Earticle

A variety of intrusion prevention techniques, such as user authentication using passwords, avoidance of programming errors and information protection, has been used to protect computer systems. However information prevention alone is not sufficient to protect our systems as those systems become even more complex with the rapid growth and expansion of Internet technology and local network systems. Moreover, programming errors, firewall configuration errors and ambiguous or undefined security policies add to the system’s complexity. An Intrusion Detection system (IDS) is therefore needed as another layer to protect computer systems. The IDS is one of the most important techniques of information dynamic security technology. It is defined as a process of monitoring the events occurring in a computer system or network and analyzing them to differentiate between normal activities of the system and behaviors that can be classified as suspicious or intrusive. Current Intrusion Detection Systems have several known shortcomings, such as low accuracy (registering high False Positives and False Negatives); low real-time performance (processing a large amount of traffic in real time); limited scalability (storing a large number of user profiles and attack signatures); an inability to detect new attacks (recognizing new attacks when they are launched for the first time); and weak system reactive capabilities (efficiency of response). This makes the area of IDS an attractive research field. In recent years, researchers have investigated techniques such as artificial intelligence, autonomous agents and distributed systems for detecting intrusion in network environments. In this work we have realized an Intrusion Detection System based on Genetic algorithm (GA) approach. For evolving and testing new rules for intrusion detection system the KDD99Cup training and testing dataset were used.

From the past few years the wireless communication system technologies increasing significantly, which shows perceptible impact in the field of telecommunications to develop and improve the entire system away from the means of transmission with wires to the without wires (radio frequency) communication. That means maintenance of QoS is important in the wireless system because it is the key problem of today’s wireless communication system due to unending requirements of user. Quality of Service (QoS) is the capacity to define the performance in any responsible system. Exact description of radio channel through basic parameters and using the mathematical models are important to predict the signal coverage, attainable data rate explicit performance quality of substitute signaling and reception schemes. In This paper we will discuss A Modified way of evaluating loss to sustain the QoS in highly populated area. Here we analyze the QoS in terms of received signal strength. To achieve it we have taken the field data and calculated the path loss at 900 and 1800 MHz frequency and compared graphically with existing path loss models and found a reliable model which gives the results near to the calculated field data. After observing the results we give correction factor to attain more reliability in the existing reliable model for that particular area specified.

The process of verifying the modified software in the maintenance phase is called Regression Testing. The size of the regression test suite and its selection process is a complex task for regression testers because of time and budget constraints. In this research paper, the Bee Colony Optimization (BCO) algorithm for the fault coverage regression test suite prioritization has been presented. In the natural bee colony, there are of two types of worker bees; Scout bees and forager bee, who are responsible for the development and maintenance of the colony. The BCO algorithm developed for the fault coverage regression test suite is based on the behavior of these two bees. The BCO algorithm has been formulated for fault coverage to attain maximum fault coverage in minimal units of execution time of each test case, using two examples whose results are comparable to optimal solution. Average Percentage of Fault Detection (APFD) metrics and charts has been used to show the effectiveness of proposed algorithm.

The morphology and the distribution of graphite grains are the decisive factors in judging the properties of the material cast iron. There are six classes of graphite grain morphology defined by ISO-945 through reference drawings for cast iron graphite grain classification. These reference drawings are universally accepted for classification of graphite grains. Many shape representations and retrieval methods exist. Among them, methods based on Fourier descriptors (FD) achieve acceptable results in classification compared to other methods. Different shape signatures have been exploited to derive FDs, however, FDs derived from different signatures can have significant effect on the result of classification [17]. In this paper, a performance analysis of classification of graphite grains using spectral and spatial features is performed. The neural network classifier based on radial basis function has been employed for classification. The experimentation is carried out using the metallographic images from the well known microstructures library [6]. For training and testing the network, the grain shapes identified in ISO-945 reference drawings and the grain classification by the experts are used. The FDs derived from centroid distance function and neural network classifier with radial basis function yield better classification results for graphite grains.

