Xiaoyong Zhou

Malware is one of the most serious security threats on the Internet today. In fact, most Internet problems such as spam e-mails and denial of service attacks have malware as their underlying cause. That is, computers that are compromised with malware are often networked together to form botnets, and many attacks are launched using these malicious, attacker-controlled networks. With the increasing significance of malware in Internet attacks, much research has concentrated on developing techniques to collect, study, and mitigate malicious code. Without doubt, it is important to collect and study malware found on the Internet. However, it is even more important to develop mitigation and detection techniques based on the insights gained from the analysis work. Unfortunately, current host-based detection approaches

We explore the threat of smartphone malware with access to on-board sensors, which opens new avenues for illicit collection of private information. While existing work shows that such “sensory malware” can convey raw sensor data (e.g., video and audio) to a remote server, these approaches lack stealthiness, incur significant communication and computation overhead during data transmission and processing, and can easily be defeated by existing protections like denying installation of applications with access to both sensitive sensors and the network. We present Soundcomber, a Trojan with few and innocuous permissions, that can extract a small amount of targeted private information from the audio sensor of the phone. Using targeted profiles for context-aware analysis, Soundcomber intelligently “pulls out” sensitive data such as credit card and PIN numbers from both toneand speech-based interaction with phone menu systems. Soundcomber performs efficient, stealthy local extraction, thereby greatly reducing the communication cost for delivering stolen data. Soundcomber automatically infers the destination phone number by analyzing audio, circumvents known security defenses, and conveys information remotely without direct network access. We also design and implement a defensive architecture that foils Soundcomber, identify new covert channels specific to smartphones, and provide a video demonstration

Genome-wide association studies (GWAS) aim at discovering the association between genetic variations, particularly single-nucleotide polymorphism (SNP), and common diseases, which is well recognized to be one of the most important and active areas in biomedical research. Also renowned is the privacy implication of such studies, which has been brought into the limelight by the recent attack proposed by Homer et al. Homer's attack demonstrates that it is possible to identify a GWAS participant from the allele frequencies of a large number of SNPs. Such a threat, unfortunately, was found in our research to be significantly understated. In this paper, we show that individuals can actually be identified from even a relatively small set of statistics, as those routinely published in GWAS papers. We present two attacks. The first one extends Homer's attack with a much more powerful test statistic, based on the correlations among different SNPs described by coefficient of determination (r2). This attack can determine the presence of an individual from the statistics related to a couple of hundred SNPs. The second attack can lead to complete disclosure of hundreds of participants' SNPs, through analyzing the information derived from published statistics. We also found that those attacks can succeed even when the precisions of the statistics are low and part of data is missing. We evaluated our attacks on the real human genomes and concluded that such threats are completely realistic.