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HTDet:A Clustering Method Using Information Entropy for Hardware Trojan Detection

Tsinghua Science and technology 2021年第1期26卷 48-61页

作者： Renjie Lu Haihua Shen Zhihua Feng Huawei Li Wei Zhao Xiaowei Li the University of Chinese Academy of Sciences Beijing 101408China Beijing Institute of Computer Technology and Application Beijing 100854China the State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential *** this paper,we propose HTDet,a novel HT detection method using information entropybased *** maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the *** implies that the regions with the low transitions will provide much more abundant and more important information for HT *** HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under *** DBSCAN is an unsupervised learning algorithm,that can improve the applicability of *** addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan *** on circuit benchmarks demonstrate the effectiveness of HTDet.

关键词： Hardware Trojan(HT)detection information entropy Density-Based Spatial Clustering of Applications with Noise(DBSCAN) unsupervised learning clustering mutual information test patterns generation

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Self-distillation Enhanced Vertical Wavelet Spatial Attention for Person Re-identification 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Yuxuan Tan, Huibin Lan, Long Teng, Xiao Ren, Jing Zhang, Yongjun Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Beijing100071 China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Person re-identification is a challenging problem in computer vision, aiming to accurately match and recognize the same individual across different viewpoints and cameras. Due to significant variations in appearance under different scenes, person re-identification requires highly discriminative features. Wavelet features contain richer phase and amplitude information as well as rotational invariance, demonstrating good performance in various visual tasks. However, through our observations and validations, we have found that the vertical component within wavelet features exhibits stronger adaptability and discriminability in person re-identification. It better captures the body contour and detailed information of pedestrians, which is particularly helpful in distinguishing differences among individuals. Based on this observation, we propose a vertical wavelet spatial attention only with the vertical component in the high frequency specifically designed for feature extraction and matching in person re-identification. To enhance spatial semantic consistency and facilitate the transfer of knowledge between different layers of wavelet attention in the neural network, we introduce a self-distillation enhancement method to constrain shallow and deep spatial attention. Experimental results on Market-1501 and DukeMTMC-reID datasets validate the effectiveness of our model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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IntNet: A novel framework using reconstructed mutation samples and a biologically informed neural network for pathway analysis

IntNet: A novel framework using reconstructed mutation sampl...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Li Li, Jie Tan, Weilong Harbin Institute of Technology School of Computer Science and Technology Harbin China National Key Laboratory of Smart Farming Technology and Systems College of Computing Harbin Institute of Technology Harbin China Technology Center for Medicines Center for Medicines Nanjing China

ISBN: (纸本)9798350386226

Most pathway analysis methods based on simple nucleotide variations overlook mutations occurring in genes outside the pathway, fail to address mutation heterogeneity with targeted measures, and do not consider the functional relationships among gene components within biomolecular networks when modeling the contributions of biomarkers to pathways. So, we proposed a novel framework, named IntNet, for pathway analysis based on simple nucleotide variations. The core components of IntNet are sample reconstruction (SC) and a new biologically informed neural network model (BINN). To address the difficulty of extracting effective features caused by the mutation heterogeneity, IntNet reconstructs mutation samples based on the hypothesis that mutations in patients with the same phenotype may cluster in similar network regions. BINN uses the human gene network and expanded biological pathways to construct its neural network architecture. BINN implements mutation signaling propagation along the human gene network and integration within the pathways. IntNet's performance was validated using multiple cancer datasets. Experimental results demonstrated that IntNet could identify more accurate and reliable pathways. © 2024 IEEE.

关键词： Nucleotides

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A large scale parallel fluid-structure interaction computing platform for simulating structural responses to a detonation shock

A large scale parallel fluid-structure interaction computing...

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作者： Zhang, Sen Guo, Xiao-Wei Li, Chao Liu, Yi Fan, Sijiang Zhao, Ran Yang, Canqun Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

Due to the intrinsic nature of multi-physics, it is prohibitively complex to design and implement a simulation software platform for study of structural responses to a detonation shock. In this article, a partitioned fluid-structure interaction computing platform is designed for parallel simulating structural responses to a detonation shock. The detonation and wave propagation are modeled in an open-source multi-component solver based on OpenFOAM and blastFoam, and the structural responses are simulated through the finite element library ***. To capture the interaction dynamics between the fluid and the structure, both solvers are adapted to preCICE. For improving the parallel performance of the computing platform, the inter-solver data is exchanged by peer-to-peer communications and the intermediate server in conventional multi-physics software is eliminated. Furthermore, the coupled solver with detonation support has been deployed on a computing cluster after considering the distributed data storage and load-balancing between solvers. The 3D numerical result of structural responses to a detonation shock is presented and analyzed. On 256 processor cores, the speedup ratio of the simulations for a detonation shock reach 178.0 with 5.1 million of mesh cells and the parallel efficiency achieve 69.5%. The results demonstrate good potential of massively parallel simulations. Overall, a general-purpose fluid-structure interaction software platform with detonation support is proposed by integrating open source codes. And this work has important practical significance for engineering application in fields of construction blasting, mining, and so forth. © 2021 John Wiley & Sons, Ltd.

