Distributed Blockchain-SDN Secure IoT System Based on ANN to Mitigate DDoS Attacks

Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing potential security threats to blockchain systems. A distributed denial of service (DDoS) attack is a major threat to the PoW algorithm. It utilizes multiple nodes in the blockchain network to attack honest miners to obtain illegal rewards. To solve this problem, academia has proposed a DDoS attack detection mechanism based on reinforcement learning methods and static game modeling methods based on mining pools. However, these methods cannot effectively make miners choose the strategy with the best profit over time when facing DDoS attacks. Therefore, this paper proposes a dynamic evolutionary game model for miners facing DDoS attacks under blockchain networks to solve the above problems for the first time. We address the model by replicating the dynamic equation to obtain a stable solution. According to the theorem of the Lyapunov method, we also obtain the only stable strategy for miners facing DDoS attacks. The experimental results show that compared with the static method, the dynamic method can affect game playing and game evolution over time. Moreover, miners’ strategy to face DDoS attacks gradually shifts from honest mining to launching DDoS attacks against each other as the blockchain network improves.

Section: Defense and Mitigation Methods For Ddos Attacksmentioning

confidence: 99%

Analyzing Miners’ Dynamic Equilibrium in Blockchain Networks under DDoS Attacks

Liu,

Huang,

Wang

et al. 2023

“…Traditional machine learning SVM [31][32][33][34][35] Decision Tree [36][37][38] KNN [38][39][40][41] Naive Bayes [38,[42][43][44] Random Forest [36][37][38] Deep learning SOM [41,45,46] ANN [47][48][49] LSTM [48][49][50] DNN [51][52][53] RNN [50,53] The SVM algorithm is a binary classification model utilized for distinguishing between normal and abnormal data in the context of DDoS attack detection based on traffic characteristics. Based on the traffic characteristics observed in the SDN network environment, the SVM detection algorithm is employed to gather input feature vectors in order to develop an algorithm for detecting malicious behavior within the network.…”

Section: Algorithm Classification Algorithm Referencesmentioning

confidence: 99%

“…Compared to Mininet, OMNeT++ has the ability to redefine network layers and protocols, making it suitable for complex network models and capable of handling large-scale and detailed network simulations. Meanwhile, some works in the literature [42,47] integrate SDN with IoT to assess the effectiveness of its detection and mitigation methods in the IoT environment.…”

Section: Sdn Simulator and Controllermentioning

confidence: 99%

A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network

Su,

Xiong,

Qian

et al. 2024

The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions.

“…However, PoW's reliance on computational power exposes blockchain to security risks, including selfish mining (SM) attacks [4], block withholding (BWH) attacks [5], and Distributed Denial of Service (DDoS) attacks [6]. SM attacks gain additional revenue by creating redundant private chains in addition to the public chain [7]. BWH attacks generate illegal revenue by sending spy miner attacks to honest mining pools [8].…”

Section: Introductionmentioning

confidence: 99%

A Repeated Game-Based Distributed Denial of Service Attacks Mitigation Method for Mining Pools

Liu,

Huang,

Wang

et al. 2024

A Distributed Denial of Service (DDoS) attack is a prevalent issue in the blockchain network layer, causing significant revenue loss for honest mining pools. This paper introduces a novel method, the Repeated Game-based DDoS attack mitigation (RGD), to address this problem. Unlike traditional methods such as game theory and machine learning-based detection, the RGD method can effectively reflect the changes in mining revenue and strategies under different network-strength environments. In particular, we abstract the problem of DDoS mining pool revenue loss into a game revenue model and propose the subgame perfect equilibrium (SPE) approach to solve the optimal payoffs and pool strategies in various network environments. Furthermore, we address the returns of mining pools in an infinitely repeated game environment using the Two-Stage Repeated Game (TSRG) method, where the strategy varies with different network environments. The Matlab experimental simulation results indicate that as the network environment improves, the optimal mining strategies of mining pools are gradually shifting from honest strategies to launching DDoS attacks against each other. The RGD method can effectively represent the impact of changes in the network environment on the mining pool’s strategy selection and optimal revenue. Consequently, with the changing network environment, the optimal revenue of the mining pool only increases by 10% of the revenue loss during a DDoS attack.