Sumanth Yenduri

First stage detection of compromised nodes in sensor networks

The node capture attack in wireless sensor networks (WSNs) can be decomposed into three stages: physically capture of node, redeployment of compromised node, and rejoin the network for various insider attacks. A well accepted belief — that the physical capture is easy to implement and that its detection is difficult — has directed majority of research effort to defense in stages three and two. The belief was recently proved false [4]. The discovery made first stage detection an attractive tactic. This paper proposes a new approach to detect the attack at the first stage. The detection is based upon the discovery of missing and malfunction of nodes due to the physical capture. The approach is simple, reliable, energy-efficient, completely local, and completely distributed. It can be used along with other approaches at stages two and three. Hence it could be employed in wide range of applications. In simulation we compare our proposal to one of latest successful research in the area. [2] Simulation results have proved that our approach is more effective and more efficient for in detection of node capture attacks.

Performance Evaluation of Imputation Methods For Incomplete Datasets

In this study, we compare the performance of four different imputation strategies ranging from the commonly used Listwise Deletion to model based approaches such as the Maximum Likelihood on enhancing completeness in incomplete software project data sets. We evaluate the impact of each of these methods by implementing them on six different real-time software project data sets which are classified into different categories based on their inherent properties. The reliability of the constructed data sets using these techniques are further tested by building prediction models using stepwise regression. The experimental results are noted and the findings are finally discussed.

Distributed first stage detection for node capture

The node capture attack on wireless sensor networks (WSNs) can be broken into three stages: node capture, node redeployment, and insider attacks. Existing detection techniques of the attack are mostly implemented in the second stage. Recent discovery proved the feasibility of quick and effective detection in the first stage. We propose a hello message based first stage detection scheme, which is faster, easier to execute, and more reliable than existing approaches. Two implementations FSD and SEFSD are provided. FSD is completely decentralized. SEFSD utilizes the base station to achieve energy efficiency and better security. Simulation shows the scheme outperforms existing techniques while illustrating SEFSD outperforms FSD in terms of message and energy overhead.

Analyzing Intrusion Detection System: An ensemble based stacking approach

Intrusion Detection System (IDS) is an application software which detects the presence of hostile or intrusive elements inside the system. As the nature and type of the intrusions are continuously changing, a simple IDS cannot completely tackle the security threat. In this paper, we have proposed an IDS model, which classifies different types of intrusion attacks based on Stacking classifier. Stacking is an ensemble based classifier. We have achieved good accuracy while classifying the KDD-Cup 99 dataset and that has been achieved with 10 fold cross validation.

Resilient Multi Sink Networks Using Simplistic Hop Based Routing

In this paper, we demonstrate the use of a simplistic time based routing protocol called Simple Resilient Multi-hop Routing (SRMR) for a multi sink wireless sensor network (WSN). SRMR will allow the network to maximize efficiency by using a combination of performance metrics. For this simulation we will use the approximate distance measure based upon travel time of messages between sensor nodes. With multi sink architecture, we show efficient routing of data from the network to the closest sink based upon the selected metric. In doing so, we will be able to allow the network to become more resilient to network isolation, excess energy usage, and suboptimal throughput.

Production Design by Simulation Software Witness A Case Study

Today’s customers demand high quality,customized goods and services, quickest delivery at low prices. This has made the design and management of systems more challenging. Companies can not afford to design their production system in a way that does not optimize the scarce resources. With the advancement of computer simulation software in the manufacturing domain, the production design is much easier. The key success of production design depends on the interpretation of simulation results. This paper illustrates the use of computer simulation to design the production of a manufacturing company that produces snow melting modules. The analysis presented here describes the production design process and compares the performance of new design with the existing system performances.

An Agglomerative Clustering Methodology For Data Imputation

The prediction of accurate effort estimates from software project data sets still remains to be a challenging problem. Major amounts of data are frequently found missing in these data sets that are utilized to build effort/cost/time prediction models. Current techniques used in the industry ignore all the missing data and provide estimates based on the remaining complete information. Thus, the very estimates are error prone. In this paper, we investigate the design and application of a hybrid methodology on six real-time software project data sets in order to better the prediction accuracies of the estimates. We perform useful experimental analyses and evaluate the impact of the methodology. Finally, we discuss the findings and elaborate the appropriateness of the methodology

Digital analysis of thermal infrared imagery using temperature mapping

Land surface temperature mapping is one of the key parameters in the physics of land surface processes. The present study covers recovering of land surface temperature from space. The study includes the separation of emissivity and temperature by using different methods. It includes computation of radiant and kinetic temperatures of the land features from Landsat 5 TM thermal infrared (TIR) data. Temperature mapping is done with thermal infrared (TIR) daytime and nighttime data. The mapping is also done using spatially improved daytime and nighttime data. As a final note, the findings between the use of improved TIR data and raw TIR data are compared and discussed.

An Intelligent Vertical Handoff Decision Strategy Based On Network Performance Prediction and Consumer Surplus Value for Next Generation Wireless Networks

Vertical handoff (VHO) decision services play an essential role in the current wireless networks. Many algorithms designed aim at quality of service while granting seamless roaming amongst a number of heterogeneous access networks. In this study, the authors proposed an intelligent VHO decision strategy targeting to maximise user satisfaction without compromising on quality of service for non-real time mobile based services. The proposed strategy applies consumer surplus value (CSV)-based pricing scheme and also a prediction system to approximate network performances at various intervals. Finally, a fuzzy rule-based behavioral system is proposed to find optimal network by considering both CSV as well as network performance as stimulus parameters. Simulation is carried out using OM Net++ and MATLAB, and the results are acknowledged with discussion and analysis.

Rerouting Transit Traffic for Effective Monitoring

Traditionally, transit traffic monitoring is performed by placing monitors throughout a network. Over time, there can be changes in the traffic, rendering these monitors useless. Raza et al. developed a framework called ‘Measu Routing’ where they rerouted traffic over fixed monitors to ensure effective monitoring. In this paper, we attempt to build software that uses the methodology presented by Raza et al.