National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

Preventing DDoS using Bloom Filter: A Survey

Abstract: Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending a DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.

Assessing channel morphology and prediction of centerline channel migration of the Barak River using geospatial techniques

Abstract: Barak River is highly meandering rivers flowing through the alluvial plains of Assam in India. However, due to dynamic system, it is found that channel being subjected to regular shifting which creates uncertainty to the habitants residing nearby the river. Therefore, it is anticipated to carry out a study regarding changes in channel morphology and prediction of centerline channel migration during 1984–2030, using multiperiod Landsat remote sensing images along with autoregressive integrated moving average model (ARIMA). From morphometric analysis, it was found that the mean value of meander length (ML), meander width (MB), and meander ratio (MR) indicates an increasing trend, while sinuosity (C), wavelength (λ), and radius of curvature (RC) show a decreasing trend. The outcome of ARIMA model specifies that channel shifting of mid-line is going to change suddenly either to rightward or leftward directions. Throughout the whole alluvial part of the Barak River, rightward side is recognized as major concern. Observed and predicted values have shown a good R2 value (R2 = 0.89 and R2 = 0.88) at CS-30 and CS-18 respectively. Also, lowest RMSE is observed at CS-12 and highest RMSE is observed at CS-21. Finally predicted values were generated for the estimation of centerline channel shifting between two time intervals (2017–2023 and 2023–2030), which shows that the channel shifting of the river basin will occur at many regions particularly at critical sections. Overall, the findings of this study could be used further in river training works and in understanding the future dynamics of channel.

A Novel Adaptive Optimized Fast Blind Channel Estimation for Cyclic Prefix Assisted Space–Time Block Coded MIMO-OFDM Systems

Abstract: In this paper an adaptive optimized fast blind channel estimation using cyclic prefix supported with Space Time Block Coded Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (STBC-MIMO-OFDM) system is presented. The main aspire of our technique is to support multiple users at the same time over same frequency band based on the Multi-Carrier Code-Division Multiple Access (MC-CDMA) approach. High complexity and low convergence is the main obstacle in earlier blind channel estimation techniques. Modified flower pollination algorithm is implemented to overcome this problem. The MC-CDMA approach is utilized to implement the blind channel estimation. The proposed MC-CDMA is used to reduce the error rate included in the Blind Channel Estimation. As a part of wireless communications, time block coding technique is utilized to transmit several copies of information across the number of antennas. To develop the consistency of data transfer different received data is used and then MFPA results in lower fuel cost compared to FPA. MFPA produces better results compared with previous methods.

Rule Extraction from Neural Network Using Input Data Ranges Recursively

Abstract: Neural network is one of the best tools for data mining tasks due to its high accuracy. However, one of the drawbacks of neural network is its black box nature. This limitation makes neural network useless for many applications which require transparency in their decision-making process. Many algorithms have been proposed to overcome this drawback by extracting transparent rules from neural network, but still researchers are in search for algorithms that can generate more accurate and simple rules. Therefore, this paper proposes a rule extraction algorithm named Eclectic Rule Extraction from Neural Network Recursively (ERENNR), with the aim to generate simple and accurate rules. ERENNR algorithm extracts symbolic classification rules from a single-layer feed-forward neural network. The novelty of this algorithm lies in its procedure of analyzing the nodes of the network. It analyzes a hidden node based on data ranges of input attributes with respect to its output and analyzes an output node using logical combination of the outputs of hidden nodes with respect to output class. And finally it generates a rule set by proceeding in a backward direction starting from the output layer. For each rule in the set, it repeats the whole process of rule extraction if the rule satisfies certain criteria. The algorithm is validated with eleven benchmark datasets. Experimental results show that the generated rules are simple and accurate.