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Due to the complexity of integrated circuit design and manufacturing process, an increasing number of third parties are outsourcing their untrusted intellectual property (IP) cores to pursue greater economic benefits, which may embed numerous security issues. The covert nature of hardware Trojans (HTs) poses a significant threat to cyberspace, and they may lead to catastrophic consequences for the national economy and personal privacy. To deal with HTs well, it is not enough to just detect whether they are included, like the existing studies. Same as malware, identifying the attack intentions of HTs, that is, analyzing the functions they implement, is of great scientific significance for the prevention and control of HTs. Based on the fined detection, for the first time, this article proposes a two-stage Graph Neural Network model for HTs' multifunctional classification, GNN4HT. In the first stage, GNN4HT localizes HTs, achieving a notable true positive rate (TPR) of 94.28% on the Trust-Hub dataset and maintaining high performance on the TRTC-IC dataset. GNN4HT further transforms the localization results into HT information graphs (HTIGs), representing the functional interaction graphs of HTs. In the second stage, the dataset is augmented through logical equivalence for training and HT functionalities are classified based on the extracted HTIG from the first stage. For the multifunctional classification of HTs, the correct classification rate reached as high as 80.95% at gate-level and 62.96% at register transfer level. This article marks a breakthrough in HT detection, and it is the first to address the multifunctional classification issue, holding significant practical importance and application prospects.
Keyword ：
Gate level Gate level golden free golden free Hardware Hardware hardware Trojan (HT) hardware Trojan (HT) HT information graph (HTIG) HT information graph (HTIG) HT location HT location HT multifunctional classification HT multifunctional classification Integrated circuit modeling Integrated circuit modeling Location awareness Location awareness Logic gates Logic gates register transfer level (RTL) register transfer level (RTL) Security Security Training Training Trojan horses Trojan horses
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| GB/T 7714 | Chen, Lihan , Dong, Chen , Wu, Qiaowen et al. GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification [J]. | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2025 , 44 (1) : 172-185 . |
| MLA | Chen, Lihan et al. "GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification" . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 44 . 1 (2025) : 172-185 . |
| APA | Chen, Lihan , Dong, Chen , Wu, Qiaowen , Liu, Ximeng , Guo, Xiaodong , Chen, Zhenyi et al. GNN4HT: A Two-Stage GNN-Based Approach for Hardware Trojan Multifunctional Classification . | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS , 2025 , 44 (1) , 172-185 . |
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Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, difficulties in handling heterogeneous data, and storage resource bottlenecks. Federated learning has the potential to address these challenges by leveraging widely distributed and heterogeneous data, achieving load balancing of storage and computing resources across multiple nodes, and reducing the risks of privacy leaks and bandwidth resource demands. This paper reviews the process of constructing federated learning based intrusion detection system from the perspective of intrusion detection. Specifically, it outlines six main aspects: application scenario analysis, federated learning methods, privacy and security protection, selection of classification models, data sources and client data distribution, and evaluation metrics, establishing them as key research content. Subsequently, six research topics are extracted based on these aspects. These topics include expanding application scenarios, enhancing aggregation algorithm, enhancing security, enhancing classification models, personalizing model and utilizing unlabeled data. Furthermore, the paper delves into research content related to each of these topics through in-depth investigation and analysis. Finally, the paper discusses the current challenges faced by research, and suggests promising directions for future exploration.
