Resource trading in cloud environments for utility maximisation using game theoretic modelling approach

We develop a novel auction-based scheme for trading free processors in cloud computing environment. Our scheme allows clients to fairly compete for the offered processors. Second-price auction is used where each client bids for the processors. The client that makes the higher bid wins the auction and starts using the processors for executing its jobs. The winner pays the second highest bid. Maximising profit is the key objective for the cloud provider (CP). The optimisation problem of renting processors on the cloud market is a complex and challenging task. The proposed mechanism takes into account the market demand and the economic concerns for the CP and clients. Within this context, we show the existence of a Nash equilibrium which leads to a unique allocation under certain processors distribution. Numerical analysis stresses the ability of our approach to maximise the client's utility under varying cloud environment conditions.     

Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks

An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the same security concerns of legacy ad hoc networks. Unlike exiting works, we consider, in this paper, detection a common attack where nodes modify safety message or drop them. Unfortunately, detecting such a type of intrusion is a challenging problem since some packets may be lost or dropped in normal VANET due to congestion without malicious action. To mitigate these concerns, this paper presents a novel scheme for minimizing the invalidity ratio of VANET packets transmissions. In order to detect unusual traffic, the proposed scheme combines evidences from current as well as past behaviour to evaluate the trustworthiness of both data and nodes. A new intrusion detection scheme is accomplished through a four phases, namely, rule-based security filter, Dempster–Shafer adder, node’s history database, and Bayesian learner. The suspicion level of each incoming data is determined based on the extent of its deviation from data reported from trustworthy nodes. Dempster–Shafer’s theory is used to combine multiple evidences and Bayesian learner is adopted to classify each event in VANET into well-behaved or misbehaving event. The proposed solution is validated through extensive simulations. The results confirm that the fusion of different evidences has a significant positive impact on the performance of the security scheme compared to other counterparts.