Wei Wang

Hamish Spencer, Wei Wang, Ruoxi Sun et al.

With the increasingly rapid development of new malicious computer software by bad faith actors, both commercial and research-oriented antivirus detectors have come to make greater use of machine learning tactics to identify such malware as harmful before end users are exposed to their effects. This, in turn, has spurred the development of tools that allow for known malware to be manipulated such that they can evade being classified as dangerous by these machine learning-based detectors, while retaining their malicious functionality. These manipulations function by applying a set of changes that can be made to Windows programs that result in a different file structure and signature without altering the software's capabilities. Various proposals have been made for the most effective way of applying these alterations to input malware to deceive static malware detectors; the purpose of this research is to examine these proposals and test their implementations to determine which tactics tend to generate the most successful attacks.

Xianan Zhang, Wei Wang, Xuedou Xiao et al.

Some important indoor localization applications, such as localizing a lost kid in a shopping mall, call for a new peer-to-peer localization technique that can localize an individual's smartphone or wearables by directly using another's on-body devices in unknown indoor environments. However, current localization solutions either require pre-deployed infrastructures or multiple antennas in both transceivers, impending their wide-scale application. In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy. P2PLocate leverages the multipath variations intentionally created by an on-body backscatter tag, coupled with spatial information offered by user movements, to accomplish this objective without relying on any pre-deployed infrastructures or pre-training. P2PLocate incorporates novel algorithms to address two major challenges: (i) interference with strong direct-path signal while extracting multipath variations, and (ii) lack of direction information while using single-antenna transceivers. We implement P2PLocate on commercial off-the-shelf Google Nexus 6p, Intel 5300 WiFi card, and Raspberry Pi B4. Real-world experiments reveal that P2PLocate can localize both static and mobile targets with a median accuracy of 0.88 m.