Keyword ：
Anomaly detection Anomaly detection Federated learning Federated learning Internet of things Internet of things Intrusion detection Intrusion detection
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| GB/T 7714 | Zhang, Hao , Ye, Junwei , Huang, Wei et al. Survey of federated learning in intrusion detection [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2024 , 195 . |
| MLA | Zhang, Hao et al. "Survey of federated learning in intrusion detection" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 195 (2024) . |
| APA | Zhang, Hao , Ye, Junwei , Huang, Wei , Liu, Ximeng , Gu, Jason . Survey of federated learning in intrusion detection . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2024 , 195 . |
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The Cyber-Physical System and even the Metaverse will become the second space in which human beings live. While bringing convenience to human beings, it also brings many security threats. These threats may come from software or hardware. There has been a lot of research on managing malware, and there are many mature commercial products, such as antivirus software, firewalls, etc. In stark contrast, the research community on governing malicious hardware is still in its infancy. Chips are the core component of hardware, and hardware Trojans are the primary and complex security issue faced by chips. Detection of hardware Trojans is the first step for dealing with malicious circuits. Due to the limitation of the golden chip and the computational consumption, the existing traditional detection methods are not applicable to very large-scale integration. The performances of traditional machine-learning-based methods depend on the accuracy of the multi-feature representation, and most of the methods may lead to instability because of the difficulty of extracting features manually. In this paper, employing deep learning, a multiscale detection model for automatic feature extraction is proposed. The model is called MHTtext and provides two strategies to balance the accuracy and computational consumption. After selecting a strategy according to the actual situations and requirements, the MHTtext generates the corresponding path sentences from the netlist and employs TextCNN for identification. Further, it can also obtain non-repeated hardware Trojan component information to improve its stability performance. Moreover, a new evaluation metric is established to intuitively measure the model's effectiveness and balance: the stabilization efficiency index (SEI). In the experimental results for the benchmark netlists, the average accuracy (ACC) in the TextCNN of the global strategy is as high as 99.26%, and one of its stabilization efficiency index values ranks first with a score of 71.21 in all comparison classifiers. The local strategy also achieved an excellent effect, according to the SEI. The results show that the proposed MHTtext model has high stability, flexibility, and accuracy, in general.
Keyword ：
computational consumption computational consumption deep learning deep learning gate level gate level hardware Trojan hardware Trojan integrated circuit security integrated circuit security semantic analysis semantic analysis
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| GB/T 7714 | Dong, Chen , Yao, Yinan , Xu, Yi et al. A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection [J]. | SENSORS , 2023 , 23 (12) . |
| MLA | Dong, Chen et al. "A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection" . | SENSORS 23 . 12 (2023) . |
| APA | Dong, Chen , Yao, Yinan , Xu, Yi , Liu, Ximeng , Wang, Yan , Zhang, Hao et al. A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection . | SENSORS , 2023 , 23 (12) . |
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Network intrusion detection system plays a crucial role in protecting the integrity and availability of sensitive assets, where the detected traffic data contain a large amount of time, space, and statistical information. However, existing research lacks the utilization of spatial-temporal multi-granularity data features and the mutual support among different data features, thus making it difficult to specifically and accurately identify anomalies. Considering the distinctions among different granularities, we propose a framework called tri-broad learning system (TBLS), which can learn and integrate the three granular features. To explore the spatial-temporal connotation of the traffic information accurately, a feature dataset containing three granularities is constructed according to the characteristics of time, space, and data content. In this way, we use broad learning basic units to extract abstract features of different granularities and then express these features in different feature spaces to enhance them separately. We use a normal distribution initialization method in BLS to optimize the weights of feature nodes and enhancement nodes for better detection accuracy. The merits of our proposed model are exhibited on the UNSW-NB15, CIC-IDS-2017, CIC-DDoS-2019, and mixed traffic datasets. Experimental results show that TBLS outperforms the typical BLS in terms of various evaluation metrics and time consumption. Compared with other machine learning methods, TBLS achieves better performance metrics.
