Security Assessment for Interdependent Heterogeneous Cyber Physical Systems

Peng, Hao; Kan, Zhe; Zhao, Dandan; Han, Jianmin

doi:10.1007/s11036-019-01489-z

Cited by 12 publications

(9 citation statements)

References 23 publications

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“…In addition, the classification system should be designed by appropriate technology that can accurately distinguish two sets of information about normal data and abnormal data. The system uses so-called dimensionality reduction technology, which can automatically calculate the distance between nodes by specific code in the network [ 19 ]. In fact, the technology is applied based on the following fundamental assumption: the data integrity and normality are estimated by the consistency of the specific distance between each pair of nodes [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

Zhong

Zhou

Chen

2021

Sensors

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IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.

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Section: Related Workmentioning

confidence: 99%

Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

Zhong

Zhou

Chen

2021

Sensors

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“…In short, two of the most direct and most apparent purposes are to implement security log analysis. Self-security events occur on the server to understand, followed by an emergency analysis and evidence collection [20]. e development of deep learning technology has promoted the progress of intrusion detection research.…”

Section: Security Application For the Intrusion Detection Systemmentioning

confidence: 99%

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

Zhong

Zhou

Chen

2021

Security and Communication Networks

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Due to the complexity of the social network server system, various system abnormalities may occur and in turn will lead to subsequent system failures and information losses. Thus, to monitor the system state and detect the system abnormalities are of great importance. As the system log contains valuable information and records the system operating status and users’ behaviors, log data in system abnormality detection and diagnosis can ensure system availability and reliability. This paper discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors; use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information; lastly, trace the source of the warning information to the corresponding component to determine the point of intrusion. The paper finally implements the proposed intrusion detection method in the server system, thereby improving the system’s security status.

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“…Intra-links order complex network’s degree distribution. All inter-links are randomly connecting nodes with different networks [ 14 , 20 ]. All links are non-directional to these CPS models.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Scholars discuss detailed cascading failure’s processes in [ 12 , 20 ]. In [ 12 ], they design an interdependent network by two single networks and assume random attacks starting the failure.…”

Section: Introductionmentioning

confidence: 99%

A Security Scheme Based on Intranal-Adding Links for Integrated Industrial Cyber-Physical Systems

Zhao

Liu

Peng

et al. 2021

Sensors

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With the advent of the Internet of Everything era, the Industrial Internet is increasingly showing mutual integration and development. Its core framework, the industrial CPS (Cyber-Physical Systems), has received more and more attention and in-depth research in recent years. These complex industrial CPS systems are usually composed of multiple interdependent sub-networks (such as physical networks and control networks, etc.). Minor faults or failure behaviors between sub-networks may cause serious cascading failure effects of the entire system. In this paper, we will propose a security scheme based on intranal-adding links in the face of the integrated and converged industrial CPS system environment. Firstly, by calculating the size of the largest connected component in the entire system, we can compare and analyze industrial CPS systems’ security performance under random attacks. Secondly, we compare and analyze the risk of cascading failure between integrated industrial CPS systems under different intranal-adding link strategies. Finally, the simulation results verify the system security strategy’s effectiveness under different strategies and show a relatively better exchange strategy to enhance the system’s security. In addition, this paper’s research work can help us design how to further optimize the interdependent industrial CPS system’s topology to cope with the integrated and converged industrial CPS system environment.

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Security Assessment for Interdependent Heterogeneous Cyber Physical Systems

Cited by 12 publications

References 23 publications

Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

A Security Log Analysis Scheme Using Deep Learning Algorithm for IDSs in Social Network

A Security Scheme Based on Intranal-Adding Links for Integrated Industrial Cyber-Physical Systems

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