Wednesday, May 11, 2011

Final Year Projects | IEEE Projects | Application Projects

11:26 AM Posted by Malla Reddy

Final Year Projects | IEEE Projects | Application Projects

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Novel Defense Mechanism Against Data Flooding Attacks In Wireless Ad Hoc Networks
Mitigating Selective Forwarding Attacks With A Channel-Aware Approach In WMNS-java
On Event-Based Middleware for Location-Aware Mobile Applications
Sparse Bayesian Learning of Filters for Efficient Image Expansion
A DWT Based Approach for Steganography Using Biometrics
An Improved Lossless Image Compression Algorithm Loco-R
Privacy-Conscious Location-Based Queries in Mobile Environments
Layered Approach Using Conditional Random Fields for Intrusion Detection
Localized Multicast Efficient and Distributed Replica Detection in Large-Scale Sensor Networks

Posted: 11 May 2011 03:16 AM PDT

Novel Defense Mechanism Against Data Flooding Attacks In Wireless Ad Hoc Networks

Posted: 11 May 2011 02:46 AM PDT

Novel Defense Mechanism Against Data Flooding Attacks In Wireless Ad Hoc Networks

Mobile users like to use their own consumer electronic devices anywhere and at anytime to access multimedia data. Hence, we expect that wireless ad hoc networks will be widely used in the near future since these networks form the topology with low cost on the fly. However, consumer electronic devices generally operate on limited battery power and therefore are vulnerable to security threats like data flooding attacks. The data flooding attack causes Denial of Service (DoS) attacks by flooding many data packets. However, there are a few existing defense systems against data flooding attacks. Moreover, the existing schemes may not guarantee the Quality of Service (QoS) of burst traffic since multimedia data are usually burst. Therefore, we propose a novel defense mechanism against data flooding attacks with the aim of enhancing the throughput. The simulation results show that the proposed scheme enhances the throughput of burst traffic

Mitigating Selective Forwarding Attacks With A Channel-Aware Approach In WMNS-java

Posted: 11 May 2011 02:43 AM PDT

Mitigating Selective Forwarding Attacks With A Channel-Aware Approach In WMNS

In this paper, we consider a special case of denial of service (DoS) attack in wireless mesh networks (WMNs) known as selective forwarding attack (a.k.a gray hole attacks). With such an attack, a misbehaving mesh router just forwards a subset of the packets it receives but drops the others. While most of the existing studies on selective forwarding attacks focus on attack detection under the assumption of an error-free wireless channel, we consider a more practical and challenging scenario that packet dropping may be due to an attack, or normal loss events such as medium access collision or bad channel quality. Specifically, we develop a channel aware detection (CAD) algorithm that can effectively identify the selective forwarding misbehavior from the normal channel losses. The CAD algorithm is based on two strategies, channel estimation and traffic monitoring. If the monitored loss rate at certain hops exceeds the estimated normal loss rate, those nodes involved will be identified as attackers. Moreover, we carry out analytical studies to determine the optimal detection thresholds that minimize the summation of false alarm and missed detection probabilities. We also compare our CAD approach with some existing solutions, through extensive computer simulations, to demonstrate the efficiency of discriminating selective forwarding attacks from normal channel losses.

On Event-Based Middleware for Location-Aware Mobile Applications

Posted: 11 May 2011 02:42 AM PDT

On Event-Based Middleware for Location-Aware Mobile Applications

As mobile applications become more widespread, programming paradigms and middleware architectures designed to support their development are becoming increasingly important. The event-based programming paradigm is a strong candidate for the development of mobile applications due to its inherent support for the loose coupling between components required by mobile applications. However, existing middleware that supports the event-based programming paradigm is not well suited to supporting location-aware mobile applications in which highly mobile components come together dynamically to collaborate at some location. This paper presents a number of techniques including location-independent announcement and subscription coupled with location-dependent filtering and event delivery that can be used by event-based middleware to support such collaboration. We describe how these techniques have been implemented in STEAM, an event-based middleware with a fully decentralized architecture, which is particularly well suited to deployment in ad hoc network environments. The cost of such location-based event dissemination and the benefits of distributed event filtering are evaluated.

Sparse Bayesian Learning of Filters for Efficient Image Expansion

Posted: 11 May 2011 02:41 AM PDT

Sparse Bayesian Learning of Filters for Efficient Image Expansion
We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones.

