arXiv subjects

Xiaolin Xu

Publications and source records attributed to Xiaolin Xu.

40 records · Page 3Linked to original sources

arXiv2021

HAWK: Rapid Android Malware Detection through Heterogeneous Graph Attention Networks

Android is undergoing unprecedented malicious threats daily, but the existing methods for malware detection often fail to cope with evolving camouflage in malware. To address this issue, we present HAWK, a new malware detection framework for evolutionary Android applications. We model Android entities and behavioural relationships as a heterogeneous information network (HIN), exploiting its rich semantic metastructures for specifying implicit higher-order relationships. An incremental learning model is created to handle the applications that manifest dynamically, without the need for re-constructing the whole HIN and the subsequent embedding model. The model can pinpoint rapidly the proximity between a new application and existing in-sample applications and aggregate their numerical embeddings under various semantics. Our experiments examine more than 80,860 malicious and 100,375 benign applications developed over a period of seven years, showing that HAWK achieves the highest detection accuracy against baselines and takes only 3.5ms on average to detect an out-of-sample application, with the accelerated training time of 50x faster than the existing approach.

cs.CR↗

arXiv2021

DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator in Cloud-FPGA

As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to accelerate Deep Neural Networks (DNN), such virtualization environments have posed many new security issues. This work investigates the integrity of DNN FPGA accelerators in clouds. It proposes DeepStrike, a remotely-guided attack based on power glitching fault injections targeting DNN execution. We characterize the vulnerabilities of different DNN layers against fault injections on FPGAs and leverage time-to-digital converter (TDC) sensors to precisely control the timing of fault injections. Experimental results show that our proposed attack can successfully disrupt the FPGA DSP kernel and misclassify the target victim DNN application.

cs.CR↗

arXiv2020

A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework

Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. However, previous pruning methods mainly focus on reducing the model size and/or improving performance without considering the privacy of user data. To mitigate this concern, we propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset. At the algorithm level of the proposed framework, a systematic weight pruning technique based on the alternating direction method of multipliers (ADMM) is designed to iteratively solve the pattern-based pruning problem for each layer with randomly generated synthetic data. In addition, corresponding optimizations at the compiler level are leveraged for inference accelerations on devices. With the proposed framework, users could avoid the time-consuming pruning process for non-experts and directly benefit from compressed models. Experimental results show that the proposed framework outperforms three state-of-art end-to-end DNN frameworks, i.e., TensorFlow-Lite, TVM, and MNN, with speedup up to 4.2X, 2.5X, and 2.0X, respectively, with almost no accuracy loss, while preserving data privacy.

cs.LG↗

arXiv2019

EOP: An Encryption-Obfuscation Solution for Protecting PCBs Against Tampering and Reverse Engineering

PCBs are the core components for the devices ranging from the consumer electronics to military applications. Due to the accessibility of the PCBs, they are vulnerable to the attacks such as probing, eavesdropping, and reverse engineering. In this paper, a solution named EOP is proposed to migrate these threats. EOP encrypts the inter-chip communications with the stream cipher. The encryption and decryption are driven by the dedicated clock modules. These modules guarantee the stream cipher is correctly synchronized and free from tampering. Additionally, EOP also incorporates the PCB-level obfuscation for protection against reverse engineering. EOP is designated to be accomplished by utilizing the COTS components. For the validation, EOP is implemented in a Zynq SoC based system. Both the normal operation and tampering detection performance are verified. The results show that EOP can deliver the data from one chip to another without any errors. It is proved to be sensitive to any active tampering attacks.

cs.CR↗