Mobile Robot Path Planning: A review

Vol. 7 No. 3

Year: 2013

 
Issue: Jan-Mar

Title : Mobile Robot Path Planning: A review

 

Author Name : Mohan Awasthy, M. Kowar

Synopsis :

This paper reviews the development in the area of vision for path planning of mobile robot. The paper deals with the two major components namely indoor navigation and outdoor navigation Each component has further been subdivided on the basis of structured and unstructured environment. The cases of geometrical and topological models have been dealt separately.

 

Intrusion Detection System in Network Using Particle Swarm Optimization (PSO)

Vol. 7 No. 

Year: 2013

 
Issue: Jan-Mar

Title : Intrusion Detection System in Network Using Particle Swarm Optimization (PSO)

   

Author Name : B. Mahalakshmi, S .Anandamurugan

Synopsis :

The network intrusion detection techniques are important to prevent our systems and networks from malicious behaviors. However, traditional network intrusion prevention such as firewalls, user authentication and data encryption have failed to completely protect networks and systems from the increasing the attacks and malwares. Existing system has proposed a new hybrid intrusion detection system by using intelligent dynamic swarm based rough set (IDS-RS) for feature selection and simplified swarm optimization for intrusion data classification. The purpose of this new local search strategy is to get the better solution from the neighborhood of the current solution produced by Simplified Swarm Optimization SSO. Inorder to improve the performance of SSO and Rough Set Theory, Particle Swarm Optimization (PSO) and Enhanced Adaboost is used. It is also used to improve the detection rate and to reduce the false alarm rate.

A Comparative Study of Prediction in Seabed Mapping

Vol. 7 No. 3

Year: 2013

 
Issue: Jan-Mar

Title : A Comparative Study of Prediction in Seabed Mapping

 

Author Name : Ghedhban Swadi, Karim Al-jebory, Dave Holifield

Synopsis :

In this paper, the performance of two dynamic neural network based predictors in seabed mapping is investigated. The two types of predictors are; the Focused Time- Delay Neural Network (FTDNN) based predictor and the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) predictor. A testing platform has been developed that consists of seabed simulator and sonar simulator. Results show the NARX predictor outperforms the FTDNN predictor.

Security Enhancement of Image Hiding Technique in Mobile Application Using SMS Module

Vol. 7 No. 3

Year: 2013

 
Issue: Jan-Mar

Title : Security Enhancement of Image Hiding Technique in Mobile Application Using SMS Module

 

Author Name : Chandrakant Badgaiyan

 

Synopsis :

Field of data security has attracted everybody’s attention due to digitization of whole world. Now shopping on internet, sending e-mails, transfer of documents, and all other type of data like text, image, audio or video is a common habit and need of mankind. But neither the medium used for data transfer is secured nor the storage media from hackers which encouraged data hiding techniques like Encryption and Steganography.  On the other hand the world is being mobile day by day and almost all type of services is being ported on mobile phones whether it is messaging services, internet services, entertainment services, sensor based services or financial services in form of various mobile applications. Millions of These applications are downloaded on mobile phones daily from all part of world.  In this paper, we are going to accomplish covert communication of images using mobile application as cover data and in order to provide authentication check to our technique we used SMS module concept. SMS module allows only registered or authorised user to download the application and has many other advantages. This technique has overcome the problem of other Steganographic techniques like limited payload capacity with added advantage of mobility and portability as it runs on mobile phones instead of PC. Proposed technique was developed in J2ME platform and tested on Nokia C5 series phones.

Intrusion Detection System using Binary Classifier Algorithm

Vol. 7 No. 3

Year: 2013

 
Issue: Jan-Mar

Title : Intrusion Detection System using Binary Classifier Algorithm

 

Author Name : Dr. S. Jeya, T. John Jeya Singh

Synopsis :

An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current systems depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an automatically tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup’99 intrusion detection dataset.