A DWT Based Approach for Steganography Using Biometrics

Posted: 11 May 2011 02:38 AM PDT

A DWT Based Approach for Steganography Using Biometrics

Steganography is the art of hiding the existence of data in another transmission medium to achieve secret communication. It does not replace cryptography but rather boosts the security using its obscurity features. Steganography method used in this paper is based on biometrics. And the biometric feature used to implement steganography is skin tone region of images [1]. Here secret data is embedded within skin region of image that will provide an excellent secure location for data hiding. For this skin tone detection is performed using HSV (Hue, Saturation and Value) color space. Additionally secret data embedding is performed using frequency domain approach – DWT (Discrete Wavelet Transform), DWT outperforms than DCT (Discrete Cosine Transform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixels in that sub-band. Different steps of data hiding are applied by cropping an image interactively. Cropping results into an enhanced security than hiding data without cropping i.e. in whole image, so cropped region works as a key at decoding side. This study shows that by adopting an object oriented steganography mechanism, in the sense that, we track skin tone objects in image, we get a higher security. And also satisfactory PSNR (Peak- Signal-to-Noise Ratio) is obtained.

An Improved Lossless Image Compression Algorithm Loco-R

Posted: 11 May 2011 02:37 AM PDT

An Improved Lossless Image Compression Algorithm Loco-R

This paper presents a state-of-the-art implementation of lossless image compression algorithm LOCO-R, which is based on the LOCO-I (low complexity lossless compression for images) algorithm developed by weinberger, Seroussi and Sapiro, with modifications and betterment, the algorithm reduces obviously the implementation complexity. Experiments illustrate that this algorithm is better than Rice Compression typically by around 15 percent.

Privacy-Conscious Location-Based Queries in Mobile Environments

Posted: 11 May 2011 02:36 AM PDT

Privacy-Conscious Location-Based Queries in Mobile Environments

In location-based services, users with location-aware mobile devices are able to make queries about their surroundings anywhere and at any time. While this ubiquitous computing paradigm brings great convenience for information access, it also raises concerns over potential intrusion into user location privacy. To protect location privacy, one typical approach is to cloak user locations into spatial regions based on user-specified privacy requirements, and to transform location-based queries into region-based queries. In this paper, we identify and address three new issues concerning this location cloaking approach. First, we study the representation of cloaking regions and show that a circular region generally leads to a small result size for region-based queries. Second, we develop a mobility-aware location cloaking technique to resist trace analysis attacks. Two cloaking algorithms, namely MaxAccu_Cloak and MinComm_Cloak, are designed based on different performance objectives. Finally, we develop an efficient polynomial algorithm for evaluating circular-region-based kNN queries. Two query processing modes, namely bulk and progressive, are presented to return query results either all at once or in an incremental manner. Experimental results show that our proposed mobility-aware cloaking algorithms significantly improve the quality of location cloaking in terms of an entropy measure without compromising much on query latency or communication cost. Moreover, the progressive query processing mode achieves a shorter response time than the bulk mode by parallelizing the query evaluation and result transmission.

Layered Approach Using Conditional Random Fields for Intrusion Detection

Posted: 11 May 2011 02:33 AM PDT

Layered Approach Using Conditional Random Fields for Intrusion Detection

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance

Localized Multicast Efficient and Distributed Replica Detection in Large-Scale Sensor Networks

Posted: 11 May 2011 02:32 AM PDT

Localized Multicast: Efficient and Distributed Replica Detection in Large-Scale Sensor Networks

Due to the poor physical protection of sensor nodes, it is generally assumed that an adversary can capture and compromise a small number of sensors in the network. In a node replication attack, an adversary can take advantage of the credentials of a compromised node to surreptitiously introduce replicas of that node into the network. Without an effective and efficient detection mechanism, these replicas can be used to launch a variety of attacks that undermine many sensor applications and protocols. In this paper, we present a novel distributed approach called Localized Multicast for detecting node replication attacks. The efficiency and security of our approach are evaluated both theoretically and via simulation. Our results show that, compared to previous distributed approaches proposed by Parno et al., Localized Multicast is more efficient in terms of communication and memory costs in large-scale sensor networks, and at the same time achieves a higher probability of detecting node replicas.