language : en
Publisher: CRC Press
Release Date : 2022-12-30
Intelligent Mobile Malware Detection written by Tony Thomas and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.
The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities. The attacks, which escalated after the COVID-19 pandemic, proved there is great importance in protecting Android mobile devices from malware attacks. Intelligent Mobile Malware Detection will teach users how to develop intelligent Android malware detection mechanisms by using various graph and stochastic models. The book begins with an introduction to the Android operating system accompanied by the limitations of the state-of-the-art static malware detection mechanisms as well as a detailed presentation of a hybrid malware detection mechanism. The text then presents four different system call-based dynamic Android malware detection mechanisms using graph centrality measures, graph signal processing and graph convolutional networks. Further, the text shows how most of the Android malware can be detected by checking the presence of a unique subsequence of system calls in its system call sequence. All the malware detection mechanisms presented in the book are based on the authors' recent research. The experiments are conducted with the latest Android malware samples, and the malware samples are collected from public repositories. The source codes are also provided for easy implementation of the mechanisms. This book will be highly useful to Android malware researchers, developers, students and cyber security professionals to explore and build defense mechanisms against the ever-evolving Android malware.
Android Malware Detection And Adversarial Methods
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Author : Weina Niu
language : en
Publisher: Springer Nature
Release Date : 2024-05-23
Android Malware Detection And Adversarial Methods written by Weina Niu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-23 with Computers categories.
The rise of Android malware poses a significant threat to users’ information security and privacy. Malicious software can inflict severe harm on users by employing various tactics, including deception, personal information theft, and device control. To address this issue, both academia and industry are continually engaged in research and development efforts focused on detecting and countering Android malware. This book is a comprehensive academic monograph crafted against this backdrop. The publication meticulously explores the background, methods, adversarial approaches, and future trends related to Android malware. It is organized into four parts: the overview of Android malware detection, the general Android malware detection method, the adversarial method for Android malware detection, and the future trends of Android malware detection. Within these sections, the book elucidates associated issues, principles, and highlights notable research. By engaging with this book, readers will gain not only a global perspective on Android malware detection and adversarial methods but also a detailed understanding of the taxonomy and general methods outlined in each part. The publication illustrates both the overarching model and representative academic work, facilitating a profound comprehension of Android malware detection.
Cyber Malware
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Author : Iman Almomani
language : en
Publisher: Springer Nature
Release Date : 2023-11-08
Cyber Malware written by Iman Almomani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-08 with Technology & Engineering categories.
This book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. The book equips readers with the necessary knowledge and techniques to successfully lower the risk against emergent malware attacks. Topics cover protections against malware using machine learning algorithms, Blockchain and AI technologies, smart AI-based applications, automated detection-based AI tools, forensics tools, and much more. The authors discuss theoretical, technical, and practical issues related to cyber malware attacks and defense, making it ideal reading material for students, researchers, and developers.
Android Malware Detection Using Machine Learning
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Author : ElMouatez Billah Karbab
language : en
Publisher: Springer Nature
Release Date : 2021-07-10
Android Malware Detection Using Machine Learning written by ElMouatez Billah Karbab and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-10 with Computers categories.
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Android Malware Detection Based On Multimodal Deep Learning Using Sensitive Api Method Calls Feature
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Author : Hsin-Cheng Chung
language : en
Publisher:
Release Date : 2025
Android Malware Detection Based On Multimodal Deep Learning Using Sensitive Api Method Calls Feature written by Hsin-Cheng Chung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Due to the openness and high market share of the Android system, its mobile devices are more vulnerable to hacker attacks. In the past, methods for detecting Android malicious applications primarily relied on machine learning or deep learning models, which effectively identified malicious applications by analyzing features such as permissions and API method calls. However, these traditional methods face significant challenges when detecting malicious applications that have been processed with obfuscation techniques. Therefore, exploring solutions to address obfuscated malicious applications has become the focus of this study. Since obfuscated malicious programs may increase the number of API method calls beyond the original count, detecting all APIs would consume a significant amount of time and resources. This study, therefore, focuses on extracting permissions from Android malicious applications and applying algorithms to filter out a small subset of critical sensitive APIs as features for model training. These features are then used to train a hybrid model architecture composed of DNN and CNN sub-models to detect obfuscated malicious applications and evaluate its effectiveness. According to the detection results of the hybrid model developed in this study, the accuracy for the unobfuscated test set reached 99.2%, while the accuracy for the obfuscated test set reached 98.8%. The F1-Score achieved 99.4% on the unobfuscated test set and 99.2% on the obfuscated test set.
