Integrating Artificial Intelligence, machine learning, and data analytics in cybersecurity: A holistic approach to advanced threat detection and response
1 Business Administration, Pompea College of Business, University of New Haven, West Haven, Connecticut, USA.
2 Management Information Systems, College of Business and Management, University of Illinois, Springfield, Illinois, USA
3 Applied Statistics and Decision Analytics, Western Illinois University, Macomb, Illinois, USA.
Research Article
World Journal of Advanced Research and Reviews, 2023, 20(03), 2005–2024
Publication history:
Received on 10 September 2023; revised on 9 December 2023; accepted on 21 December 2023
Abstract:
Introduction: The integration of artificial intelligence (AI), machine learning (ML), and data analytics is revolutionizing cybersecurity practices. With the advancement in technology and new threats emerging in the cyberspace, conventional approaches to security are not effectively sufficient. This paper aims at identifying how these sophisticated technologies improve the methods of threat identification, response, and the overall analytical capability to strengthen the computerized structures against modern SNEs. The threat is changing at incredible speeds, making it impossible to just wait for new threats to unfold and take a response. AI&ML are capable to analyses enormous quantity of data in extremely short time, as well as find patterns and changing previous unnoticed by analysts, automatically respond to threats in real time. Data analytics forms the bedrock on which the advanced systems are built and serve to process and analyze a large chunk of the security related information. The combination of these technologies provides a strong foundation for the cybersecurity environment that can be responsive to emerging threats, utilize prior attacks for training purposes, and self-develop the methodology for better protection.
Methodology: The study employed a comprehensive search strategy across multiple electronic databases, including IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, and Google Scholar. Keywords related to AI, ML, data analytics, and cybersecurity were used in combination with Boolean operators. To make the outcome more meaningful and relevant, the general criteria for the eligibility of the papers were as follows. The selection process involved two phases: Title and abstract evaluation for the inclusion in the initial set of studies and subsequent full-text review of these studies. Some of our extraction process involved the use of a data extraction form to gather specific details from each of the study included in the analysis. To evaluate the quality of the studies included, the CASP tools were used with slight modifications. In this study, two independent reviewers participated in the decision on the study inclusion, data extraction, and quality assessment to reduce bias. This approach of writing helped in providing a comprehensive and methodical analysis of the contemporary state and potential developments in the context of AI and ML in the realm of cybersecurity.
Results and Discussion: The review highlights that AI and ML greatly boost the threat detection by detecting patterns and anomalies within large volumes of security data. These technologies can be used to descend new and previously unknown type of attack known as zero-day attack & APTs (advanced persistent threats). Using AI and ML for predictive analytics enables the organization to leverage previous attacks and contexts to predict future attacks, and prepare for their defense. The use of AI in response to security threats also minimizes response time in times of security threats and optimizes processes. These technologies integrate to help quickly and more with minimal human intervention respond to threats thereby also reducing the time it takes to respond to threats. However, issues like quality of the data used in the model, reliability of the algorithm besides, question marks like who will tamper with the AI systems. The review also discusses new trends in cyber defense and remediation that may be of interest in the future, namely continuous authentication and advanced threat hunting. Potential issues associated with data privacy and algorithmic bigotry are pointed out as promising directions for future studies in this domain.
Conclusion: The integration of AI, ML, and data analytics in cybersecurity represents a paradigm shift in how organizations approach digital defense. These technologies provide relevant functions for increasing threat diagnostics and response capabilities, as well as improving the predictive feature offered by this automation. The integration of AI, ML along with data analytics results into an architecture that is strong, flexible, intelligent and adaptive enough to cope up with growing security threats. Despite all these issues, including the problems with data quality and reliability of algorithms, as well as the numerous ethical questions, employing these technologies in cybersecurity seems promising. New types of cyber threats constantly emerge and therefore ongoing enhancement of AI and ML security tools will be imperative. The long-term research should endeavor to address the challenges mentioned above as well as elaborate on additional possible uses of these technologies in strengthening cybersecurity
Keywords:
Artificial Intelligence; Machine Learning; Data Analytics; Cybersecurity; Threat Detection; Predictive Analytics; Automated Response; Zero-Day Attacks; Advanced Persistent Threats; Big Data; Anomaly Detection; Continuous Authentication; Algorithm Reliability; Data Privacy
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