Customer changing their needs is a typical phenomenon in the software development phase. Predicting impact of a change is needed prior to actual change implementation so that an effective planning can be made. Several high level artifact analysis approaches have been developed including performing the prediction using class interactions. Since design patterns are one of the elements that affect the structure of actual class interactions, this paper proposes a new class interactions prediction approach that considers a design pattern analysis in its process. To demonstrate the design pattern analysis implementation, this paper selects the Boundary-Controller-Entity (BCE) pattern as an instance. A comparison between the new approach (with design pattern analysis) and two selected current approaches (without design pattern analysis) were conducted. The contributions of the paper are: (1) a new class interactions prediction approach; and (2) an evaluation that shows the new approach gives more accurate class interactions prediction than the selected current approaches.

This paper features the kinematic analysis of a SCORBOT-ER Vplus robot arm which is used for doing successful robotic manipulation task in its workspace. The SCORBOT-ER Vplus is a 5-dof vertical articulated robot and all the joints are revolute [1]. The kinematics problem is defined as the transformation from the Cartesian space to the joint space and vice versa. The Denavit-Harbenterg (D-H) model of representation is used to model robot links and joints in this study along with 4x4 homogeneous matrix. SCORBOT-ER Vplus is a dependable and safe robotic system designed for laboratory and training applications. This versatile system allows students to gain theoretical and practical experience in robotics, automation and control systems. The MATLAB 8.0 is used to solve this mathematical model for a set of joint parameter.

There is almost no water left on earth that is safe to drink without purification after 20-25 years from today. This is a seemingly bold statement, but it is unfortunately true. Only 1% of Earth's water is in a fresh, liquid state, and nearly all of this is polluted by both diseases and toxic chemicals. For this reason, purification of water supplies is extremely important. Keeping these things in mind, we have devised a model which will convert the dirty/saline water into pure/potable water using the renewable source of energy (i.e. solar energy). The basic modes of the heat transfer involved are radiation, convection and conduction. The results are obtained by evaporation of the dirty/saline water and fetching it out as pure/drinkable water. The designed model produces 1.5 litres of pure water from 14 litres of dirty water during six hours. The efficiency of plant is 64.37%. The TDS(Total Dissolved Solids) in the pure water is 81ppm.

This paper is based on the development of a fuzzy logic based edge detection technique in digital images. The proposed technique used three linear spatial filters to generate three edge strength values at each pixel of a digital image through spatial convolution process. These edge strength values are utilized as fuzzy system inputs. Decision on whether pixels in focus belong to an edge or non-edge is made in the proposed technique based on the Gaussian membership functions and fuzzy rules. Mamdani defuzzifier method is employed to produce the final output pixel classification of a given image. Experimental results show the ability and high performance of proposed algorithm compared with Sobel and Kirsch operators.

Recently, a malware is growing rapidly and the number of malware applies various techniques to protect itself from the anti-virus solution detection. The reason of this phenomenon is that a longer resident on an infected host guarantees the more profit. As a result, these many protection techniques are applied to a malware, a representative of those is a Packing. It is not an exaggeration that most of the malware currently is distributed. In other words, a packer is widely used for a malware protection. Therefore analysts must determine whether the malware was packed or not and if the malware is packed, what packer is used, before an analysis of the malware. For these procedures, some packer detection tools were released and used. But, the detection performance is not good and there is some false positive and false negative. Therefore we propose a signature generation method that is based on an unpacking process and algorithm in this paper. And we offer the packer detection experiment result using the proposed packer detection signature generation method.

In this paper, we have compared and analyzed the Constrained Flooding, the Real-Time Search and the Adaptive Tree sensor network protocols on MICA platform using Prowler. The simulation results obtained show that the AT protocol has the highest throughput in case of RMGMF while CF protocol has the highest throughput in case of RMSINR. Further, the RTS protocol has the highest throughput in case of NRM. The lifetime of AT, CF and RTS protocols is better in case of RMGMF, NRM and RMGMF respectively.