Keyword ：
Broad learning system Broad learning system Network intrusion detection Network intrusion detection Spatial-temporal multi-granularity Spatial-temporal multi-granularity Traffic information Traffic information
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| GB/T 7714 | Li, Jieling , Zhang, Hao , Liu, Zhihuang et al. Network intrusion detection via tri-broad learning system based on spatial-temporal granularity [J]. | JOURNAL OF SUPERCOMPUTING , 2023 , 79 (8) : 9180-9205 . |
| MLA | Li, Jieling et al. "Network intrusion detection via tri-broad learning system based on spatial-temporal granularity" . | JOURNAL OF SUPERCOMPUTING 79 . 8 (2023) : 9180-9205 . |
| APA | Li, Jieling , Zhang, Hao , Liu, Zhihuang , Liu, Yanhua . Network intrusion detection via tri-broad learning system based on spatial-temporal granularity . | JOURNAL OF SUPERCOMPUTING , 2023 , 79 (8) , 9180-9205 . |
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Due to technical barriers and economic costs, malicious circuits, known as hardware Trojans, are easily implanted in the complicated integrated circuit design and manufacturing process, which can lead to many disastrous consequences, such as denial of service, information leakage, performance degradation, etc. Research on how to detecting hardware Trojans has grown into a significantly open issue over the past decade. While, for very large scale integrated circuits, numerous new challenges deserve our full attention, including golden -free chip reference, automatic feature engineering, hardware Trojan localization, and scalable framework. In response to the above challenges, a fine-grained gate-level hardware Trojan detection approach is proposed in this paper, named GateDet, from improving earlier circuit graph modeling to developing a detection framework based on Bidirectional Graph Convolution Networks with a timely information fusion strategy. GateDet achieves automatic feature circuit extraction and further overcomes the original neighborhood limitation of Bidirectional Graph Convolution Network. Moreover, for large-scale training, it comprehensively considers the problems of sample imbalance and boundary network, and develops a circuit directed graph sampling method based on GraphSAINT, which improves the training performance of the directed graph framework. From experiments, GateDet shows high scalability on 24 benchmarks of TrustHub. It could be used to learn about adaptive structural feature extraction for different Trojans simultaneously. Compared to the existing gate-level detections, the fine-grained results of GateDet are more accurate and can be used to track suspicious structures, reducing manual review.
Keyword ：
Gate-level Gate-level Golden-free Golden-free Graph Neural Network Graph Neural Network Hardware Trojan Hardware Trojan Static detection Static detection
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| GB/T 7714 | Cheng, Dong , Dong, Chen , He, Wenwu et al. A fine-grained detection method for gate-level hardware Trojan base on bidirectional Graph Neural Networks [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (10) . |
| MLA | Cheng, Dong et al. "A fine-grained detection method for gate-level hardware Trojan base on bidirectional Graph Neural Networks" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 35 . 10 (2023) . |
| APA | Cheng, Dong , Dong, Chen , He, Wenwu , Chen, Zhenyi , Liu, Ximeng , Zhang, Hao . A fine-grained detection method for gate-level hardware Trojan base on bidirectional Graph Neural Networks . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (10) . |
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With the rapid development of network technology, the Internet has brought significant convenience to various sectors of society, holding a prominent position. Due to the unpredictable and severe consequences resulting from malicious attacks, the detection of anomalous network traffic has garnered considerable attention from researchers over the past few decades. Accurately labeling a sufficient amount of network traffic data as a training dataset within a short period of time is a challenging task, given the rapid and massive generation of network traffic data. Furthermore, the proportion of malicious attack traffic is relatively small compared to the overall traffic data, and the distribution of traffic data across different types of malicious attacks also varies significantly. To address the aforementioned challenges, this paper presents a novel network anomaly detection algorithm based on semi-supervised learning and adaptive multiclass balancing. Building upon the assumption of consistent distribution between labeled and unlabeled data, this paper introduces the multiclass split balancing strategy and the adaptive confidence threshold function. These innovative approaches aim to tackle the issue of the multiclass imbalanced in traffic data. By leveraging the mutually beneficial relationship between semi-supervised learning and ensemble learning, this paper presents the collaborative rotation forest algorithm. This algorithm is specifically designed to enhance performance of anomaly detection in an environment with label inadequacy. Several comparative experiments conducted on the NSL-KDD, UNSW-NB15, and ToN-IoT demonstrate that the proposed algorithm achieves significant improvements in performance. Specifically, it enhances precision by 1.5-5.7%, recall by 1.5-5.7%, and F-Measure by 1.4-4.3% compared to the state-of-the-art algorithms.
