Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset using ER-VEC

Securing a network the preliminary defense mechanisms of applied Security Compliance along with IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) that are applied all over the networks to block the passage of malicious content into it. With the advent of modern technologies and innovations in every field, there has been a significant load on the security architecture to provide for faultless, secure, and robust infrastructure. In today's modern day, firewalls are capable of blocking a major portion of malicious activity from creeping into the network. Still, attackers have been successful in penetrating in the form of internal attacks and can perform their targeted exploitation, like denial of service, link failures, ransomware, and so on, which has disastrous consequences. Thus, such anomalies do occur that pose a threat to data security and the underlying structure. This study aims to propose a novel technique, including the use of ER-VEC (Extra-Tree Random Voting Ensemble Classifier), to classify various attack network packets into benign and suspicious categories. We also did comparisons with SVM(support vector machine), DT (decision tree), RF(random Foreset), and EXTC(extra tree extension) proposed by other researchers, to testify our models we have also tested and compared on the same datasets, and compared the results proved to be more effective and efficient than the previous methodologies. This article started with a section introduction then section two related work which included with problem statement, section three with the proposed predicted model, then we covered result analysis and comparative study with proposed strategies by other researchers and conclusion and future scope.