Biblio

The Security is a vital aspect of cloud service as it comprises of data that belong to multiple users. Cloud service providers are responsible for maintaining data integrity, confidentiality and availability. They must ensure that their infrastructure and data are protected from intruders. In this research work Intrusion Detection System is designed to detect malicious server by using Cuckoo Search (CS) along with Artificial Intelligence. CS is used for feature optimization with the help of fitness function, the server's nature is categorized into two types: normal and attackers. On the basis of extracted features, ANN classify the attackers which affect the networks in cloud environment. The main aim is to distinguish attacker servers that are affected by DoS/DDoS, Black and Gray hole attacks from the genuine servers. Thus, instead of passing data to attacker server, the server passes the data to the genuine servers and hence, the system is protected. To validate the performance of the system, QoS parameters such as PDR (Packet delivery rate), energy consumption rate and total delay before and after prevention algorithm are measured. When compared with existing work, the PDR and the delay have been enhanced by 3.0 %and 21.5 %.

Optimization plays an important role in many problems that expect the accurate output. Security of the data stored in remote servers purely based on secret key which is used for encryption and decryption purpose. Many secret key generation algorithms such as RSA, AES are available to generate the key. The key generated by such algorithms are need to be optimized to provide more security to your data from unauthorized users as well as from the third party auditors(TPA) who is going to verify our data for integrity purpose. In this paper a method to optimize the secret key by using cuckoo search algorithm (CSA) is proposed.

In wireless sensor networks (WSNs), congestion control is a very essential region of concern. When the packets that are coming get increased than the actual capacity of network or nodes results into congestion in the network. Congestion in network can cause reduction in throughput, increase in network delay, and increase in packet loss and sensor energy waste. For that reason, new complex methods are mandatory to tackle with congestion. So it is necessary to become aware of congestion and manage the congested resources in wireless sensor networks for enhancing the network performance. Diverse methodologies for congestion recognition and prevention have been presented in the previous couple of years. To handle some of the problems, this paper exhibits a new technique for controlling the congestion. An efficient and reliable routing protocol (ERRP) based on bio inspired algorithms is introduced in this paper for solving congestion problem. In the proposed work, a way is calculated to send the packets on the new pathway. The proposed work has used three approaches for finding the path which results into a congestion free path. Our analysis and simulation results shows that our approach provides better performance as compared to previous approaches in terms of throughput, packet loss, delay etc.

The globalization and outsourcing of the semiconductor industry has raised serious concerns about the trustworthiness of the hardware. Importing Third Party IP cores in the Integrated Chip design has opened gates for new form of attacks on hardware. Hardware Trojans embedded in Third Party IPs has necessitated the need for secure IC design process. Design-for-Trust techniques aimed at detection of Hardware Trojans come with overhead in terms of area, latency and power consumption. In this work, we present a Cuckoo Search algorithm based Design Space Exploration process for finding low cost hardware solutions during High Level Synthesis. The exploration is conducted with respect to datapath resource allocation for single and nested loops. The proposed algorithm is compared with existing Hardware Trojan detection mechanisms and experimental results show that the proposed algorithm is able to achieve 3x improvement in Cost when compared existing algorithms.