Heng Yin

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 languagedescriptions. 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.

Malicious software (i.e., malware) has become a severe threat to interconnected computer systems for decades and has caused billions of dollars damages each year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving becoming more sophisticated and evasive to strike against current malware analysis and defense systems. Automatic Malware Analysis presents a virtualized malware analysis framework that addresses common challenges in malware analysis. In regards to this new analysis framework, a series of analysis techniques for automatic malware analysis is developed. These techniques capture intrinsic characteristics of malware, and are well suited for dealing with new malware samples and attack mechanisms.