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学者姓名：张浩
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网络流量数据的获取较为容易,而对流量数据进行标记相对困难。半监督学习利用少量有标签数据和大量无标签数据进行训练,减少了对有标签数据的需求,能较好适应海量网络流量数据下的异常检测。文章对近年来的半监督网络异常检测领域的论文进行深入调研。首先,介绍了一些基本概念,并深入剖析了网络异常检测中使用半监督学习策略的必要性；然后,从半监督机器学习、半监督深度学习和半监督学习结合其他范式三个方面,分析和比较了半监督网络异常检测领域近年来的论文,并进行归纳和总结；最后,对当前半监督网络异常检测领域进行了现状分析和未来展望。
Keyword ：
入侵检测 入侵检测 半监督学习 半监督学习 异常检测 异常检测 标签稀缺 标签稀缺
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| GB/T 7714 | 张浩 , 谢大智 , 胡云晟 et al. 基于半监督学习的网络异常检测研究综述 [J]. | 信息网络安全 , 2024 , 24 (04) : 491-508 . |
| MLA | 张浩 et al. "基于半监督学习的网络异常检测研究综述" . | 信息网络安全 24 . 04 (2024) : 491-508 . |
| APA | 张浩 , 谢大智 , 胡云晟 , 叶骏威 . 基于半监督学习的网络异常检测研究综述 . | 信息网络安全 , 2024 , 24 (04) , 491-508 . |
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In recent years, the rapid advancement of image generation techniques has resulted in the widespread abuse of manipulated images, leading to a crisis of trust and affecting social equity. Thus, the goal of our work is to detect and localize tampered regions in images. Many deep learning based approaches have been proposed to address this problem, but they can hardly handle the tampered regions that are manually fine-tuned to blend into image background. By observing that the boundaries of tempered regions are critical to separating tampered and non-tampered parts, we present a novel boundary-guided approach to image manipulation detection, which introduces an inherent bias towards exploiting the boundary information of tampered regions. Our model follows an encoder-decoder architecture, with multi-scale localization mask prediction, and is guided to utilize the prior boundary knowledge through an attention mechanism and contrastive learning. In particular, our model is unique in that 1) we propose a boundary-aware attention module in the network decoder, which predicts the boundary of tampered regions and thus uses it as crucial contextual cues to facilitate the localization; and 2) we propose a multi-scale contrastive learning scheme with a novel boundary-guided sampling strategy, leading to more discriminative localization features. Our state-of-art performance on several public benchmarks demonstrates the superiority of our model over prior works.
Keyword ：
Contrastive learning Contrastive learning Decoding Decoding Deepfakes Deepfakes Feature extraction Feature extraction Image manipulation detection/localization Image manipulation detection/localization Location awareness Location awareness Task analysis Task analysis Visualization Visualization
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| GB/T 7714 | Liu, Wenxi , Zhang, Hao , Lin, Xinyang et al. Attentive and Contrastive Image Manipulation Localization With Boundary Guidance [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 6764-6778 . |
| MLA | Liu, Wenxi et al. "Attentive and Contrastive Image Manipulation Localization With Boundary Guidance" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 6764-6778 . |
| APA | Liu, Wenxi , Zhang, Hao , Lin, Xinyang , Zhang, Qing , Li, Qi , Liu, Xiaoxiang et al. Attentive and Contrastive Image Manipulation Localization With Boundary Guidance . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 6764-6778 . |
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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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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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异常检测系统在网络空间安全中起着至关重要的作用,为网络安全提供有效的保障.对于复杂的网络流量信息,传统的单一的分类器往往无法同时具备较高检测精确度和较强的泛化能力.此外,基于全特征的异常检测模型往往会受到冗余特征的干扰,影响检测的效率和精度.针对这些问题,本文提出了一种基于平均特征重要性的特征选择和集成学习的模型,选取决策树(DT)、随机森林(RF)、额外树(ET)作为基分类器,建立投票集成模型,并基于基尼系数计算基分类器的平均特征重要性进行特征选择.在多个数据集上的实验评估结果表明,本文提出的集成模型优于经典集成学习模型及其他著名异常检测集成模型.且提出的基于平均特征重要性的特征选择方法可以使集成模型准确率平均进一步提升约0.13％,训练时间平均节省约30％.
Keyword ：
异常检测 异常检测 异常流量 异常流量 特征选择 特征选择 网络入侵检测 网络入侵检测 集成学习 集成学习
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| GB/T 7714 | 庄锐 , 张浩 . 基于平均特征重要性和集成学习的异常检测 [J]. | 计算机系统应用 , 2023 , 32 (6) : 60-69 . |
| MLA | 庄锐 et al. "基于平均特征重要性和集成学习的异常检测" . | 计算机系统应用 32 . 6 (2023) : 60-69 . |
| APA | 庄锐 , 张浩 . 基于平均特征重要性和集成学习的异常检测 . | 计算机系统应用 , 2023 , 32 (6) , 60-69 . |
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软件定义网络(Software Defined Networking,SDN)作为一种新兴的网络范式,在带来便利性的同时也引入了更为严峻的分布式拒绝服务攻击(Distributed Denial of Service Attacks,DDoS)风险.现有的模型通常是使用机器学习模型来检测DDoS攻击,忽略了模型给SDN控制器带来的额外开销.为了更加高效且精确地检测DDoS攻击,文章采取了多级检测模块的方式,即一级模块通过计算当前流量窗口的联合熵快速检测异常,二级模块采用半监督模型,并使用特征选择、multi-training算法、多重聚类等技术,通过训练多个局部模型提高检测性能.与现有的其他模型相比,该模型在多个数据集上均表现更好,拥有更好的检测精度和泛化能力.
Keyword ：
分布式拒绝服务攻击 分布式拒绝服务攻击 半监督学习 半监督学习 统计学习 统计学习 软件定义网络 软件定义网络
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| GB/T 7714 | 王智 , 张浩 , 顾建军 . SDN网络中基于联合熵与多重聚类的DDoS攻击检测 [J]. | 信息网络安全 , 2023 , (10) : 1-7 . |
| MLA | 王智 et al. "SDN网络中基于联合熵与多重聚类的DDoS攻击检测" . | 信息网络安全 10 (2023) : 1-7 . |
| APA | 王智 , 张浩 , 顾建军 . SDN网络中基于联合熵与多重聚类的DDoS攻击检测 . | 信息网络安全 , 2023 , (10) , 1-7 . |
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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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探讨信息安全专业课程思政教学体系建设思路。围绕我国网络强国战略，以立德树人为根本，以社会主义核心价值观为导向，融入思政教学元素，以科教兴国战略为指引构建思政教学体系，建立教学评价与反馈机制。
Keyword ：
信息安全 信息安全 思政元素 思政元素 课程思政 课程思政
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| GB/T 7714 | 张浩 , 郭文忠 , 董晨 et al. 信息安全专业课程思政教学体系建设研究 [J]. | 科技创业月刊 , 2022 , 35 (06) : 145-147 . |
| MLA | 张浩 et al. "信息安全专业课程思政教学体系建设研究" . | 科技创业月刊 35 . 06 (2022) : 145-147 . |
| APA | 张浩 , 郭文忠 , 董晨 , 肖祖德 . 信息安全专业课程思政教学体系建设研究 . | 科技创业月刊 , 2022 , 35 (06) , 145-147 . |
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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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