Network

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.

The role accurate measurement and reception of electromagnetic signal play in telecommunications cannot be over emphasized. These measurements and reception are a function of terrain/topology, level of infrastructural development, vegetation and other factors. Wave propagation models are very important and necessary tools for determining the radio wave propagation characteristics of a user environment or area. The research handles site specific measurements for Lagos at frequencies of 900MHz and 1800MHz and comparison done with standard calculated values. From the analysis of data collected, the mean square errors (µe) ranged from 5.39dB to 13.60dB for Okumura Hata at 900MHz. For COST 231 Hata at this frequency, it was from 5.06dB to 13.52dB. This agrees with the acceptable International range. The acceptable range lies between 1? µ ? 15dB (Wu and Yuan 1998). The mean square error at 1800MHz varied from 4.66dB to 5.06dB.

Identifying the occurrence of congestion in a Mobile Ad-hoc Network is a major task. The existing TCP congestion control techniques do not handle the unique properties of shared wireless multi-hop link. Frequent changes in the ad-hoc network topology poses great deal of problems for congestion control. There are several approaches proposed for detecting and overcoming the congestion in the mobile ad-hoc network. In this paper we try to bring out some of the congestion control techniques and its salient features.

Mobile ad-hoc networks have become very popular because of their widespread usage. Cooperation among the nodes in ad-hoc networks is an important issue for communication to be possible. But some nodes do not cooperate in communication and saves their energy. These nodes are called as selfish nodes. In the literature there are many methods which deal with the selfish behavior of nodes. This paper compares different methods available for reducing the effect of selfish nodes in mobile ad hoc networks.

Nature Inspired Computation defined as a computational intelligence paradigm inspired from natural biological systems (such as human brain and neural systems, Genetic Algorithms; Bees and Ants Colonies Optimization, etc........). Recently, this paradigm has been successfully applied for solving computational optimization problems, in many fields (such as, machine learning, and engineering design, etc.........). Moreover, by considering its unique computational intelligence characters of self-adaptive, self-organizing and self-learning. It is relevant to adopt its application for modeling and performance evaluation of educational Systems. Herein, the presented work motivated by recent research publications inspired by two interdisciplinary computational intelligence Models. Both have been characterized by challenged bridging of two Natural inspired Models across an interesting practical educational issue. More specifically, this piece of research concerned with the theoretical analysis and evaluation of constructive bridges of Artificial Neural Networks (ANNs) & Ant Colony Systems (ACS) across Open Learning Systems. Noting that motivating research publications include the following two topics: 1- Assessment of students' adaptability using ANNs models (Cognitive Styles Approach). 2- Modeling of Cooperative e-Learning Performance (ACS Approach).

In this article neuro-fuzzy systems for active management of packet queue of the output port of a router of a telecommunication network are considered. The employment of this systems allows reduction of average packet delay in a queue of the output port of a router and lowering of the probability of packet discard.

This research paper suggests a load balancing algorithm using fuzzy logic methodology so that maximum Quality of Service can be attained. Avoidance of jamming of packets is one of the key performance objectives of traffic management in MPLS networks. Load balancing can avoid the congestion caused due to inefficient allocation of network resources. Another feature of the network performance is Quality of Service (QOS). QOS in telecommunications jargon, is a measurement used to determine how well that network is satisfying the end user's requirements. The Mean Opinion Score (MOS) is an important feature in determining the QOS. MOS is a measurement of the quality delivered by the network based on human observation at the destination end. Precisely we can tell average opinion score (MOS) provides a numerical indication of the perceived quality of received media after compression and transmission.