Advanced Threat Intelligence Systems for Early Warning of Cyber Hacking

Advanced Threat Intelligence Systems for Early Warning of Cyber Hacking

연구 분야: Safety

학회: International Conference on Advancements in Smart Computing and Information Security

As we continue to deploy state-of-the-art cybersecurity measures, targeted cyber-attacks have increased in scale and frequency. A Research on a New Advanced Threat Intelligence System for Early Warning in Cyber Attack based on Realtime Monitoring, Machine Learning and Big data Analytics The system combines, tidies and synthesizes data across disparate sources. It uses supervised and unsupervised machine learning models to assess the risks in that case. ATIS have introduced big data tools to conduct fast large-scale data analysis to predict threats online and activate alerts; In a controlled evaluation of ATIS, there was seen to be an improvement in the accuracy and reduction in false positives associated with threat detection. This new method gives an end-to-end cybersecurity solution for proactive security services, which allows organizations to gather such required data on a timely basis and respond when the attack takes place. The main objective of this research is to predict the cyber attack in the field of data engineering. Here the proposed algorithm Random Forest (RF) gave best accuracy of 95.154%, precision of 92%, recall of 94%, f1 score of 93% and AUC-ROC of 0.97 which is high compared to other existing algorithms.