Anshul Mahajan ¹, Arvinder Kaur ¹

Affiliations
1 Department of Computer Science and Engineering, Chandigarh University, Mohali -140413, Punjab, IN

Abstract

Objectives: Intelligent Transportation System (ITS) is raising the interests of the research community in the field of communication. The main objectives of this work are to implement ITS scenario and evaluate the Traffic Density Estimation for different cities for checking the density of vehicles on the different roads and perform a comparative evaluation of traffic densities in the different number of city based scenarios and to increase the accuracy of the density prediction technique for ITS. Method/Statistical Analysis: In this manner, an advanced density prediction technique has been proposed for the prediction of density on the road. The proposed technique is based on the number of vehicles on the road, in which vehicle on the map of the three cities at a particular instance of time has been estimated. After estimating the density of vehicle, prediction of that density has been done which shows that there is a maximum density on the lane and in future density on the road has been predicted. Finding: A range of values has been generated with the help of a dedicated simulation scenario using SUMO simulator. The computed values have been used for estimating the density of vehicles in Chandigarh, Delhi and Kolkata based on road side scenarios using Poisson distribution for randomly generated of vehicles and the obtained values has been evaluated to compare the densities of vehicles for these three cities. Improvement: When we are talking about traffic, then the term comes in our mind is congestion. To avoid that congestion on the roads, we use the Poisson distribution in which vehicle generates after every instance of time. With the help of this distribution, the amount of congestion on the roads can be decreased and this technique is very effective on the hurdle like jam.

Keywords

Density Estimation, ITS, Linear Regression, Poisson Distribution, R Tool, SUMO Simulator, Vehicular Bigdata.

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Simarleen Kaur ¹, Arvinder Kaur ¹

Affiliations
1 Computer Science and Engineering Department Chandigarh University, Mohali -140413, Punjab, IN

Abstract

Background/Objectives: Malware is progressing at a faster pace so the identification of malware is a vital area in modernized world where information technology is rapidly emerging. This paper emphasizes on enhancement of performance parameters for malware detection of source code clones using proposed clone detection algorithm. Methods/ Statistical Analysis: The approaches defined by researchers didn’t consider data types, variables while clone detection. To fulfill the goal of proposed work, malware detection of clone clone and achieve better results the approach adopted is implementation of a clone detection algorithm ‘String Pattern Back Propagation Neural Network’ to determine the code clones and matching them with malware signatures in the repository for computation of performance parameters. Findings: The identification of malware is proceeded by utilizing java projects having different window size (20,40). The source code files are put into modularization phase to extract functions from different classes. Code clones are determined by applying the implemented algorithm for the evaluation of malware signatures. It was observed that employed approach results into better performance with high accuracy of 96.97% and hence, the approach developed proved to be deterministic and efficient. The paper provides an overview of state of the art and focuses on enhanced performance in terms of precision, recall and F-measure in case of Java language where the data types, variables, comments in the application are also given priority to detect code clones as compared to existing research malware binaries for achieving better performance. Applications/ Improvements: To handle the tremendous range of malicious code, the approach can be applied in varied multiple languages to detect the number of clones in an application or a system and achieve greater outcomes.

Keywords

Code Clone, Clone Detection Algorithm, Malware, Malware Analysis, Reverse Engineering.

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