DPRF: A Differential Privacy Protection Random Forest

Hou, Jun; Li, Qianmu; Meng, Shunmei; Zhang, Ni; Chen, Yini; Liu, Yaozong

doi:10.1109/access.2019.2939891

Cited by 34 publications

(17 citation statements)

References 32 publications

(31 reference statements)

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“…Besides, we can see the number of test cases generated by AFL-fuzzer grows steadily as the number of samples increases, because Afl-fuzzer prunes input samples in consideration of that users may offer low-quality initial samples and thus cause the possible data redundancy in some types of variations. Although depending more on the number of samples, the method proposed adopts a more pertinent generation strategy [35][36][37][38][39][40][41][42][43][44][45][46][47].…”

Section: Methods and Experimentalmentioning

confidence: 99%

A Dynamic Taint Tracking Optimized Fuzz Testing Method based on Multi-modal Sensor Data Fusion

Meng

Zhang

et al. 2020

Preprint

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The safety of Industrial Internet Control Systems has been a hotspot in the information security. To meet 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 fuzzy test methods. However, the current industrial control protocol fuzzy 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 multi-modal 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

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Section: Methods and Experimentalmentioning

confidence: 99%

A Dynamic Taint Tracking Optimized Fuzz Testing Method based on Multi-modal Sensor Data Fusion

Meng

Zhang

et al. 2020

Preprint

Self Cite

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“…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]. Research on mimetic honeypot intrusion detection technology based on game theory is also often involved [35,36]. In general, game theory has been widely used in the field of network attack and defence and can be used to solve the optimal strategy of network attack and defence.…”

Section: Related Researchmentioning

confidence: 99%

“…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. e security protection technologies for edge computing networks have also experienced password protection, security models, access control policies, host hardening to anomaly detection, and association analysis [30][31][32][33][34][35][36][37][38]. However, the above technologies are mainly based on passive defence.…”

Section: Introductionmentioning

confidence: 99%

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

Hou

Meng

et al. 2020

Complexity

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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.

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“…If rand < p (20) Foraging behavior: update the position X i of birds using equation 8; 21Else (22) Vigilance behavior: update the position X i of birds using equation 2; 23End If (24) End For (25) Else (26) Flight behavior is divided into producers and scroungers; (27) For…”

Section: N);mentioning

confidence: 99%

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Zhang

Xia

et al. 2020

Computational Intelligence and Neuroscience

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Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

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DPRF: A Differential Privacy Protection Random Forest

Cited by 34 publications

References 32 publications

A Dynamic Taint Tracking Optimized Fuzz Testing Method based on Multi-modal Sensor Data Fusion

A Dynamic Taint Tracking Optimized Fuzz Testing Method based on Multi-modal Sensor Data Fusion

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

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

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