Defense against distributed DoS attack detection by using intelligent evolutionary algorithm

Dwivedi, Shubhra; Vardhan, Manu; Tripathi, Sarsij

doi:10.1080/1206212x.2020.1720951

Cited by 37 publications

(30 citation statements)

References 29 publications

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“…Authors have proposed grasshopper optimization algorithm (GOA) [86] based intrusion detection system named GOIDS (Grasshopper Optimization Intrusion Detection System) to alleviate and diminish DoS (Denial of Service) attack. Machine learning based GOA has been implemented along with support decision tree (C4.5), vector machine (SVM), naïve Bayes (NB) and multilayer perceptron (MLP) to optimize performance of technique.…”

Section: ) Grasshopper Optimization Intrusion Detection Systemmentioning

confidence: 99%

A Survey on Holes Problem in Wireless Underground Sensor Networks

Sharma¹,

Singh

Mohammed

et al. 2022

IEEE Access

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The paper aims to put in the forefront a survey on the existing holes problem in the Wireless Underground Sensor Networks (WUSNs). Anomalies in wireless sensor networks can weaken the network by impeding their functionalities. These existing anomalies commonly known as holes may cause irreversible devastating problems and may prove fatal to humans. There can be a different kind of holes in the network such as coverage hole, routing hole, jamming hole, and sink/ worm hole, etc. This paper intends to discuss in entirely various holes problems in WUSNs, reasons behind their occurrence and different techniques to mitigate the issues. An efficient hole detection technique shall increase network lifetime, use lesser resources and give accurate results with zero false positive. To put a light on the present scenario, methods/ techniques used to cope with different types of holes have been discussed in detail, presenting their analysis, constraints, and advantages. Besides, it would also touch upon the comparative analysis in terms of soundness and limitations of these techniques. The study is concluded with future research directions.

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Section: ) Grasshopper Optimization Intrusion Detection Systemmentioning

confidence: 99%

A Survey on Holes Problem in Wireless Underground Sensor Networks

Sharma¹,

Singh

Mohammed

et al. 2022

IEEE Access

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“…Tripathi et al [46] utilized grasshopper optimization algorithm (GOA). Their suggested method was IDS-based to discriminate between malicious and regular traffic.…”

Section: Related Workmentioning

confidence: 99%

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

et al. 2021

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The need for timely identification of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) has become critical in minimizing security risks as the number of IoT devices deployed rapidly grows globally and the volume of such attacks rises to unprecedented levels. Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional attacks. Several methods have been developed for detecting botnet attacks, such as Swarm Intelligence (SI) and Evolutionary Computing (EC)-based algorithms. In this study, we propose a Local-Global best Bat Algorithm for Neural Networks (LGBA-NN) to select both feature subsets and hyperparameters for efficient detection of botnet attacks, inferred from 9 commercial IoT devices infected by two botnets: Gafgyt and Mirai. The proposed Bat Algorithm (BA) adopted the local-global best-based inertia weight to update the bat’s velocity in the swarm. To tackle with swarm diversity of BA, we proposed Gaussian distribution used in the population initialization. Furthermore, the local search mechanism was followed by the Gaussian density function and local-global best function to achieve better exploration during each generation. Enhanced BA was further employed for neural network hyperparameter tuning and weight optimization to classify ten different botnet attacks with an additional one benign target class. The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes. The performance of LGBA-NN was compared with several recent advanced approaches such as weight optimization using Particle Swarm Optimization (PSO-NN) and BA-NN. The experimental results revealed the superiority of LGBA-NN with 90% accuracy over other variants, i.e., BA-NN (85.5% accuracy) and PSO-NN (85.2% accuracy) in multi-class botnet attack detection.

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“…The wrapper strategies use a predetermined algorithm and its performance to assess the optimal DOS-DDOS subset features [31]. It executed in an iterative process, and at each iteration a new subset of DOS-DDOS features is generated to be evaluated by the classification algorithm [32]. The criterion of selection is principally based on the cross-validation accuracy during the DOS-DDOS training data [33].…”

Section: Impact Of Feature Selection Process Dos-ddos Machine Learning Projectsmentioning

confidence: 99%

Predicting DOS-DDOS Attacks: Review and Evaluation Study of Feature Selection Methods based on Wrapper Process

Bouzoubaa¹,

Taher²,

Nsiri³

2021

IJACSA

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Now-a-days, Cybersecurity attacks are becoming increasingly sophisticated and presenting a growing threat to individuals, private and public sectors, especially the Denial Of Service attack (DOS) and its variant Distributed Denial Of Service (DDOS). Dealing with these dangerous threats by using traditional mitigation solutions suffers from several limits and performance issues. To overcome these limitations, Machine Learning (ML) has become one of the key techniques to enrich, complement and enhance the traditional security experiences. In this context, we focus on one of the key processes that improve and optimize Machine Learning DOS-DDOS predicting models: DOS-DDOS feature selection process, particularly the wrapper process. By studying different DOS-DDOS datasets, algorithms and results of several research projects, we have reviewed and evaluated the impact on used wrapper strategies, number of DOS-DDOS features, and many commonly used metrics to evaluate DOS-DDOS prediction models based on the optimized DOS-DDOS features. In this paper, we present three important dashboards that are essential to understand the performance of three wrapper strategies commonly used in DOS-DDOS ML systems: heuristic search algorithms, meta-heuristic search and random search methods. Based on this review and evaluation study, we can observe some of wrapper strategies, algorithms, DOS-DDOS features with a relevant impact can be selected to improve the DOS-DDOS ML existing solutions.

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Defense against distributed DoS attack detection by using intelligent evolutionary algorithm

Cited by 37 publications

References 29 publications

A Survey on Holes Problem in Wireless Underground Sensor Networks

A Survey on Holes Problem in Wireless Underground Sensor Networks

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

Predicting DOS-DDOS Attacks: Review and Evaluation Study of Feature Selection Methods based on Wrapper Process

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