Information Security Privacy And Digital Forensics
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Author : Bhavesh N. Gohil
language : en
Publisher: Springer Nature
Release Date : 2026-01-01
Information Security Privacy And Digital Forensics written by Bhavesh N. Gohil and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-01 with Computers categories.
This volume contains the selected proceedings of the International Conference on Information Security, Privacy, and Digital Forensics (ICISPD 2023). The content highlights novel contributions and recent developments in areas such as cyber-attacks and defenses, computer forensics, cybersecurity database forensics, cyber threat intelligence, data analytics for security, anonymity, penetration testing, incident response, Internet of Things security, malware and botnets, social media security, humanitarian forensics, software and media piracy, crime analysis, and hardware security, among others. This volume will serve as a valuable resource for researchers in both industry and academia who are working in the fields of security, privacy, and digital forensics from both technological and social perspectives.
Android Application Security
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Author : Mu Zhang
language : en
Publisher: Springer
Release Date : 2016-11-16
Android Application Security written by Mu Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-16 with Computers categories.
This SpringerBrief explains the emerging cyber threats that undermine Android application security. It further explores the opportunity to leverage the cutting-edge semantics and context–aware techniques to defend against such threats, including zero-day Android malware, deep software vulnerabilities, privacy breach and insufficient security warnings in app descriptions. The authors begin by introducing the background of the field, explaining the general operating system, programming features, and security mechanisms. The authors capture the semantic-level behavior of mobile applications and use it to reliably detect malware variants and zero-day malware. Next, they propose an automatic patch generation technique to detect and block dangerous information flow. A bytecode rewriting technique is used to confine privacy leakage. User-awareness, a key factor of security risks, is addressed by automatically translating security-related program semantics into natural language descriptions. Frequent behavior mining is used to discover and compress common semantics. As a result, the produced descriptions are security-sensitive, human-understandable and concise.By covering the background, current threats, and future work in this field, the brief is suitable for both professionals in industry and advanced-level students working in mobile security and applications. It is valuable for researchers, as well.
Android Malware Analysis Defensive Exploitation 2025 Hinglish Edition
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Author : A. Clarke
language : en
Publisher: Code Academy
Release Date : 2025-10-07
Android Malware Analysis Defensive Exploitation 2025 Hinglish Edition written by A. Clarke and has been published by Code Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-07 with Computers categories.
“Android Malware Analysis & Defensive Exploitation 2025 (Hinglish Edition)” by A. Clarke ek practical aur responsible guide hai jo Android apps aur mobile threats ko analyse, detect, aur mitigate karna sikhata hai — sab Hinglish (Hindi + English mix) mein.
Security Vetting Of Android Applications Using Graph Based Deep Learning Approaches
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Author : Prabesh Poudel
language : en
Publisher:
Release Date : 2021
Security Vetting Of Android Applications Using Graph Based Deep Learning Approaches written by Prabesh Poudel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Android (Electronic resource) categories.
Along with the immense popularity of Android applications, the Android ecosystem is under constant threat of malware attacks. This issue warrants developing efficient tools to detect malware apps. There is a large body of work in the literature that has applied static analysis for malware detection. For instance, one popular idea has been to extract API-calls from the app code and then to use those API-calls as artifacts to train machine learning models to classify malware and benign apps. However, most of this line of work does not incorporate the true execution sequence of the API-calls, and thus misses out to capture a potentially rich signature. Furthermore, while evaluating the vetting accuracy, many of the prior work report their primary results on a randomly selected test set that are not spatially consistent (malware percentage in the test set approximating real-world scenario) and/or temporally consistent (having correct time split of train and test data) which artificially inflates the performance of the model. In this thesis, we explore if tracking the true sequence of the API-calls improves the effectiveness of the vetting process and present results ranging from testing on a random test set to a spatially and temporally consistent test set. We perform deep learning-based malware classification using a graph that we name API sequence graph which preserves the true sequence of API calls. The experiments show that our best performing model achieves AuPRC ranging from 0.977 to 0.86 and an F1-score of 0.955 to 0.83 depending on the consistency of the test set. The results show that our best-performing model, based on the true sequence of API calls, outperforms a quasi-sequence-based model.