A Comprehensive Study on CIC-IDS2017 Dataset for Intrusion Detection Systems

Abstract

In the present era of digital technology, electronic assaults result in the compromise of confidential information and substantial financial ramifications for individuals, organizations, and nations. Hence, the role of cybersecurity resources is essential in safeguarding data from any Cybersecurity event. Researchers are prioritizing the use of anomaly-based intrusion detection systems for the identification of cybersecurity threats. Machine learning algorithms are crucial in this endeavor since they possess the ability to identify such attacks reliably. It’s a dataset that enhances the efficiency of the machine learning algorithms. The datasets currently employed in intrusion detection systems exhibit a notable deficiency in accurately representing actual network threats and attacks. They also contain a significant number of concealed threats, thereby restricting the precision of detection within existing machine-learning intrusion detection system approaches. Consequently, these systems are unable to effectively cope with the growing number of novel attacks in the real word scenarios, in cloud environments. The objective of this study is to integrate the categorization and analysis of current datasets to enhance the generation of future datasets that accurately replicate actual network data. This will enhance the efficacy of the next generation of intrusion detection systems and correctly mirror network threats.