Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
Cloud environment requires better security due to the exponential growth of information, policies, services, resources, application and users. Flooding attacks such as SYN flood, UDP flood, HTTP flood, and FIN-WAIT have been posing a dangerous threat to Web servers, DNS servers, Mail servers, VoIP servers, etc. These flooding attacks reduce the limited capacity of the server resources and legal users could not able to access the resources of the server. Existing detection techniques used in Firewalls, IPS, IDS, etc., fail to identify the illegitimate traffic due to its self-similarity nature of legitimate traffic, suffer from low detection accuracy and high false alarms. Hence, automatic self intelligent mechanism is needed to identify these attacks that reduce the performance of the server. Intrusion Detection System (IDS) is an updatable, extensible and flexible security component that essential needs for protecting resources from illegitimate traffic and users in cloud environment. This paper deals with the existing computational techniques available with respect to IDS in cloud, their merits and demerits. The contents of this study should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of IDSs.
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 Auto regressive 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.
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.
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.
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.
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.
