Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.
In the field of network security, researchers have implemented different models to secure the network. Intrusion Detection System is also one of them and Snort is an open source tool for Intrusion Detection and Prevention System. Today intrusion Detection System is a growing technology in network security and mostly researchers have focused in this field, some of them used signature or rule-based technique and some are anomaly based techniques to improve security of network. In this paper we propose a rule-base Intrusion Detection System with our self generated new Efficient Port Scan Detection Rules (EPSDR). These rules will be used to detect naive port scan attacks in real time network using Snort and Basic Analysis Security Engine (BASE). BASE is used to view the snort results in font-end web page because Snort has no graphic user interface. In This rule-based Intrusion Detection
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.