关键词： Detonation

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Degenerate polarization entangled photon source based on a single Ti-diffusion lithium niobate waveguide in a polarization Sagnac interferometer

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Chinese Physics B 2023年第8期32卷 69-73页

作者：孙宇孙昌伟周唯杨然端家晨龚彦晓徐平祝世宁 National Laboratory of Solid State Microstructures and School of Physics Nanjing UniversityNanjing 210093China NanZhi Institude of Advanced Optoelectronic Integration Nanjing 210018China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Combining a Ti-diffusion periodically poled lithium niobate(PPLN)waveguide with a Sagnac interferometer,two opposite directions type-II spontaneous parametric down conversions(SPDC)occur coherently and yield a high brightness,high stability polarization entanglement *** source produces degenerate photon pairs at 1540.4 nm with a brightness of B=(1.36±0.03)×10^(6) pairs/(s·nm·m W).We perform quantum state tomography to reconstruct the density matrix of the output state and obtain a fidelity of F=0.983±*** high brightness and phase stability of our waveguide source enable a wide range of quantum information experiments operating at a low pump power as well as hold the advantage in mass production which can promote the practical applications of quantum technologies.

关键词： polarization entanglement source Sagnac interferometer quantum information lithium niobate waveguide

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OL-CBBA: An Online Task Allocation Algorithm under Weak Communication Conditions 29

OL-CBBA: An Online Task Allocation Algorithm under Weak Comm...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Jun Huang, Wanrong Wu, Honglin Cai, Zhongxuan Zhang, Yongjun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology Changsha China

ISBN: (纸本)9798350330717

Multiple unmanned aerial vehicles (UAVs) and multiple tasks allocation problem is difficult to solve. Existing task allocation algorithms assume that the UAVs' position is static, and cannot assign tasks with the changing UAVs' position simultaneously during task execution. Those algorithms reduce the efficiency of task allocation. In this paper, we propose an online consensus-based bundle algorithm (OL-CBBA) under weak communication for dynamic task allocation. We consider the situation that the location information of UAVs will constantly change during the dynamic execution of tasks. The algorithm first improves the static CBBA to an online algorithm by updating the task marginal score in task path. Moreover, we specify a flag for the convergence of individual tasks, allowing UAVs to start executing tasks earlier. Extensive comparative experiments prove the highly consistent efficiency of OL-CBBA under weak communication conditions. Specifically, the proposed OL-CBBA attains up to 22% improvement compared with CBBA. © 2023 IEEE.

关键词： Auction Algorithm Decentralized System Dynamic Task Allocation Weak Communication

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Acorns: A Framework for Accelerating Deep Neural Networks with Input Sparsity 19

Acorns: A Framework for Accelerating Deep Neural Networks wi...

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Proceedings of the International Conference on Parallel architectures and Compilation Techniques

作者： Xiao Dong Lei Liu Peng Zhao Guangli Li Jiansong Li Xueying Wang Xiaobing Feng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728136134

Deep neural networks have been employed in a broad range of applications, including face detection, natural language processing, and autonomous driving. Yet, the neural networks with the capability to tackle real-world problems are intrinsically expensive in computation, hindering the usage of these models. Sparsity in the input data of neural networks provides an optimizing opportunity. However, harnessing the potential performance improvement on modern CPU faces challenges raised by sparse computations of the neural network, such as cache-unfriendly memory accesses and efficient sparse kernel *** this paper, we propose Acorns, a framework to accelerate deep neural networks with input sparsity. In Acorns, sparse input data is organized into our designed sparse data layout, which allows memory-friendly access for kernels in neural networks and opens the door for many performance-critical optimizations. Upon that, Acorns generates efficient sparse kernels for operators in neural networks from kernel templates, which combine directions that express specific optimizing transformations to be performed, and straightforward code that describes the *** evaluations demonstrate Acorns can outperform state-of-the-art baselines by significant speedups. On the real-world detection task in autonomous driving, Acorns demonstrates 1.8-22.6× performance improvement over baselines. Specifically, the generated programs achieve 1.8-2.4× speedups over Intel MKL-DNN, 3.0-8.8× speedups over TensorFlow, and 11.1-13.2× speedups over Intel MKL-Sparse.

