Intrusion detection is a process of identifying the Attacks in the networks. The main aim of IDS is to identify the Normal and Intrusive activities. In recent years, many researchers are using data mining techniques for building IDS. Due to the nonlinearity and quantitative or qualitative network data traffic IDS is complicated. For making the IDS efficient we have to choose the key features. Support Vector Machine (SVM) gives the potential solution for IDS problem. SVM suffers by selecting the suitable SVM parameters. Here we propose a new approach using data mining technique such as SVM and Particle swarm optimization for attaining higher detection rate. PSO is an Optimization method and has a strong global search capability. The SVM-PSO Method is applied to KDD Cup 99 dataset. Free parameters are obtained by standard PSO for support vector machine and the binary PSO is used to obtain the best possible feature subset at building intrusion detection system. The propose technique has major steps: Preprocessing, Feature Reduction using Information Gain, Training using SVM-PSO. Then based on the subsequent training subsets a vector for SVM classification is formed and in the end, classification using PSO is performed to detect Intrusion has happened or not. The experimental result shows that SVM-PSO acquire high detection rate than regular SVM Method algorithm.
A Mobile Ad-Hoc Network (MANET) is a arrangement of wireless mobile nodes which forms a temporary network for the communication without the access point, high availability of wireless devices in everyday is a measure factor in the success of infrastructure-less networks. MANET is dealing with both kinds of attacks, active and passive attacks at all the layers of network model. The lack in security measures of their routing protocols is alluring a number of attackers to intrude the network. A particular type of attack; known as Wormhole, which is launched by creation of tunnels and it results in complete disruption of routing paths on MANET. This paper presents a technique NWLID: Normalized Wormhole Local Intrusion detection Algorithm which is the modified version of Local Intrusion Detection Routing Security over mobile adhoc Network which has an intermediate neighbor node discovery mechanism, packet drop calculator, individual node receiving packet estimator followed by isolation technique for the confirmed Wormhole nodes . Result shows the effect of wormhole attack on normal behavior and improvement of performance after the application of proposed scheme. The effectiveness of NWLID algorithm is evaluated using ns2 network simulator.
The conservation of energy and the improvement in lifetime of the network has been a challenging task in Mobile Ad hoc Networks (MANET) due to their dynamic and unpredictable behavior and topology change. A route selection and packet drop due to limited buffer(queue) size are two important factor that causes energy consumption by handling retransmission of dropped packets. So many energy aware routing and queue management strategies have been introduced individually. By combining energy based routing with queue management, the lifetime of the network can be increased most effectively. This paper proposes integrated maximum energy based routing with prediction of the route failure by energy estimation and Dynamic queue initialization to reduce packet drop. We modified AODV protocol and drop-tail queue. A Simulation has been performed on NS-2 with the parameter packet delivery fraction, Throughput, endto end delay and number of packets. By comparison planned approach with the old one better result has obtained.
To ride the tide of change which is inevitable, innovations are necessary. By using the concept of virtualization most of enterprises are trying to reduce their computing cost. This demand of reducing the computing cost has led to the innovation of Cloud Computing. Nowadays organizations recognized cloud for it different attractive property such as economically attractive and use it to host their services. So that their services available easily and economically to their users. But also many organization put security in their top concern before adopting the cloud service. One of the most significant problem that associated with cloud computing is cloud security that drawn a lot of analysis and research within past few years. Inside the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the actual prognosis associated with zombie exploration problems is exceedingly hard. This is because cloud users might deploy somewhat insecure purposes on the exclusive products. NICE is a Network Intrusion detection and Countermeasure selection in virtual network systems (NICE) design to establish an intrusion detection framework which is defense-in-depth in nature. Into the intrusion detection processes an attack graph analytical procedures is incorporated by NICE for better attack detection. In this paper we proposed to implement NICE-A as a host based agent instead network based so the data delivery time between sender and intended destination is saved as NICE-A is implemented in destination (which is cloud server in our case) and for large amount of data this definitely shows improvement in computation time. Moreover as NICE-A is implemented as host based so CPU utilization is also improved.
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