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1. Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA

   Rakin, Adnan Siraj; Luo, Yukui; Xu, Xiaolin; Fan, Deliang (August 2021, 30th USENIX Security Symposium)

   The wide deployment of Deep Neural Networks (DNN) in high-performance cloud computing platforms brought to light multi-tenant cloud field-programmable gate arrays (FPGA) as a popular choice of accelerator to boost performance due to its hardware reprogramming flexibility. Such a multi-tenant FPGA setup for DNN acceleration potentially exposes DNN interference tasks under severe threat from malicious users. This work, to the best of our knowledge, is the first to explore DNN model vulnerabilities in multi-tenant FPGAs. We propose a novel adversarial attack framework: Deep-Dup, in which the adversarial tenant can inject adversarial faults to the DNN model in the victim tenant of FPGA. Specifically, she can aggressively overload the shared power distribution system of FPGA with malicious power-plundering circuits, achieving adversarial weight duplication (AWD) hardware attack that duplicates certain DNN weight packages during data transmission between off-chip memory and on-chip buffer, to hijack the DNN function of the victim tenant. Further, to identify the most vulnerable DNN weight packages for a given malicious objective, we propose a generic vulnerable weight package searching algorithm, called Progressive Differential Evolution Search (P-DES), which is, for the first time, adaptive to both deep learning white-box and black-box attack models. The proposed Deep-Dup is experimentally validated in a developed multi-tenant FPGA prototype, for two popular deep learning applications, i.e., Object Detection and Image Classification. Successful attacks are demonstrated in six popular DNN architectures (e.g., YOLOv2, ResNet-50, MobileNet, etc.) on three datasets (COCO, CIFAR-10, and ImageNet). 

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2. A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks

   https://doi.org/10.1109/ACCESS.2024.3357357  

   Holland, James C.; Javidi-Niroumand, Farahnaz; Alnaser, Ala’ J.; Sargolzaei, Arman (January 2024, IEEE Access)

   Control systems are used in safety-critical applications where tuning the system parameters is required to ensure safe and secure operation. The process of tuning these parameters can be arduous, time-consuming, and unreliable as they are dependent on the operating environment. In this paper, we discuss a testing and verification approach for tuning the control parameters of a secure cooperative adaptive cruise controller (CACC) while simultaneously testing the safety of the algorithm under false data injection (FDI) attacks. In our approach, we use particle swarm optimization (PSO) to tune the parameters of the controller and observer. The performance of the controller will be evaluated before and after the optimization of control and detection parameters. After employing several swarms, it was noticed that the global optimal solution is reached within 74 iterations, on average. In summary, the configurations found by each swarm ensured that a safe following distance was achieved throughout testing. In terms of FDI estimation, however, the more conservative configuration with the minimum optimal parameter values performed the best. 

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3. IDCrypt: A Multi-User Searchable Symmetric Encryption Scheme for Cloud Applications

   https://doi.org/10.1109/ACCESS.2017.2786026  

   Wang, Guofeng; Liu, Chuanyi; Dong, Yingfei; Han, Peiyi; Pan, Hezhong; Fang, Binxing (January 2018, IEEE Access)

   Searchable Encryption (SE) has been extensively examined by both academic and industry researchers. While many academic SE schemes show provable security, they usually expose some query information (e.g., search patterns) to achieve high efficiency. However, several inference attacks have exploited such leakage, e.g., a query recovery attack can convert opaque query trapdoors to their corresponding keywords based on some prior knowledge. On the other hand, many proposed SE schemes require significant modification of existing applications, which makes them less practical, weak in usability, and difficult to deploy. In this paper, we introduce a secure and practical SE scheme with provable security strength for cloud applications, called IDCrypt, which improves the search efficiency and enhanced the security strength of SE using symmetric cryptography. We further point out the main challenges in securely searching on multiple indexes and sharing encrypted data between multiple users. To address the above issues, we propose a token-adjustment scheme to preserve the search functionality among multi-indexes, and a key sharing scheme which combines Identity-Based Encryption (IBE) and Public-Key Encryption (PKE). Our experimental results show that the overhead of IDCrypt is fairly low. 

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4. Controller Switching-Enabled Active Detection of Multiplicative Cyberattacks on Process Control Systems

   https://doi.org/10.23919/ACC53348.2022.9867804  

   Narasimhan, Shilpa; El-Farra, Nael H.; Ellis, Matthew J. (June 2022, American Control Conference)

   This work focuses on the problem of enhancing cyberattack detection capabilities in process control systems subject to multiplicative cyberattacks. First, the relationship between closed-loop stability and attack detectability with respect to a class of residual-based detection schemes is rigorously analyzed. The results are used to identify a set of controller parameters (called "attack-sensitive" controller parameters) under which an attack can destabilize the closed-loop system. The selection of attack-sensitive controller parameters can enhance the ability to detect attacks, but can also degrade the performance of the attack-free closed-loop system. To balance this trade-off, a novel active attack detection methodology employing controller parameter switching between the nominal controller parameters (chosen on the basis of standard control design criteria) and the attack-sensitive controller parameters, is developed. The proposed methodology is applied to a chemical process example to demonstrate its ability to detect multiplicative sensor-controller communication link attacks. 

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5. Defense Mechanisms Against Undetectable Cyberattacks on Encrypted Telerobotic Control Systems

   https://doi.org/10.1109/TMECH.2025.3570933  

   Kwon, Hyukbin; Blevins, Jacob; Ueda, Jun (June 2025, IEEE/ASME Transactions on Mechatronics)

   Networkedcontrol systems are vulnerable to manipulation via data injection to observed states and control commands, resulting in undesired state trajectories and system instabilities. Adversarial attacks against such systems can be implemented in the form of undetectable attacks such that an observer never notices deviations from expected behavior. Even when protected by homomorphic encryption, these systems remain vulnerable to stealthy and perfectly undetectable attacks due to the malleability of encrypted data. This research develops a defense architecture against such undetectable attacks through the fusion of two complementary detection protocols working in conjunction with encryption. The mechanism’s strengths and weaknesses are analyzed for affine transformation-based perfectly undetectable attacks and covert attacks. The attacks are implemented against a mobile robot, and defense performance is analyzed, resulting in a robust defense mechanism that outperforms previous undetectable attack detection methods in terms of detection accuracy and reliability across the two representative attack types. 

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