Time Series Association State Analysis Method for Attacks on the Smart Internet of Electric Vehicle Charging Network

et al. 2020

J Wireless Com Network

Self Cite

The safety of the Industrial Internet Control Systems has been a hotspot in the information security. To meet the needs of communication, a large variety of proprietary protocols have emerged in the field of industrial control. The protocol field is often trusted in the implementation of industrial control terminal code. If attackers modify the data of these fields using the protocol defect, the operation of the program can be controlled and the entire system will be affected. To cope with such security threats, academia and industry generally adopt fuzz test methods. However, the current industrial control protocol fuzz test methods generally have low code coverage, where unified description models are missing and test cases are not targeted. A method of fuzzification processing combined with dynamic multimodal sensor communication data is proposed. To track the program execution, the dynamic pollution analysis is used to search for the input fields that affect the execution of the conditional branch and capture the dependencies between the conditional branches to guide the grammar generation of test cases, which can increase the chances of executing deep code. The experimental results show that the proposed method improves the validity and code coverage of test cases to a certain extent and greatly increases the probability of anomaly detection in the protocol implementation.

Section: Methods and Principlementioning

confidence: 99%

A dynamic taint tracking optimized fuzz testing method based on multi-modal sensor data fusion

Liu

et al. 2020

J Wireless Com Network

Self Cite

“…(1) Use different training data sets to train the same type of model [12,13]. Bagging and random forest methods both generate different base models in this way.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Industrial Control Equipment Safety Fault Diagnosis Method in Industrial Internet of Things

Zhang

Security and Communication Networks

et al. 2021

Self Cite

With the rapid development of intelligent manufacturing and Industrial Internet of Things, many industrial control systems have high requirements for the security of the system itself. Failures of industrial control equipment will cause abnormal operation of industrial control equipment and waste of resources. It is very meaningful to detect and identify potential equipment abnormalities and failures in time and implement effective fault tolerance strategies. In the Industrial Internet of Things environment, the instructions and parameters of industrial control equipment often change due to changes in actual requirements. However, it is impractical to customize the learning method for each parameter value. Aiming at the problem, this paper proposes a fault diagnosis model based on ensemble learning and proposes a method of updating voting weights based on dynamic programming to assist decision-making. This method is based on Bagging strategy and combined with dynamic programming voting weight adjustment method to complete fault type prediction. Finally, this paper uses different loads as dynamic conditions; the diagnostic capability of the Bagging-based fault diagnosis integrated model in a dynamically changing industrial control system environment is verified by experiments. The fault diagnosis model of industrial control equipment based on ensemble learning effectively improves the adaptive ability of the model and makes the fault diagnosis framework truly intelligent. The voting weight adjustment method based on dynamic programming further improves the reliability of voting.

“…From the perspective of saving defence cost, it gives the optimal strategy for intrusion detection service deployment in a wireless sensor network environment. To address the massive linkage control problem of attack detection, Li et al [28] used game theory to analyse the security combination model of firewall, intrusion detection system (IDS), and vulnerability scanning technology and gave the optimal intrusion detection calculation method. At the same 4 Complexity time, in the particular network environment with obvious resource constraints such as WSN network and mobile Adhoc network, the game theory is also applied to the solution of the optimal intrusion detection strategy [29][30][31][32][33][34].…”

Section: Related Researchmentioning

confidence: 99%

“…Many researches have been carried out in the academic world to address the network security risks introduced in the development of edge computing networks [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. However, as the edge computing network is a hybrid network architecture that involves multiple links and multiple technologies, a unified international standard has not yet been formed.…”

Section: Introductionmentioning

confidence: 99%

GLIDE: A Game Theory and Data-Driven Mimicking Linkage Intrusion Detection for Edge Computing Networks

Hou

et al. 2020

Complexity

Self Cite

The real-time and high-continuity requirements of the edge computing network gain more and more attention because of its active defence problem, that is, a data-driven complex problem. Due to the dual constraints of the hybrid feature of edge computing networks and the uncertainty of new attack features, implementing active defence measures such as detection, evasion, trap, and control is essential for the security protection of edge computing networks with high real-time and continuity requirements. The basic idea of safe active defence is to make the defence gain more significant than the attack loss. To encounter the new attacks with uncertain features introduced by the ubiquitous transmission network in the edge computing network, this paper investigates the attack behaviour and presents an attack-defence mechanism based on game theory. Based on the idea of dynamic intrusion detection, we utilize the game theory in the field of edge computing network and suggest a data-driven mimicry intrusion detection game model-based technique called GLIDE. The game income of participants and utility computing methods under different deployment strategies are analysed in detail. According to the proof analysis of the Nash equilibrium condition in the model, the contradictory dynamic game relationship is described. Therefore, the optimal deployment strategy of the multiredundancy edge computing terminal intrusion detection service in the edge computing network is obtained by solving the game balance point. The detection probability of the edge computing network for network attacks is improved, and the cost of intrusion detection of the edge computing network is reduced.