A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response

Dunsin, Dipo; Ghanem, Mohamed C.; Ouazzane, Karim; Vassilev, Vassil

doi:10.1016/j.fsidi.2023.301675

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Cited by 6 publications

(1 citation statement)

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“…Alongside the potential of AI-assisted communications to offer efficient connectivity, the incorporation of IRSs into wireless communication systems has garnered considerable attention. The utilization of IRSs can extend to various applications including road signs, architectural exteriors, ceilings, indoor walls, and even portable devices worn by pedestrians [ 15 ]. It is composed of a variety of low-cost and energy-efficient reflecting pieces.…”

Section: Literature Reviewmentioning

confidence: 99%

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques

Li,

Qiang

2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques

Li,

Qiang

2024

Heliyon

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Multiple Inputs and Hybrid Deep Learning Model to Identify Suspicious Posts on Social Media

Alotaibi

2024

2024 IEEE 12th International Conference on Intelligent Systems (IS)

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Generative AI for Threat Intelligence and Information Sharing

Sindiramutty,

V. Prabagaran,

Jhanjhi

et al. 2024

Advances in Digital Crime, Forensics, and Cyber Terrorism

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Collaboration in providing threat intelligence and disseminating information enables cyber security professionals to embrace digital security most successfully, whose risks are ever-changing. This article dwells on the capacity of machine intelligence to change information security by categorising indicators of compromise (IOC) and threat actors, then highlights the limits of traditional methods. Among Artificial intelligence tools such as generative adversarial networks (GANs) and Variational autoencoders (VAEs), which are the key innovators, one can create synthetic or fake threat data that emulates real attack scenarios in the past. This allows cyber-related risks to be analysed differently from before. In addition, this feature enables secure stakeholder collaborations. It is also meant mainly for factual data that protects private information but allows the exchange of helpful information. It is clear from the fact that showcasing real-world examples demonstrates Al's automation through cybersecurity detection.

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A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response

Cited by 6 publications

References 35 publications

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques

Multiple Inputs and Hybrid Deep Learning Model to Identify Suspicious Posts on Social Media

Generative AI for Threat Intelligence and Information Sharing

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