Network

Decision making method of telecommunications channel throughput distribution based on linear programming and neuro fuzzy predicting tool.

Ad-hoc networks, due to their improvised nature, are frequently established insecure environments, which makes them susceptible to attacks. These attacks are launched by participating malicious nodes against different network services. Routing protocols, which act as the binding force in these networks, are a common target of these nodes. On Demand Route Routing Protocol (ODRRP) is a Broadcast Reply network routing protocol for Mobile Ad hoc Network (MANET). Black hole attack is one of the severe security threats in ad-hoc networks which can be easily employed by exploiting vulnerability of on-demand routing protocols such as ODRRP. In this paper we proposed a solution for identifying the malicious node in ODRRP protocol suffering from black hole attack.

Load balancing using clustering can increase network scalability. This paper attempting to apply efficient techniques of load, energy and security such that network life can be increased with security and also its reliability. Continuous aggregation is required in sensor applications to obtain the temporal variation information of aggregates. It helps the users to understand how the environment changes over time and track real time measurements for trend analysis. Due to the high frequency and the long period of a continuous aggregation in every epoch, the false temporal variation pattern would incur a great communication cost. In this paper, we propose a clustering technique which will balance the load among the cluster by using some backup nodes. In the continuous aggregation, the attacker could manipulate a series of aggregation results through compromised nodes to fabricate false temporal variation patterns of the aggregates .We detect and verify a false temporal variations pattern by checking only a small part of aggregation results to reduces a verification cost. A sampling based approach is used to check the aggregation results and we also proposed a security mechanisms to protect the sampling process.

Mobile ad hoc networks (MANET) play an important role in connecting devices in pervasive environments. Each node in MANET can act as source and router. In this paper, we propose a On Demand Route Routing Protocol (ODRRP) with Broadcast Reply which combines the merits of both proactive and reactive approach. Like proactive approach, it maintains routing table at every node. However, it differs from proactive approach; that the routing table is not built prior to communication. Routing table is built in incremental steps during route discovery. Route discovery takes place like reactive approach only on demand. ODRRP takes advantage of broadcast nature in MANET for route discovery and store maximum information in the routing tables at each node. Broadcast natures avoid handshaking of RTS and CTS and effectively utilize trans-receiver antennas which reduce power consumption and effectively utilize bandwidth. The simulation of ODRRP carried out in network simulator (NS-2.32) which shows significant improvement in packet delivery ratio.

The decision Mechanism is the concluding phase of any decision making process. This paper discusses on the different methodologies available for implementing the decision mechanisms. The paper preambles with a brief description on set of conventional MCDM techniques like Analytical Hierarchy Process (AHP), Simple Additive weighting Method (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Rational Analysis (GRA), along with benefits and limitations of each technique. The different intelligent/soft computing techniques that are widely used in decision making processes like fuzzy logic, neural networks are discussed and finally confines the discussion is confined to the different neural network (NN) based decision support systems. The paper proposes an architecture for fuzzy neural network based call admission control decision mechanism for heterogeneous wireless networks environment.

This study seeks to achieve a complete elucidation of structures of unknown Eudesmane sesquiterpenes from their 13C values. The 13C values for each of the fifteen (15) positions of the skeletons of the Eudesmane compounds were predicted using Generalized Regression Neural Network (GRNN). From the predicted 13C values, GRNN and Scatter Plot methods were used to predict the substituents attached to each position on the skeleton of the Eudesmane compounds. Recognition of the test compounds ranged between 40 and 100%. GRNN and Scatter plots demonstrated great potential for use in the structural elucidation of unknown compounds from 13C values.

Number of smartphones users is increasing rapidly and Android is currently the most popular Smartphone operating system. However, users feel their private information at threat, facing a rapidly increasing number of malwares for Android which significantly increasing that of other platforms. There are large number of apps available for the ease and use of the smartphone users. When a user installs any application from the google play store he/she is asked to grant some permissions to function the particular application properly. User either has to accept all those permissions in order to install the application on his/her device or he/she has to deny all those permission and terminate the installation of the application. A normal smartphone user is not aware of most of the permissions asked during installation so he/she tends to accept those all permissions in order to use the application. This introduces a potential threat to the users device. With this smartphones usage, mobile malware attacks are also growing. The application that we are developing will help user to identify the malicious applications that are installed on the device. And if a user finds any malicious activity being performed by any application then he/she can change the necessary permissions to avoid the malicious activity being done by the application. All this will be done post the installation of any application. So user will first have to accept all those permissions and get the app installed on his/her device from the Google play store. And then user can modify (allow/deny) the permissions the application is using. Our proposed application will have a scanning activity which will tell the user which applications are malicious and may harm the device. The application will use machine learning approach to some extent for scanning the applications to determine the application is malicious or not.

Mobile ad hoc networks will appear in environments where the nodes of the networks are absent and have little or no physical protection against tampering. The wireless nodes of MANET are thus susceptible to compromise and are particularly vulnerable to denial of service (DoS) attacks launched by malicious nodes or intruders. Flooding attack is one such type of DoS attack, in which a compromised node floods the entire network by sending a large number of fake RREQs to nonexistent nodes in the network, thus resulting in network congestion. In this paper, the security of MANET AODV routing protocol is investigated by identifying the impact of flooding attack on it. A simulation study of the effects of flooding attack on the performance of the AODV routing protocol is presented using random waypoint mobility model. The simulation environment is implemented by using the NS-3 network simulator. It is observed that due to the presence of such malicious nodes, average percentage of packet loss in the network, average routing overhead and average bandwidth requirement? all increases, thus degrading the performance of MANET significantly.

Mobile ad hoc networks will appear in environments where the nodes of the networks are absent and have little or no physical protection against tampering. The wireless nodes of MANET are thus susceptible to compromise and are particularly vulnerable to denial of service (DoS) attacks launched by malicious nodes or intruders. Flooding attack is one such type of DoS attack, in which a compromised node floods the entire network by sending a large number of fake RREQs to nonexistent nodes in the network, thus resulting in network congestion. In this paper, the security of MANET AODV routing protocol is investigated by identifying the impact of flooding attack on it. A simulation study of the effects of flooding attack on the performance of the AODV routing protocol is presented using random waypoint mobility model . The simulation environment is implemented by using the NS-2 network simulator. It is observed that due to the presence of such malicious nodes, average percentage of packet loss in the network, average routing overhead and average bandwidth requirement? all increases, thus degrading the performance of MANET significantly.

Security threats for the network services have been constantly increasing day by day. Distributed denial of service (DDoS) attack is one such kind of security threat which involves multiple systems generating a large amount of traffic towards a target machine and thereby making any service from that target machine or server unavailable to its clients. This threat by nature needs no control over the target system. Traditional methods of detecting DDoS attacks are mostly centralized in nature and highly disadvantageous. To overcome the disadvantages of those schemes, we propose a distributed methodology which involves installing the attack detectors at various parts of the network. Each router in the network will monitor the traffic flowing through it and if any anomaly in the traffic pattern is detected, it will raise an alarm to the nearby routers. The alarm propagates to all the routers through which the attack flows. By this way a tree like construct is made, which will have information about number of alarms raised and the path of the attack flow. If the construct shows any converging pattern then it is declared as DDoS attack.