Keyword ：
Anomaly detection Anomaly detection Class imbalance Class imbalance Ensemble learning Ensemble learning Network intrusion detection Network intrusion detection Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Zhang, Hao , Xiao, Zude , Gu, Jason et al. A network anomaly detection algorithm based on semi-supervised learning and adaptive multiclass balancing [J]. | JOURNAL OF SUPERCOMPUTING , 2023 , 79 (18) : 20445-20480 . |
| MLA | Zhang, Hao et al. "A network anomaly detection algorithm based on semi-supervised learning and adaptive multiclass balancing" . | JOURNAL OF SUPERCOMPUTING 79 . 18 (2023) : 20445-20480 . |
| APA | Zhang, Hao , Xiao, Zude , Gu, Jason , Liu, Yanhua . A network anomaly detection algorithm based on semi-supervised learning and adaptive multiclass balancing . | JOURNAL OF SUPERCOMPUTING , 2023 , 79 (18) , 20445-20480 . |
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Network intrusion detection plays an important role as tools for managing and identifying potential threats, which presents various challenges. Redundant features and difficult marking in data cause a long-term problem in network traffic detection. In this paper, we propose a semi-supervised machine learning framework based on multi-strategy feature filtering, principal component analysis (PCA), and an improved Tri-Light Gradient Boosting Machine (Tri-LightGBM) based on stratified sampling. This multi-strategy feature filtering method employing Fisher score and Information gain can select features that have good category discrimination and are more relevant to category labels. After that, we combine PCA to convert multiple features into comprehensive features, which are used as the input of the Tri-LightGBM model. Tri-LightGBM can exploit unlabeled data cooperatively and maintain a large disagreement among the base learners. Moreover, we propose a stratified sampling based on labeled categories to reduce the probability of being selected as the same category during the model update process. Thus, the Tri-LightGBM based on stratified sampling can compensate for the classification error rate caused by the imbalance of the dataset. The semi-supervised machine learning framework is evaluated on two intrusion detection evaluation datasets, namely UNSW-NB15 and CIC-IDS-2017. The evaluation results show that the multi-strategy feature filtering method can increase the accuracy, recall, precision, and F-measure by up to 0.5%, and reduce the false-positive rate by up to 0.5%. Furthermore, the precision rate of minority categories can be increased by about 1-2%.
Keyword ：
Fisher score Fisher score Information gain Information gain Network intrusion detection Network intrusion detection PCA PCA Tri-LightGBM Tri-LightGBM
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| GB/T 7714 | Li, Jieling , Zhang, Hao , Liu, Yanhua et al. Semi-supervised machine learning framework for network intrusion detection [J]. | JOURNAL OF SUPERCOMPUTING , 2022 , 78 (11) : 13122-13144 . |
| MLA | Li, Jieling et al. "Semi-supervised machine learning framework for network intrusion detection" . | JOURNAL OF SUPERCOMPUTING 78 . 11 (2022) : 13122-13144 . |
| APA | Li, Jieling , Zhang, Hao , Liu, Yanhua , Liu, Zhihuang . Semi-supervised machine learning framework for network intrusion detection . | JOURNAL OF SUPERCOMPUTING , 2022 , 78 (11) , 13122-13144 . |
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Owing to the buckling problem of corrugated steel webs, up to now the span of prestressed concrete box girder bridges with corrugated steel webs has not exceeded 200m. Based on the analysis of buckling strength of corrugated steel webs, a new type of large span prestressed concrete box girder bridge with double-limb corrugated steel webs is proposed in this paper. It reduces the free height of the corrugated steel webs by filling core concrete between the double-limbed corrugated steel webs, and increases buckling strength of the webs. Additionally, the optimization technique of strong top plate and thin bottom plate is applied to reduce the self-weight of constant load without reducing the cross-sectional stiffness. The result of the trial design shows that this bridge type is applicable and economical, which can achieve a breakthrough in the span of prestressed concrete box girder bridges with corrugated steel webs. © IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report. All rights reserved.
