This paper proposes a new array geometry configuration to improve the two dimensional direction of arrival (2D-DOA) estimation of narrowband moving sources with less complexity. This new array is denoted by verticircular configuration, which is composed of both Uniform linear array (ULA) and Uniform Circular array (UCA) to avoid too much computation for 2D-DOA estimation. The proposed verticircular array is applied with the LPA nonparametric estimator to estimate multiple rapidly moving sources’ parameters (angles and angular velocities) for both azimuth as well as elevation directions. Simulation results show that this nonparametric technique is capable of resolving closely spaced sources provided that their velocities are sufficiently different with decreased computational complexity when using the verticircular array. Different scenarios are used to show the efficient LPA beamformer to distinguish sources that can have the same angles using their different angular velocities. In addition, this paper is to compare the performance of the 2D- LPA DOA estimation algorithm when using verticircular array (proposed array geometry) or rectangular planar array geometry. Simulation results show that the performance of the proposed method with less complexity than that obtained when using rectangular planar array.

Alert correlation is a process that analyzes the alerts produced by one or more intrusion detection systems and provides a more succinct and high-level view of occurring or attempted intrusions. Several alert correlation systems use pairwise alert correlation in which each new alert is checked with a number of previously received alerts to find its possible correlations with them. An alert selection policy defines the way in which this checking is done. There are different alert selection policies such as select all, window-based random selection and random directed selection. The most important drawback of all these policies is their high computational costs. In this paper a new selection policy which is named Enhanced Random Directed Time Window (ERDTW) is introduced. It uses a limited time window with a number of sliding time slots, and selects alerts from this time window for checking with current alert. ERDTW classifies time slots to Relevant and Irrelevant slots based on the information gathered during previous correlations. More alerts are selected randomly from relevant slots, and less or no alerts are selected from irrelevant slots. ERDTW is evaluated by using DARPA2000 and netforensicshoneynet data. The results are compared with other selection policies. For LLDoS1.0 and LLDoS2.0 execution times are decreased 60 and 50 percent respectively in comparing with select all policy. While the completeness, soundness and false correlation rate for ERDTW are comparable with other more time consuming policies. For larger datasets like netforensicshoneynet, performance improvement is more considerable while the accuracy is the same.

Low cost energy-efficient (power based) routing protocols of mobile ad hoc networks (MANETs) increase the lifetime of static networks by using received signal strength (RSS) and battery power status (PS). They require GPS service to find the exact location of mobile nodes. The GPS devices themselves consume power because they need excessive updates to find the stationary nodes for efficient routing. To overcome this, RSS is being used as a metric, followed by, residual battery power. The recent protocols, based on these concepts, provide energy efficient routes during the route discovery phase only. Topological changes make these routes weak in due course of time. To update routes, HELLO process can be used, which however creates unnecessary overhead, delay and consumes power. Hence, these protocols do not update the routes. We propose an energy-efficient reactive routing protocol that uses the RSS and PS of mobile nodes. Proposed Link Failure Prediction (LFP) algorithm uses the link-layer feedback system to update active routes. We use ns2 for simulation of the proposed algorithm. Comparing the results of proposed scheme and existing scheme, in terms of energy consumption, link failure probability, and retransmission of packets, we observe that the proposed scheme outperforms the existing one.

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and significant increase in spectrum efficiency. In this paper, performance analysis of robust channel estimators for Downlink Long Term Evolution-Advanced (DL LTE-A) system using three Artificial Neural Network ANN Architectures: Feed-forward neural network, Cascade-forward neural network and Layered Recurrent Neural Network (LRN) are adopted to train the constructed ANNs models separately using Back-Propagation Algorithm. The methods use the information got by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase it estimates the channel matrix to improve performance of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator. Performance of the proposed channel estimator, Layered Recurrent Neural Network is compared with traditional Least Square (LS) algorithm and ANN based other estimator like Feed-forward neural network and Cascade-forward neural network for Closed Loop Spatial Multiplexing-Single User Multi-input Multi-output (2×2 and 4×4) (CLSM-SUMIMO) in terms of throughput. Simulation result shows LRN gives better performance than other ANN based estimations methods and LS.

Wireless local area network (WLAN) can provide e-government services at all levels, from local to national as the WLAN enabled devices have the flexibility to move from one place to another within offices while maintaining connectivity with the network. However, government organizations are subject to strict security policies and other compliance requirements. Therefore, WLAN must ensure the safeguard the privacy of individual data with the strictest levels of security. The 802.11 MAC specifications describe an encryption protocol called Wired Equivalent Privacy (WEP) which is used to protect wireless communications from eavesdropping. It is also capable of preventing unauthorized access. However, the WEP protocol often fails to accomplish its security goal due to the weakness in RC4 and the way it is applied in WEP protocol. This paper focuses the improvement of existing WEP protocol using the varying secret key for each transmission. This will remove the insecurities that currently make the RC4 unattractive for secured networking and this will add further cryptographic strength if applied to Rijndael algorithm. Our result shows that the proposed algorithm is more suitable for small and medium packets and AES for large packets.

Distributed Sensor Network consists set of distributed nodes having the capability of sensing, computation and wireless communications. Power management, various routing and data dissemination protocols have been specifically designed for DSN, where energy consumption is an essential design issues for routing. Optimization of routing method is an essential for routing of DSN because of long communication distances between distributed sensor nodes and sink node in a network can greatly drain the energy of sensors and decrease the lifetime of the network.
In this paper, simulation is carried out for optimization of routing in DSNs using MATLAB software. The objective is to maximize the network life time and improve the energy efficiency using heuristic technique. A proposed Genetic Algorithm based routing protocol is used for solving an optimization through the evolution of genes parameters, which are coded by strings of characters or numbers and genetic operations (selection, crossover and mutation) are iterated. Finally, the performance parameters for the proposed scheme are evaluated and are shown in terms of energy and routing efficiency, time computation and network lifetime.

The increasing counterfeit of the internet usage has raised concerns of the security agencies to work very hard in order to diminish the presence of the abnormal users from the web. The motive of these illicit users (called intruders) is to harm the system or the network either by gaining access to the system or prohibiting genuine users to access the resources. Hence in order to tackle the abnormalities Intrusion Detection System (IDS) with Data Mining has evolved as the most demanding approach. On the one end IDS aims to detect the intrusions by monitoring a given environment while on the other end Data Mining allows mining of these intrusions hidden among genuine users. In this regard, IDS with Data Mining has been through several revisions in consideration to meet the current requirements with efficient detection of intrusions. Also several models have been proposed for enhancing the system performance. In context to improved performance, the paper presents a new model for intrusion detection. This improved model, named as REP (Reduced Error Pruning) based Intrusion Detection Model results in higher accuracy along with the increased number of correctly classified instances.

Conventional approaches for adapting security enforcement in the face of attacks rely on administrators to make policy changes that will limit damage to the system. Paradigm shifts in the capabilities of attack tools demand supplementary strategies that can also adjust policy enforcement dynamically. We extend the current research by proposing an approach for integrating real-time security assessment data into access control systems. Critical application scenarios are tested to examine the impact of using risk data in policy evaluation and enforcement.