Detection of Denial-of-Service Attack in Wireless Sensor Networks: A Lightweight Machine Learning Approach

Elsadig, Muawia A.

doi:10.1109/access.2023.3303113

Cited by 18 publications

(1 citation statement)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine-learning models are subject to a number of attacks (Elsadig, 2023;Elsadig & Gafar, 2022), including membership inference, model stealing, model poisoning, and input manipulation. Therefore, more robust adversarial machine-learning systems are required.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2024

JISIS

View full text Add to dashboard Cite

Numerous advancements in natural language processing (NLP) have been made possible by the development of artificial intelligence (AI). In particular, the recent large growth in this field has been attributed to the development of sophisticated conversational AI systems such as ChatGPT. Since ChatGPT was released in November 2022, its outstanding features and capabilities have amazed millions of users. However, there is an increasing concern about its capacity to further the objectives of bad actors. In particular, ChatGPT provides new opportunities for hackers to compromise cybersecurity. This article provides a thorough investigation and discussion of how ChatGPT can significantly support hackers in committing various attacks. This investigation covers the recent state-of-the-art in research work that conducted on this context. In addition, some responses of the ChatGPT regarding its positive and negative impacts on cybersecurity were evaluated and discussed. The article concluded that ChatGPT has significantly supported hacking behaviors and can be exploited to spread malicious activities. Therefore, continuous development and enforcement of appropriate standards to prevent negative effects are required. To achieve this, policymakers and developers should work together, considering public user concerns and the misuse of this promising tool. Finally, this article provides thoughtful discussions and recommendations that can contribute to improving IA-based systems.

show abstract

Section: Related Workmentioning

confidence: 99%