2024· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
An AI-based threat detection framework that integrates network traffic analysis, preprocessing, feature engineering, threat classification, and automated response is proposed that outperforms conventional methods by providing higher detection accuracy, lower false alarms, and faster response.
Abstract
Modern networks face increasing cyber threats such as malware, ransomware, phishing, DDoS, insider attacks, and advanced persistent threats, making traditional signature-based security systems less effective. Artificial Intelligence (AI), through machine learning and deep learning, enables intelligent threat detection by identifying known and unknown attacks in real time. This study proposes an AI-based threat detection framework that integrates network traffic analysis, preprocessing, feature engineering, threat classification, and automated response. Experimental evaluation using metrics such as accuracy, precision, recall, F1-score, false positive rate, and detection latency demonstrates that the proposed framework outperforms conventional methods by providing higher detection accuracy, lower false alarms, and faster response. Despite challenges related to data quality, model interpretability, and computational cost, AI-driven cybersecurity offers a scalable and effective solution for modern network security.
They originate from the rapid rise of cyber threats such as malware, phishing, ransomware,
denial of service, and unauthorised network intrusion, which have proven to be so difficult to
tackle that traditional security measures can hardly deal with the issue. Signature-based
intrusion detection system techniques in particular, which are commonly adopted by traditional
methods, usually lack the ability to detect novel and evolving attack vectors in addition to high
false positive rate and response time. In this regard, this paper proposes an AI threat detection
framework, employing data science methods to boost cybersecurity performances. The
researchers of this paper have tested the effectiveness of several models using a benchmark
dataset for cyber security, including CICIDS2017 or NSL-KDD and machine learning
techniques such as Random Forest, Support Vector Machine, Logistic Regression and
XGBoost for evaluating performance. Using measures of accuracy, precision, recall and F1-
score, the experiments show that the performance of ensemble learning models is higher than
shallow learning models in this research; XGBoost and Random Forest.
Praveen Kumar Reddy Gouni· International Journal of Soc...· 0 citations
A multi-layered intelligent detection system that unites supervised learning, unsupervised anomaly analysis, and ensemble decision strategies to identify network intrusions, malicious software activity, and stealthy advanced persistent threats in near real time is introduced.
Ameen Pasha.A· International Scientific Jou...· 0 citations
An in-depth analysis of AI-powered threat detection when it is applied to guarantee network security, its principles, techniques, methodology, and the performance results shows that AI threatened detection systems can greatly increase the accuracy, a decrease in false positives and an increase in the response time in comparison to the usual methods.
R. Sharma· International Journal of Mod...· 0 citations
A conceptual framework is proposed that combines detection, explanation, and orchestrated response in a continuous feedback loop that is suitable for zero trust and IoT-enabled critical-infrastructure environments that will allow for continuous retraining of the model.
Jayesh Dalmet· Journal of Digital Security...· 0 citations
This study examines the application of artificial intelligence-powered intrusion detection systems that leverage deep learning architectures and anomaly detection methodologies to identify malicious activities within dynamic network environments and concludes that the convergence of deep learning methodologies and anomaly detection techniques provides a robust foundation for next-generation intrusion detection systems.
M. A. Gandhi, Dinesh Baban Kute, U. Hemavathi· International journal of com...· 0 citations
An Advanced Machine Learning Model for Anticipating and Preventing Cyber Attacks Using Random Forest is presented, which effectively detects cyber threats with high accuracy and reliability, thereby improving threat anticipation and reducing security risks.
T Pushpalatha and RP Rajeshwari· International Journal of Adv...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