关键词： Compiler

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Network intrusion detection method based on CAE-ResNet-BiLSTM 4

Network intrusion detection method based on CAE-ResNet-BiLST...

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Yansong, Liu Pin, Wang College of Computer Science and Technology Shenyang University of Chemical Technology Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry Shenyang110142 China Shenyang Institute of computing technology Chinese academy of sciences China

ISBN: (纸本)9781510686106

To address the issue of low detection accuracy in existing network intrusion detection technologies caused by insufficient learning of data feature information, this paper proposes a network intrusion detection model based on Convolutional Autoencoder (CAE), Residual Network (ResNet), and Bidirectional Long Short-Term Memory (BiLSTM) networks. This method first performs preliminary feature extraction using a CAE, then enhances feature processing capabilities with ResNet, and finally captures time series information with BiLSTM. Classification is carried out through a fully connected layer and an output layer. The validation results on the NSL-KDD dataset and UNSW-NB15 dataset show that the model achieved accuracy rates of 99.34% and 82.47%, with false positive rates of 0.376% and 6.088%, respectively. The experimental results indicate that the proposed model effectively addresses issues such as low detection accuracy caused by insufficient learning of data feature information. © 2024 SPIE.

关键词： Network intrusion

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Towards Radar Emitter Recognition in Changing Environments with Domain Generalization 2

Towards Radar Emitter Recognition in Changing Environments w...

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2nd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2023

作者： Wu, Honglin Li, Xueqiong Lan, Long Xu, Liyang Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798400716430

Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task. However, in the real world, the changing EW environment results in inconsistent signal distribution, such as the pulse repetition interval (PRI) mismatch between different detected scenes. In this paper, we propose a novel domain generalization framework to improve the adaptability of signal recognition in changing environments. Specifically, we first design several noise generators to simulate varied scenes. Different from conventional augmentation methods, our introduced generators carefully enhance the diversity of the detected signals and meanwhile maintain the semantic features of the signals. Moreover, we propose a signal scene domain classifier that works in the manner of adversarial learning. The proposed classifier guarantees the signal predictor to generalize to different scenes. Extensive comparative experiments prove the proposed method's superiority. © 2023 ACM.

关键词： Electronic warfare

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NeuVSA: A Unified and Efficient Accelerator for Neural Vector Search 31

NeuVSA: A Unified and Efficient Accelerator for Neural Vecto...

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31st IEEE International Symposium on High Performance computer architecture, HPCA 2025

作者： Yuan, Ziming Dai, Lei Li, Wen Zhang, Jie Liang, Shengwen Wang, Ying Liu, Cheng Li, Huawei Li, Xiaowei Guo, Jiafeng Wang, Peng Chen, Renhai Zhang, Gong Shanxi University School of Computer and Information Technology China Peking University School of Computer Science China Institute of Computing Technology CAS State Key Lab of Processors China University of Chinese Academy of Sciences China Zhongguancun National Laboratory Beijing China Institute of Computing Technology CAS Key Lab of Network Data Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798331506476

Neural Vector Search (NVS) has exhibited superior search quality over traditional key-based strategies for information retrieval tasks. An effective NVS architecture requires high recall, low latency, and high throughput to enhance user experience and cost-efficiency. However, implementing NVS on existing neural network accelerators and vector search accelerators is sub-optimal due to the separation between the embedding stage and vector search stage at both algorithm and architecture levels. Fortunately, we unveil that Product Quantization (PQ) opens up an opportunity to break separation. However, existing PQ algorithms and accelerators still focus on either the embedding stage or the vector search stage, rather than both simultaneously. Simply combining existing solutions still follows the beaten track of separation and suffers from insufficient parallelization, frequent data access conflicts, and the absence of scheduling, thus failing to reach optimal recall, latency, and throughput. To this end, we propose a unified and efficient NVS accelerator dubbed NeuVSA based on algorithm and architecture co-design philosophy. Specifically, on the algorithm level, we propose a learned PQ-based unified NVS algorithm that consolidates two separate stages into the same computing and memory access paradigm. It integrates an end-to-end joint training strategy to learn the optimal codebook and index for enhanced recall and reduced PQ complexity, thus achieving smoother acceleration. On the architecture level, we customize a homogeneous NVS accelerator based on the unified NVS algorithm. Each sub-accelerator is optimized to exploit all parallelism exposed by unified NVS, incorporating a structured index assignment strategy and an elastic on-chip buffer to alleviate buffer conflicts for reduced latency. All sub-accelerators are coordinated using a hardware-aware scheduling strategy for boosted throughput. Experimental results show that the joint training strategy improves recall

关键词： Network embeddings

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