Traffic Congestion Model in India by Shock Wave Theory

This manuscript is a series of traffic flow analysis in India and it describes traffic congestion model by using shockwave theory from fluid mechanism. This is unique study for emerging country India traffic flow analysis during more than one month in October 2020. In order to chaotic traffic flow analysis in India, author observes one moth traffic flow data from the traffic monitoring cameras in 26 locations in a city where it is one of major city Ahmedabad in Gujarat states of India. In terms of traffic congestion, it is sued occupancy parameter of traffic flow data which indicates road occupancy percentage by vehicles. By using shock wave theory, author defines two traffic congestion model which are “forwarding traffic congestion” model and “stacking traffic congestion” model. These models explain traffic congestion condition and it is able to provide hint for solving traffic congestion problem in India.

Detection of Botnet Based Attacks on Network

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.