Chenggang Wu

Lichen Jia, Bowen Tang, Chenggang Wu et al.

The binary code similarity detection (BCSD) method measures the similarity of two binary executable codes. Recently, the learning-based BCSD methods have achieved great success, outperforming traditional BCSD in detection accuracy and efficiency. However, the existing studies are rather sparse on the adversarial vulnerability of the learning-based BCSD methods, which cause hazards in security-related applications. To evaluate the adversarial robustness, this paper designs an efficient and black-box adversarial code generation algorithm, namely, FuncFooler. FuncFooler constrains the adversarial codes 1) to keep unchanged the program's control flow graph (CFG), and 2) to preserve the same semantic meaning. Specifically, FuncFooler consecutively 1) determines vulnerable candidates in the malicious code, 2) chooses and inserts the adversarial instructions from the benign code, and 3) corrects the semantic side effect of the adversarial code to meet the constraints. Empirically, our FuncFooler can successfully attack the three learning-based BCSD models, including SAFE, Asm2Vec, and jTrans, which calls into question whether the learning-based BCSD is desirable.

Yongbiao Chen, Sheng Zhang, Fangxin Liu et al.

In this paper, we make the very first attempt to investigate the integration of deep hash learning with vehicle re-identification. We propose a deep hash-based vehicle re-identification framework, dubbed DVHN, which substantially reduces memory usage and promotes retrieval efficiency while reserving nearest neighbor search accuracy. Concretely,~DVHN directly learns discrete compact binary hash codes for each image by jointly optimizing the feature learning network and the hash code generating module. Specifically, we directly constrain the output from the convolutional neural network to be discrete binary codes and ensure the learned binary codes are optimal for classification. To optimize the deep discrete hashing framework, we further propose an alternating minimization method for learning binary similarity-preserved hashing codes. Extensive experiments on two widely-studied vehicle re-identification datasets- \textbf{VehicleID} and \textbf{VeRi}-~have demonstrated the superiority of our method against the state-of-the-art deep hash methods. \textbf{DVHN} of $2048$ bits can achieve 13.94\% and 10.21\% accuracy improvement in terms of \textbf{mAP} and \textbf{Rank@1} for \textbf{VehicleID (800)} dataset. For \textbf{VeRi}, we achieve 35.45\% and 32.72\% performance gains for \textbf{Rank@1} and \textbf{mAP}, respectively.

Bowen Tang, Chenggang Wu, Zhe Wang et al.

Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead; (2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this paper, we propose a label-based transparent speculation scheme called SpecBox. It dynamically partitions the cache system to isolate speculative data and non-speculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, SpecBox also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that SpecBox is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small.

Zhi Zhang, Yueqiang Cheng, Minghua Wang et al.

Rowhammer attacks that corrupt level-1 page tables to gain kernel privilege are the most detrimental to system security and hard to mitigate. However, recently proposed software-only mitigations are not effective against such kernel privilege escalation attacks. In this paper, we propose an effective and practical software-only defense, called SoftTRR, to protect page tables from all existing rowhammer attacks on x86. The key idea of SoftTRR is to refresh the rows occupied by page tables when a suspicious rowhammer activity is detected. SoftTRR is motivated by DRAM-chip-based target row refresh (ChipTRR) but eliminates its main security limitation (i.e., ChipTRR tracks a limited number of rows and thus can be bypassed by many-sided hammer). Specifically, SoftTRR protects an unlimited number of page tables by tracking memory accesses to the rows that are in close proximity to page-table rows and refreshing the page-table rows once the tracked access count exceeds a pre-defined threshold. We implement a prototype of SoftTRR as a loadable kernel module, and evaluate its security effectiveness, performance overhead, and memory consumption. The experimental results show that SoftTRR protects page tables from real-world rowhammer attacks and incurs small performance overhead as well as memory cost.