Evolutionary Intelligence

Intrusion detection system (IDS) using machine learning approach is getting popularity as it has an advantage of getting updated by itself to defend against any new type of attack. Another emerging technology, called internet of things (IoT) is taking the responsibility to make automated system by communicating the devices without human intervention. In IoT based systems, the wireless communication between several devices through the internet causes vulnerability for different security threats. This paper proposes a novel unified intrusion detection system for IoT environment (UIDS) to defend the network from four types of attacks such as: exploit, DoS, probe, and generic. The system is also able to detect normal category of network traffic. Most of the related works on IDS are based on KDD99 or NSL-KDD 99 data sets which are unable to detect new type of attacks. In this paper, UNSW-NB15 data set is considered as the benchmark dataset to design UIDS for detecting malicious activities in the network. The performance analysis proves that the attack detection rate of the proposed model is higher compared to two existing approaches ENADS and DENDRON which also worked on UNSW-NB15 data set.

Vehicle detection and classification has been an area of application of image processing and machine learning which is being researched extensively in accordance with its importance due to increasing number of vehicles, traffic rule defaulters and accidents. This paper aims to review various methodologies used and how it has evolved to give better results in the past years, closely moving towards usage of machine learning. This has resulted in advancing the problem statement towards helmet detection followed by number plate detection of defaulters. Object detection and Text recognition that are available in various frameworks offer built-in models which are easy to use or offer easy methods to build and train customized models.