Artificial intelligence analysis in cyber domain: A review

Abstract

The application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. In this study, we develop a repeatable procedure for detecting cyber-attacks in an accurate, scalable, and timely manner. An in-depth learning algorithm is utilized for training a neural network for detecting suspicious user activities. The proposed system architecture was implemented with the help of Splunk Enterprise Edition 6.42. A data set of average feature counts has been executed through a Splunk search command in 1-min intervals. All the data sets consisted of a minute trait total derived from a sparkling file. The attack patterns that were not anonymized or were indicative of the vulnerability of cyber-attack were denoted with yellow. The rule-based method dispensed a low quantity of irregular illustrations in contrast with the Partitioning Around Medoids method. The results in this study demonstrated that using a proportional collection of instances trained with the deep learning algorithm, a classified data set can accurately detect suspicious behavior. This method permits for the allocation of multiple log source types through a sliding time window and provides a scalable solution, which is a much-needed function.

Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis

“…• Human-AI collaboration: The research may overlook the human factors involved in the interaction between humans and AI systems in CS operations [9,10]. Further exploration is needed to understand the dynamics of human-AI collaboration, including human-machine interaction, decision-making processes, and trust dynamics, to optimize the effectiveness and usability of AI-driven CS solutions.…”

“…• Phishing attacks: Phishing remains one of the most prevalent and widely-used tactics employed by cybercriminals to deceive users and gain unauthorized access to sensitive information [10,11]. These attacks often involve the use of fraudulent emails, websites, or messages designed to trick recipients into disclosing personal data, login credentials, or financial information.…”

A Comprehensive Study on Cyber Attacks in Communication Networks in Water Purification and Distribution Plants: Challenges, Vulnerabilities, and Future Prospects

“…It is important to design and develop safe and secure methods. Several studied techniques use deep learning-, machine learning-, or artificial intelligence-based algorithms [ 93 ]. However, there are some disadvantages to such approaches, such as ensemble datasets and high dimensional data.…”

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

“…This is one of the studies or learning techniques that were inverted in the early 1960s that can be deliberated to be LA where it selects and inferiors their existing action per the past familiarities from the atmosphere. This makes it more suitable to tumble interested in the variety of RL in case the atmosphere is stochastically utilized; MDP is used [38] and [39].…”