Availability is one of the primary security issues in Cloud computing environment. The existing solutions that address the availability related issues can be applied in cloud computing environment, but because of their unique characteristics, such as on-demand self service, rapid elasticity, etc., there is a need to develop a detection mechanism that must satisfy the characteristics and an optimal profit for the Cloud Service Provider (CSP). A solution named Escape-on- Sight (EoS) algorithm is proposed in this paper that helps in detecting the attacker’s characteristics by analyzing traffic conditions stage by stage and protects the Data Center (DC) from malicious traffic. The profit analysis shows that the proposed approach has a reasonable chance of deploying EoS mechanism at DCs that are prone to DDoS attacks.
Distributed Denial of Service (DDoS) in a Cloud leads to a high rate of overload conditions, which subverts the Data Center (DC) performance and ends up in resource unavailability. This work proposes a “Trilateral Trust mechanism” which helps in detecting different kinds of attack groups at different points of time. It is the direct trust based defense mechanism for segregating legitimate and attack groups from the vast number of incoming requestors. It is a hybrid mechanism of trusts that follows the zero trust approach initially and eventually supports both Mutual trust and Momentary trust. This combinatorial trust mechanism helps in detecting almost all kinds of overload conditions at a cautionary period. Detecting the high rate of an attack at an earlier moment of time could reduce the traffic impact towards DC. The simulation results and profit analysis proved that the mechanism proposed is deployable at an attack-prone DC for resource protection, which would eventually benefit the DC economically as well.
Wireless Mesh Sensor nodes are deployed in harsh environments, like Industrial Wireless Mesh Sensor Networks (IWMSN). There the equipment is exposed to temperature and electrical noise, so providingareliable, interference free and efficient communication in this environment isachallenge. We proposea Multi Route Rank based Routing (MR3) protocol, which enhances the link dynamics for IWMSNand also provides interference free reliable packet delivery in harsh environments. The rank ofanode is estimated based on density, hop count, energy and Signal to Interference plus Noise Ratio (SINR). The route discovery phase finds the rank value to forward the data packet inareliable path. Once the forwarding path is established, subsequently the data packets can be propagated towards the destination without using any location information. Our simulation results show that this method improves the packet delivery ratio and the throughput tremendously, and at the same time minimizes the packet delay, in heavy traffic condition.
In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the optimal routing by satisfying the Quality of Service (QoS) constraints is an ambitious task. Multiple paths are available from the source node to the gateway for reliability, and sometimes it is necessary to deal with failures of the link in WMN. A major challenge in a MCMR-WMN is finding the routing with QoS satisfied and an interference free path from the redundant paths, in order to transmit the packets through this path. The Particle Swarm Optimization (PSO) is an optimization technique to find the candidate solution in the search space optimally, and it applies artificial intelligence to solve the routing problem. On the other hand, the Genetic Algorithm (GA) is a population based meta-heuristic optimization algorithm inspired by the natural evolution, such as selection, mutation and crossover. PSO can easily fall into a local optimal solution, at the same time GA is not suitable for dynamic data due to the underlying dynamic network. In this paper we propose an optimal intelligent routing, using a Hybrid PSO-GA, which also meets the QoS constraints. Moreover, it integrates the strength of PSO and GA. The QoS constraints, such as bandwidth, delay, jitter and interference are transformed into penalty functions. The simulation results show that the hybrid approach outperforms PSO and GA individually, and it takes less convergence time comparatively, keeping away from converging prematurely.
In the present age of Internet, data is accumulated at a dramatic pace. The accumulated huge data has no relevance, unless it provides certain useful information pertaining to the interest of the organization. But the real challenge lies in hiding sensitive information in order to provide privacy. Therefore, attribute reduction becomes an important aspect for handling such huge database by eliminating superfluous or redundant data to enable a sensitive rule hiding in an efficient manner before it is disclosed to the public. In this paper we propose a privacy preserving model to hide sensitive fuzzy association rules. In our model we use two processes, named a pre-process and post-process to mine fuzzified association rules and to hide sensitive rules. Experimental results demonstrate the viability of the proposed research.