Review on Machine and Deep Learning Applications for Cyber Security

Thangavel, M.; Shree, T G R Abiramie; Priyadharshini, P. Sugantha; Saranya, T.

doi:10.4018/978-1-5225-9611-0.ch003

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…There are other types of attacks and threats such as SQL injection attack, drive-by attack, password attack, a man in the middle, authentication attacks, wrapping attacks, watering hole, and webshell [65,109]. However, we have just considered intrusion detection (ID), malware detection, and spam detection in this review article.…”

Section: A Basics Of Attacks and Threatsmentioning

confidence: 99%

“…On the defence side, ML models are playing a vital role to provide robust and smarter techniques to improve the performance and early detection of attacks to decrease the impact and damage that occurred [63,64]. Machine learning techniques are combined to enhance the accuracy of correct and early classification of cyberattacks [65]. However, most of the studies are performed with an inadequate dataset.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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Section: A Basics Of Attacks and Threatsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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“…They exploit the vulnerabilities existing in the victim's devices and silently enter their network, making them more dangerous. In the recent past, progressively more file-less attacks have been reported [10].…”

Section: Introductionmentioning

confidence: 99%

HoneyNetCloud Investigation Model, A Preventive Process Model for IoT Forensics

Raman¹,

Varadharajan²

2021

ISI

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With the pervasive usage of sensing systems and IoT things, the importance of security has increased. Attempts towards breaching IoT security systems by attackers are on upsurge. Many intrusions in embedded systems, sensing equipment and IoT things have occurred in the past. Though there are cyber security tools like Antivirus, Intrusion detection and prevention systems available for securing the digital devices and its networks. However, a forensic methodology to be followed for the analysis and investigation to detect origin cause of network incidents is lacking. This paper derives a comprehensive preventive cyber forensic process model with honeypots for the digital IoT investigation process which is formal, that can assist in the court of law in defining the reliability of the investigative process. One year data of various attacks to the IoT network has been recorded by the honeypots for this study. The newly derived model HIM has been validated using various methods and instead of converging on a particular aspect of investigation, it details the entire lifecycle of IoT forensic investigation. The model is targeted to address the forensic analysts’ requirements and the need of legal fraternity for a forensic model. The process model follows a preventive method which reduce further attacks on network.

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A Review on Machine Learning Strategies for Real-World Engineering Applications

Jhaveri

Revathi

Ramana³

et al. 2022

Mobile Information Systems

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Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing automated and intelligent applications that can handle data in fields such as health, cyber-security, and intelligent transportation systems. There are a range of strategies in the field of machine learning, including reinforcement learning, semi-supervised, unsupervised, and supervised algorithms. This study provides a complete study of managing real-time engineering applications using machine learning, which will improve an application's capabilities and intelligence. This work adds to the understanding of the applicability of various machine learning approaches in real-world applications such as cyber security, healthcare, and intelligent transportation systems. This study highlights the research objectives and obstacles that Machine Learning approaches encounter while managing real-world applications. This study will act as a reference point for both industry professionals and academics, and from a technical standpoint, it will serve as a benchmark for decision-makers on a range of application domains and real-world scenarios.

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Review on Machine and Deep Learning Applications for Cyber Security

Cited by 3 publications

References 4 publications

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

HoneyNetCloud Investigation Model, A Preventive Process Model for IoT Forensics

A Review on Machine Learning Strategies for Real-World Engineering Applications

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