Attack Detection in IoT Using Machine Learning—A Survey

Ali, Saeed Ali Haifa; Rani, J. Vakula

doi:10.1007/978-3-031-18497-0_16

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Attackers using this method aim to access private and sensitive information such as credit card numbers and passwords passed across networks. Sniffers could be introduced to carry out this attack and seize transmitted data [70], [71]. Eavesdropping is broadly categorized into two; active and passive eavesdropping.…”

Section: Eavesdroppingmentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

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Section: Eavesdroppingmentioning

confidence: 99%