Review on Intelligent Algorithms for Cyber Security

Subashini, P.; Krishnaveni, M.; Shanmugavalli, R.

doi:10.4018/978-1-5225-9611-0.ch001

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…al. [45] extend this work by reviewing ML and DL algorithms. They mostly focus on the infrastructure area for intrusion and anomaly detection, and they marginally outline the contributions in the software area for botnet and malware detection.…”

Section: Related Workmentioning

confidence: 85%

“…They mostly focus on the infrastructure area for intrusion and anomaly detection, and they marginally outline the contributions in the software area for botnet and malware detection. Unlike other surveys, [45] reviews Nature Inspired computing (NIC) paradigms which are applied for fine-tuning the parameters in the security learning model to categorize attacks. This improves efficiency and performance.…”

Section: Related Workmentioning

confidence: 99%

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A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Rodríguez

Otero

Gutiérrez

et al. 2021

IEEE Commun. Surv. Tutorials

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The widespread use of mobile devices, as well as the increasing popularity of mobile services has raised serious cybersecurity challenges. In the last years, the number of cyberattacks has grown dramatically, as well as their complexity. Traditional cybersecurity systems have failed to detect complex attacks, unknown malware, and they do not guarantee the preservation of user privacy. Consequently, cybersecurity systems have embraced Deep Learning (DL) models as they provide efficient detection of novel attacks and better accuracy. This paper presents a comprehensive survey of recent cybersecurity works that use DL in mobile and wireless networks. It covers all cybersecurity aspects: infrastructure threads and attacks, software attacks and privacy preservation. First, we provide a detailed overview of DL techniques applied, or with potential applications, to cybersecurity. Then, we review cybersecurity works based on DL. For each cybersecurity threat or attack, we discuss the challenges for using DL methods. For each contribution, we review the implementation details and the performance of the solution. In a nutshell, this paper constitutes the first survey that provides a complete review of the DL methods for cybersecurity. Given the analysis performed, we identify the most effective DL methods for the different threats and attacks.

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Section: Related Workmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Rodríguez

Otero

Gutiérrez

et al. 2021

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…Subsequently, studies have been carried out to explore new techniques and tools to counter the issue of application-level security. For instance, the authors in [43], [44], [45], [46], [47], [48], [49] and [50] point out that it is pertinent for security to be the initial step prior to system development. The process should not only be flexible but also independent, containing security aspects customized to the organization.…”

Section: Introductionmentioning

confidence: 99%

Review of the security challenges in web-based systems

Awuor¹

2023

World J. Adv. Eng. Technol. Sci.

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Web-based systems are vulnerable to security issues similar to any other applications. Due to the characteristics of web-based systems such as their distributable nature and cross platform accessibility, security challenges are predominant. Recently, more focus has been placed on how to handle security concerns in web systems. Current solutions to counteract the web-based system security challenges include web system languages, firewalls, Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), cryptographic techniques, digital certificates and signatures among others. However, attacks and threats such as cross site scripting (XSS), Distributed Denial of Service (DDoS), cross-site request forgery (CSRF) and structured query language (SQL) injection attacks are still common. This gives the impression that there are still security challenges in this regard, despite the efforts for detection and prevention of attacks. Consequently, due to their dynamism, secure architectures are pivotal for the security of web-based systems. The focus of this paper therefore, is to review the existing security challenges of web-based systems. It is evident from this literature study that most security challenges in web-based systems stem from the threat of unauthorized access and risks from implementing technologies and standards that are under developed as regards security.

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“…The use of intelligent algorithms in the development of cyber security models to detect various sorts of assaults is researched to improve performance. As a result, it underlines the effectiveness of intelligent algorithms in developing sophisticated cyber security models that can detect threats or vulnerabilities [17,18].…”

mentioning

confidence: 99%

An enhanced mechanism for detection of Domain Name System‐based distributed reflection denial of service attacks depending on modified metaheuristic algorithms and adaptive thresholding techniques

et al. 2022

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The rapid growth of the number of devices connected to the Internet and the increasing demand for electronic services have led to a huge growth in the number of cyberattacks targeting cyberspace and the development of their methodology. Therefore, there must be mechanisms, laws, and rules regulating the work of these applications and protecting them from electronic attacks. The Domain Name System (DNS) has several vulnerabilities that can be exploited by cyber attackers to launch their attacks, and the most important one of these vulnerabilities is that the response size is always greater than the size of the request. According to reports published by numerous security companies, distributed reflection denial of service (DRDoS) attacks against DNS are regarded as one of the most hazardous and rapidly spreading threats in recent years. An enhanced mechanism that is able to detect DNS-based DRDoS attacks that exploit the DNS responses to launch their attacks is proposed. The proposed mechanism was designed based on modified metaheuristic optimization algorithms and an adaptive threshold. This mechanism consists of two models and four stages. The first model is called 'Proactive Feature Selection,' and the second model is called 'Evolving Dynamic Fuzzy Clustering.' The four stages of the proposed mechanism are: the preprocessing stage, feature selection stage, detection stage, and enhancement stage. The new mechanism has been implemented on the CICDDoS2019 standard dataset and achieves a detection accuracy of 95.44% with a false-positive rate of 0.22%. The results show that the new mechanism is better than others depending on the detection accuracy and false positives. | INTRODUCTIONWith the increase in the number of Internet-connected assets, the effect of distributed denial of service (DDoS) attacks increases proportionately. Due to the nature of this extremely disruptive attack, the level to which its service quality is disrupted and degraded is uncertain [1]. As a result of the widespread use of these services, cyberattacks on the infrastructure of the systems that utilise them become an attractive target [2,3].DDoS Attack is the most frequently used type of cyberattack to disrupt these services [4]. Recent years have seen an explosion in the growth of information security infrastructure and services, such as payment gateways, search engines, banks, and social media websites. This has resulted in an exponential increase in network traffic, increasing Internet misuse. In addition to, security and privacy concerns instil fear in end users, preventing them from using online services [5]. Although proposals and implementations for information security management haveThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Review on Intelligent Algorithms for Cyber Security

Cited by 6 publications

References 36 publications

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Review of the security challenges in web-based systems

An enhanced mechanism for detection of Domain Name System‐based distributed reflection denial of service attacks depending on modified metaheuristic algorithms and adaptive thresholding techniques

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