Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic

Hamamoto, Anderson H.; Carvalho, Luiz F.; Sampaio, Lucas Dias Hiera; Abro, Taufik; Proena, Mario Lemes

doi:10.1016/j.eswa.2017.09.013

Cited by 215 publications

(98 citation statements)

References 30 publications

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“…Another approach using genetic algorithms (GA) and fuzzy logic for network intrusion detection is presented by Hamamoto et al [27]. The GA is used to create a digital signature of a network segment using glow analysis (DSNSF), a prediction of the network's traffic behavior for a given time interval.…”

Section: Intrusion Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

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Section: Intrusion Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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“…Fitness function is used to evaluate the quality if the generated candidate solutions. In this study, the proposed fitness function is given by the following formula [1]:…”

Section: Fitness Function Evaluationmentioning

confidence: 99%

“…In line with the development of communication technology, the data security in a computer system is now more vulnerable to threats that could be possibly misused for a wrong reason. In order to deal with this situation, a good and efficient mechanism must be developed and employed as a preemptive method to protect the valuable information available in a computer system [1][2][3][4]. The unauthorized access of sensitive information does not only occur externally from a network (Internet), but also internally (intranet).…”

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system using immune-genetic algorithm (IGA)

Suhaimi

Suliman

Musirin

et al. 2020

IJEECS

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Network security is an important aspect in maintaining computer network systems and personal information from being illegally accessed by third parties. The major problem that frequently occurs in computer network systems is the failure in detecting possible network-attacks. Apart from that, the process of recognizing the type of attack that occurs is very crucial as it will determine the elimination process that should take place to counter the intrusion. This paper proposes the application of standard Genetic Algorithm (GA) that combines with immune algorithm process to enhance the computer system's capability in recognizing possible intrusion occurrence in a computer system. Simulation was conducted numerous times to test the effectiveness of the proposed intrusion detection system by manipulating the parameter values for genetic operators utilized in GA. The effectiveness of the proposed method is shown in the gathered results and the analysis conducted further supports and proves that Immune Genetic Algorithm (IGA) has the capability to predict the occurrence of intrusion in computer network.

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“…In [21], the authors also used the concept of DSNSF to encapsulate six analyzed dimensions for each IP flow. The system monitors the network and extracts traffic characterization to the standard IPFIX form.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Genetic-FSM Technique for Detection of High-Volume DoS Attack

Nafie¹,

Adel²,

Abounaser³

et al. 2019

IJACSA

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Insecure networks are vulnerable to cyber-attacks, which may result in catastrophic damages on the local and global scope. Nevertheless, one of the tedious tasks in detecting any type of attack in a network, including DoS attacks, is to determine the thresholds required to discover whether an attack is occurring or not. In this paper, a hybrid system that incorporates different heuristic techniques along with a Finite State Machine is proposed to detect and classify DoS attacks. In the proposed system, a Genetic Programming technique combined with a Genetic Algorithm are designed and implemented to represent the system core that evolves an optimized tree-based detection model. A Hill-Climbing technique is also employed to enhance the system by providing a reference point value for evaluating the optimized model and gaining better performance. Several experiments with different configurations are conducted to test the system performance using a synthetic dataset that mimics real-world network traffic with different features and scenarios. The developed system is compared to many state-of-art techniques with respect to several performance metrics. Additionally, a Mann-Whitney Wilcoxon test is conducted to validate the accuracy of the proposed system. The results show that the developed system succeeds in achieving higher overall performance and prove to be statistically significant.

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Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic

Cited by 215 publications

References 30 publications

Artificial Intelligence in the Cyber Domain: Offense and Defense

Artificial Intelligence in the Cyber Domain: Offense and Defense

Network intrusion detection system using immune-genetic algorithm (IGA)

Hybrid Genetic-FSM Technique for Detection of High-Volume DoS Attack

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