2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)最新文献_第5页

Advanced Persistent Threats Detection based on Deep Learning Approach 基于深度学习方法的高级持续威胁检测

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128062

H. Eke, Andrei V. Petrovski

{"title":"Advanced Persistent Threats Detection based on Deep Learning Approach","authors":"H. Eke, Andrei V. Petrovski","doi":"10.1109/ICPS58381.2023.10128062","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128062","url":null,"abstract":"Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses “low-and-slow” approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed “$APT_{DASAC}$” to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and pre-processing, (ii) Data analysis layer; applies the core process of “$APT_{DASAC}$” to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer; the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129459805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prediction and scheduling for blast furnace gas generation based on time series feature extraction 基于时间序列特征提取的高炉煤气生成预测与调度

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128061

Huihang Li, Jie Hu, Qingfeng Yang, Luefeng Chen, Min Wu

{"title":"Prediction and scheduling for blast furnace gas generation based on time series feature extraction","authors":"Huihang Li, Jie Hu, Qingfeng Yang, Luefeng Chen, Min Wu","doi":"10.1109/ICPS58381.2023.10128061","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128061","url":null,"abstract":"Due to the significant time lag and under-regulation, predicting the blast furnace gas generation and formulating its scheduling strategy is complex. This paper proposes a blast furnace gas generation prediction method based on time series feature extraction and designs a blast furnace gas scheduling strategy based on the prediction results. Firstly, Pearson correlation analysis is used to identify the parameters that have a significant correlation with the blast furnace gas generation, and the selected parameters are decomposed into several intrinsic mode components with different frequency characteristics using the complete ensemble empirical mode decomposition; Then, the principal component analysis method is used to extract the principal components of several intrinsic modal components, and these principal components are employed as the inputs of long short-term memory neural network to predict the blast furnace gas generation; Finally, according to the prediction results designs the scheduling strategy of blast furnace gas. The experiment and contrast experiments are carried out with the industrial field data, and experimental results illustrate that the proposed method is correct and effective.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stabilization of Stochastic Linear Systems via Path-dependent Feedback Control 基于路径相关反馈控制的随机线性系统镇定

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128019

Ruizhe Yu, Xiaofeng Zong

引用次数: 0

Bounded Tracking of Uncertain Multiagent Systems with Time-Delays and Multiplicative Noises 时滞与乘性噪声不确定多智能体系统的有界跟踪

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128036

Guoliu Chen, Xiaofeng Zong

引用次数: 0

Allocating defense resources for spatial cyber-physical power systems based on deep reinforcement learning 基于深度强化学习的空间信息物理动力系统防御资源分配

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128014

Zhengcheng Dong, Mian Tang, Meng Tian

引用次数: 0

Prediction model of photovoltaic output power based on VMD-EMD-BiLSTM 基于VMD-EMD-BiLSTM的光伏输出功率预测模型

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128087

Shaolong Zheng, Danyun Li

{"title":"Prediction model of photovoltaic output power based on VMD-EMD-BiLSTM","authors":"Shaolong Zheng, Danyun Li","doi":"10.1109/ICPS58381.2023.10128087","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128087","url":null,"abstract":"The accuracy of photovoltaic (PV) power prediction is significantly influenced by the high complexity and volatility of the PV sequence. The existing methods for predicting photoelectric power are difficult to effectively mine and analyze the internal variation law of data. To improve the accuracy of PV power prediction, a new method is proposed that first performs variational mode decomposition (VMD) and empirical mode decomposition (EMD), and then establishes a bi-directional long and short-term memory neural network (BiLSTM) for PV output power prediction. The proposed method extracts the amplitude and frequency characteristics of the PV output power series through VMD. After that, the residual term with strong non-stationarity is generated, which still has more sequence characteristics. The residual term is then decomposed by EMD for the second time to extract more features. Finally, the BiLSTM model is established to conduct bidirectional mining for PV power data and predict PV output power. The actual PV data is used to test the experimental results, which show that the proposed VMD-EMD-BiLSTM prediction model has better prediction performance.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124892648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Digital Twin Development: Mathematical Modeling 数字孪生发展:数学建模

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128007

Dennis Krummacker, M. Reichardt, Christoph Fischer, H. Schotten

引用次数: 0

Incipient Gradual Fault Detection via Transformed Component and Dissimilarity Analysis 基于变换分量和不相似度分析的早期渐进故障检测

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128091

Lingxia Mu, Wenzhe Sun, Youmin Zhang, Nan Feng

引用次数: 0

An improved 3D reconstruction method for weak texture objects combined with calibration and ICP registration 结合标定和ICP配准的弱纹理物体三维重建方法

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128042

Lang Qin, Xin Chen, Xuan Gong

{"title":"An improved 3D reconstruction method for weak texture objects combined with calibration and ICP registration","authors":"Lang Qin, Xin Chen, Xuan Gong","doi":"10.1109/ICPS58381.2023.10128042","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128042","url":null,"abstract":"3D reconstruction of weak texture objects is a major area of interest within the field of high-precision reconstruction. Because fewer feature points can be extracted from weak texture objects, the classical method of points cloud registration by extracting feature points is no longer feasible anymore, yet registration is an indispensable step for multiview 3D reconstruction. The classical method of point cloud registration, the ICP algorithm, requires less feature point extraction but depends on the initial iteration position. When the initial position is not suitable, the registration process becomes especially time-consuming and tends to degrade the registration accuracy by falling into the problem of local optima. Therefore, a novel method that combines calibration and ICP registration is developed to complete the reconstruction of weakly textured surfaces. By using the calibration results to roughly unify multiple point clouds under the same coordinate system, which gives a better initial position for subsequent ICP registration, to reduce the iterative time consumption problem of ICP algorithms. Experiments demonstrate the feasibility and transferability of the developed method and verify the effectiveness of solving the time-consuming problem of the ICP algorithm. Moreover, it can improve the accuracy of the 3D reconstruction method only by coordinate transformation. Hence, this method can be well used to complete the 3D reconstruction of weak texture objects.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127082415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault-Tolerant Attitude Tracking Control for Satellite Based on Event-Triggered Training 基于事件触发训练的卫星容错姿态跟踪控制

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128018

Baomin Li, Kenan Yong, Shuyi Shao

引用次数: 0