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The Android application ecosystem has thrived, with hundreds of thousands of applications (apps) available to users; however, not all of them are safe or privacy-friendly. Analyzing these many apps for malicious behaviors is an important but challenging area of research as malicious apps tend to use prevalent stealth techniques, e.g., encryption, code transformation, and other obfuscation approaches to bypass detection. Academic researchers and security companies have realized that the traditional signature-based and static analysis methods are inadequate to deal with this evolvingthreat. In recent years, a number of static and dynamic code analysis proposals for analyzing Android apps have been introduced in academia and in the commercial world. Moreover, as a single detection approach may be ineffective against advanced obfuscation techniques, multiple frameworks for privacy leakage detection have been shown to yield better results when used in conjunction. In this dissertation, our contribution is two-fold. First, we organize 32 of the most recent and promising privacy-oriented proposals on Android apps analysis into two categories: static and dynamic analysis. For each category, we survey the state of-the-art proposals and provide a high-level overview of the methodology they rely on to detect privacy-sensitive leakages and app behaviors. Second, we choose one popular proposal from each category to analyze and detect leakages in 5,000 Android apps. Our toolchain setup consists of IntelliDroid (static) to find and trigger sensitive API (Application Program Interface) calls in target apps and leverages TaintDroid (dynamic) to detect leakages in these apps. We found that about 33%of the tested apps leak privacy-sensitive information over the network (e.g., IMEI, location, UDID), which is consistent with existing work. Furthermore, we highlight the efficiency of combining IntelliDroid and TaintDroid in comparison with Android Monkey and TaintDroid as used in most prior work. We report an overall increase in the frequency of leakage of identifiers. This increase may indicate that IntelliDroid is a better approach over Android Monkey.
This book constitutes the refereed proceedings of the 10th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2017, held in Guangzhou, China, in December 2017. The 47 papers presented in this volume were carefully reviewed and selected from 140 submissions. They deal with research findings, achievements, innovations and perspectives in information security and related fields covering topics such as security algorithms and architectures, privacy-aware policies, regulations and techniques, anonymous computation and communication, encompassing fundamental theoretical approaches, practical experimental projects, and commercial application systems for computation, communication and storage.
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.
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.
This book constitutes the thoroughly refereed roceedings of the 13th International Conference on Security and Privacy in Communications Networks, SecureComm 2017, held in Niagara Falls, ON, Canada, in October 2017.The 31 revised regular papers and 15 short papers were carefully reviewed and selected from 105 submissions. The topics range from security and privacy in machine learning to differential privacy, which are currently hot research topics in cyber security research.
This volume constitutes the thoroughly refereed post-conference proceedings of the 11th International Conference on Security and Privacy in Communication Networks, SecureComm 2015, held in Dallas, TX, USA, in October 2015. The 29 regular and 10 poster papers presented were carefully reviewed and selected from 107 submissions. It also presents 9 papers accepted of the workshop on Applications and Techniques in Cyber Security, ATCS 2015. The papers are grouped in the following topics: mobile, system, and software security; cloud security; privacy and side channels; Web and network security; crypto, protocol, and model.
This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes. The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions – this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works. The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with backgrounds in artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.
Smart phones have become an important daily companion and often used by users to store various private data such as contacts, photos, messages, various social network accounts etc. Users can furthermore extend the functionality of their phone by downloading applications (or apps) from various developers and online application stores. However, apps may misuse the data stored on the phone or obtained from the sensors and users do not have any direct means to track that. Hence, the need for improved mechanisms to better manage the privacy of user data is very important. There has been a lot of effort to detect and thwart unauthorized access to these private data. However, there is no consensus method which can ensure protection of user sensitive information from mobile devices and at the same time easily deployable at user side. This dissertation aims at developing methods to test Android applications for privacy leakage detection. For this, it presents a new technique: if an application is run twice and all program inputs and environment conditions are kept equal, then it should produce identical outputs. So, if a sensitive input is changed in two separate executions of the target application, and a variance is observed at output, then the output contains information from that sensitive input. Based on this idea we developed two systems namely DroidTest and MirrorDroid to detect leakage of privacy sensitive data. DroidTest instruments the Android framework APIs to insert security monitoring code. The instrumented APIs help to record user interactions and sensitive API values in record phase (first run of application) and restore the recorded information during replay execution (second run of the target application). Program inputs (except sensitive data) and environment conditions are kept equal in both runs and change in corresponding outputs corresponds to leakage of sensitive data. DroidTest does not require costly platform update and can be easily distributed as a modified Android SDK. On the other hand, MirrorDroid places the monitoring code within the Android Runtime (Dalvik Virtual Machine). It does not explicitly run an application twice like DroidTest. Rather, the instrumented Dalvik VM intercepts execution of each instruction and duplicates it before fetching next instructions, essentially running a separate execution (mirror execution) of the target program in parallel. Then the outgoing data in original and mirror execution is compared to find evidences of information leakage. We have evaluated the proposed systems on two data sets. The first data set is taken from the Android Malware Genome Project containing 225 samples from 20 malware families. Using DroidTest and MirrorDroid to monitor information leakage, we could successfully detect leakage already reported in literature. The second data set consists of 50 top free applications from the official Android Market Place (Google Play Store). We found 36 out of this 50 applications leak some kind of information, which is very alarming considering these are very popular and highly downloaded applications. Although, the proposed systems either instruments the application framework APIs or the Dalvik Virtual Machine, they produce low runtime overhead (DroidTest 22% and MirrorDroid 8.2%). The accuracy of the proposed detection mechanisms also proves the effectiveness of our methods. DroidTest produces 22% false positives. If we ignore false warnings generated by different ordering of thread executions in record and replay phase, the false positives rate stands at 10%. MirrorDroid does better than DroidTest and generates only 6% false positives for the applications in test data sets.
This book constitutes the refereed conference proceedings of the 20th International Symposium on Research in Attacks, Intrusions, and Defenses, RAID 2017, held in Atlanta, GA, USA, in September 2017. The 21 revised full papers were selected from 105 submissions. They are organized in the following topics: software security, intrusion detection, systems security, android security, cybercrime, cloud security, network security.