Jul 2026· International Journal of Advanced Research in Science, Communication and Technology· 0 citations· 1 references
TL;DR
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.
Abstract
The rapid advancement of digital technologies, cloud computing, and internet-based services has significantly increased the occurrence of cyber threats and security breaches. Traditional cybersecurity systems primarily rely on signature-based detection techniques, which often fail to identify new and evolving cyberattacks. This limitation creates a need for intelligent and automated solutions capable of detecting malicious activities in real time. To address this challenge, the proposed research presents an Advanced Machine Learning Model for Anticipating and Preventing Cyber Attacks Using Random Forest (RF). The system collects network traffic data, performs preprocessing and feature extraction, and applies the Random Forest algorithm to classify network activities as safe, suspicious, or malicious. In addition, the framework provides real-time monitoring, alert generation, attack classification, and prevention recommendations to enhance cybersecurity management. Experimental results demonstrate that the proposed model effectively detects cyber threats with high accuracy and reliability, thereby improving threat anticipation and reducing security risks. The proposed framework offers a scalable and intelligent solution for strengthening modern cybersecurity infrastructures
Highly accurate systems for detecting threats in real time are needed urgently owing to the exponential growth in cloud-network systems and increasingly sophisticated attacks. The conventional security systems using rules and signatures are inadequate in the changing environment of cloud computing because of evolving attacks.The suggested framework represents an intelligent solution for detecting and classifying threats in cloud computing by using smart machine learning algorithms. An intelligent system will collect data related to cloud network traffic and extract the features, and then it will use the supervisory learning algorithm to classify the threats. The experimental assessment has been performed based on a cloud intrusion detection dataset that consists of various types of attacks including network intrusion, malware, phishing, and data exfiltration. The implemented model had a total classification accuracy of 99.98% that proved to be very reliable with regard to detection of threats in which there are few false positives as well as false negatives. The findings confirm the assertion that the proposed framework offers real-time, scalable and effective security protection that is applicable in contemporary cloud-networks.
Pallapati Solmon, Shaik Khuran Bi, Yerram Lokeshreddy et al.· 2026 4th International Confe...· 0 citations
APTs can be very advanced, able to hide within an organization for years, potentially compromising sensitive data and information. Old-fashioned signature-driven security tools don't keep up with the latest and most advanced attacks, and thus require proactive and intel-driven threats detection products. This research aims to design an early prediction and detection system of Advanced Persistent Threat activities with a machine learning system that works on Cyber Threat Intelligence. The architecture pulls together any and all threat intelligence gathered from network traffic logs, security alerts and external sources including Indicators of Compromise that include suspicious IP addresses, malicious website addresses, and other unusual communications. After data preprocessing and feature engineering, machine learning models such as Random Forest, Support Vector Machine, and Gradient Boosting are employed to learn the harmful user actions and foresee potentially high-risk actions. This suggested methodology is tested using the CICIDS2017 and UNSW-NB15 benchmark datasets of cyber security. It was observed from the experimental results that the best results has been obtained by Random Forest classifier with the highest accuracy as 97.8% after the differentiation of the legitimate and harmful activity. The results show that the integration of Cyber Threat Intelligence with machine learning has a significant impact on early threat detection, reduces the number of false-positive alerts and strengthens cyber security efforts to prevent Advanced Persistent Threat attacks.
Maheshwari S, S. Kirubakaran, R. Chitra et al.· 2026 6th International Confe...· 0 citations
The increasing complexity of cyber threats has exposed the limitations of traditional signature-based intrusion detection systems, creating a need for intelligent and adaptive cybersecurity solutions. This study proposes an artificial intelligence-driven cybersecurity framework for enterprise threat detection using the CICIDS2017 benchmark dataset. The framework incorporates data preprocessing, feature engineering, and supervised machine learning to classify network traffic as benign or malicious. Seven machine learning algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Extra Trees, LightGBM, and XGBoost, were evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that ensemble learning models outperform conventional classifiers, with XGBoost achieving the highest performance, recording 99.42% accuracy, 99.39% precision, 99.31% recall, 99.35% F1-score, and an AUC-ROC of 0.999. LightGBM also demonstrated excellent performance with lower computational time. The findings suggest that the proposed XGBoost-based framework provides an accurate, scalable, and efficient solution for real-time enterprise threat detection and can be effectivel
Sanjida Akter Tisha· The American Journal of Engi...· 0 citations
It is concluded that AI has become an indispensable component of modern cybersecurity strategies and will play a critical role in safeguarding digital infrastructure against emerging cyber threats.
Shaurya Gupta· Innovative Research Thoughts· 0 citations
This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models, and discusses critical challenges affecting the deployment of ML-based IDS.
Ranobir Hasan, H. Jamal, Kamal Kamal et al.· The Eastasouth Journal of In...· 0 citations
The rapid expansion of digital infrastructures has increased the magnitude and sophistication of cyber threats, making timely and accurate threat prediction a foundational requirement for modern cyber-security systems. We use multiple algorithms—including Random Forest, Gradient Boosting Machines, and Deep Neural Networks—on benchmark intrusion-detection datasets and real-world enterprise log samples. Experimental results demonstrate that an ensemble-optimized model achieves improved predictive accuracy, reduced false positives, and enhanced generalization to unseen attack patterns. The study highlights important feature engineering techniques, model-optimization strategies, and deployment considerations for practical cyber-security environments. This research focuses on developing a supervised machine learning model to improve the accuracy of cyber threat prediction by leveraging historical and labeled cyber-security data.
Experimental analysis on a benchmark transaction dataset demonstrates that unsupervised models can achieve over 90% recall in detecting abnormal activities, providing a scalable and adaptive defense against evolving cyber threats in online banking.
Bandana Gupta, Chandra Shekhar Gautam· International Journal of Cre...· 0 citations