Keyword ：
Box girder bridges Box girder bridges Buckling Buckling Concrete beams and girders Concrete beams and girders Plates (structural components) Plates (structural components) Prestressed concrete Prestressed concrete Steel bridges Steel bridges
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| GB/T 7714 | Bing, Shangguan , Su, Qingtian , Li, Tianyu et al. Trial Design of a New Type of Large-Span Double-Limb Prestressed Concrete Box Girder Bridge with Corrugated Steel Webs [C] . 2022 : 165-172 . |
| MLA | Bing, Shangguan et al. "Trial Design of a New Type of Large-Span Double-Limb Prestressed Concrete Box Girder Bridge with Corrugated Steel Webs" . (2022) : 165-172 . |
| APA | Bing, Shangguan , Su, Qingtian , Li, Tianyu , Zhang, Hao . Trial Design of a New Type of Large-Span Double-Limb Prestressed Concrete Box Girder Bridge with Corrugated Steel Webs . (2022) : 165-172 . |
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A robust network intrusion detection system (NIDS) plays an important role in cyberspace security for protecting confidential systems from potential threats. In real world network, there exists complex correlations among the various types of network traffic information, which may be respectively attributed to different abnormal behaviors and should be make full utilized in NIDS. Regarding complex network traffic information, traditional learning based abnormal behavior detection methods can hardly meet the requirements of the real world network environment. Existing methods have not taken into account the impact of various modalities of data, and the mutual support among different data features. To address the concerns, this paper proposes a multi-dimensional feature fusion and stacking ensemble mechanism (MFFSEM), which can detect abnormal behaviors effectively. In order to accurately explore the connotation of traffic information, multiple basic feature datasets are established considering different aspects of traffic information such as time, space, and load. Then, considering the association and correlation among the basic feature datasets, multiple comprehensive feature datasets are set up to meet the requirements of real world abnormal behavior detection. In specific, stacking ensemble learning is conducted on multiple comprehensive feature datasets, and thus an effective multi-dimensional global anomaly detection model is accomplished. The experimental results on the dataset KDD Cup 99, NSL-KDD, UNSW-NB15, and CIC-IDS2017 have shown that MFFSEM significantly outperforms the basic and meta classifiers adopted in our method. Furthermore, its detection performance is superior to other well-known ensemble approaches. (C) 2021 Elsevier B.V. All rights reserved.
Keyword ：
Feature fusion Feature fusion Multi-dimensional Multi-dimensional Network intrusion detection Network intrusion detection Stacking ensemble learning Stacking ensemble learning
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| GB/T 7714 | Zhang, Hao , Li, Jie-Ling , Liu, Xi-Meng et al. Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection [J]. | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2021 , 122 : 130-143 . |
| MLA | Zhang, Hao et al. "Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection" . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 122 (2021) : 130-143 . |
| APA | Zhang, Hao , Li, Jie-Ling , Liu, Xi-Meng , Dong, Chen . Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2021 , 122 , 130-143 . |
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Evolutionary game theory is widely applied in network attack and defense. The existing network attack and defense analysis methods based on evolutionary games adopt the bounded rationality hypothesis. However, the existing research ignores that both sides of the game get more information about each other with the deepening of the network attack and defense game, which may cause the attacker to crack a certain type of defense strategy, resulting in an invalid defense strategy. The failure of the defense strategy reduces the accuracy and guidance value of existing methods. To solve the above problem, we propose a reward value learning mechanism (RLM). By analyzing previous game information, RLM automatically incentives or punishes the attack and defense reward values for the next stage, which reduces the probability of defense strategy failure. RLM is introduced into the dynamic network attack and defense process under incomplete information, and a multistage evolutionary game model with a learning mechanism is constructed. Based on the above model, we design the optimal defense strategy selection algorithm. Experimental results demonstrate that the evolutionary game model with RLM has better results in the value of reward and defense success rate than the evolutionary game model without RLM. © 2021 Yanhua Liu et al.
Keyword ：
Computer crime Computer crime Game theory Game theory Network security Network security
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| GB/T 7714 | Liu, Yanhua , Chen, Hui , Zhang, Hao et al. Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory [J]. | Security and Communication Networks , 2021 , 2021 . |
| MLA | Liu, Yanhua et al. "Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory" . | Security and Communication Networks 2021 (2021) . |
| APA | Liu, Yanhua , Chen, Hui , Zhang, Hao , Liu, Ximeng . Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory . | Security and Communication Networks , 2021 , 2021 . |
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