Informatics Publishing Limited

K. Poongodi ¹, B. Rosiline Jeetha ²

Affiliations
1 Bharathiar University, Coimbatore, IN
2 School of Computer Studies (PG), RVS College of Arts and Science, Coimbatore, IN

Abstract

This study introduces and describes a novel Intrusion Detection System (IDS) called MOVCIDS (Mobile Visualization Connectionist IDS). By its advanced visualization facilities, the proposed IDS allows providing an overview of the network traffic as well as identifying anomalous situations tackled by computer networks, responding to the challenges presented by volume, dynamics and diversity of the traffic, including novel (0-day) attacks. MOVCIDS splits massive traffic data into segments and analyze them, thereby providing administrators with an intuitive snapshot to analyze the kinds of events taking place on the computer network. IDS have been probed through several real anomalous situations related to the Simple Network Management Protocol as it is potentially dangerous. GICAP-IDS dataset is used to protect from the external attacks. The main experimental study of MOVCIDS makes use of this dataset.

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T. Bhuvaneshwari ¹, K. Poongodi ¹

Affiliations
1 Department of Computer Science and Engineering, K.S. Rangasamy College of Technology, Tamilnadu, IN

Abstract

Accurate detection of moving objects is an important precursor to stable tracking or recognition. The algorithm used here aims to segment the foreground objects in live video when background scene textures change over time. To accommodate interactions among neighboring pixels, a global online learning algorithm is then derived that explicitly distinguishes objects versus background. The average deviation method is used in order to subtract the object from the live video. Formulate background subtraction as minimizing a penalized instantaneous risk functional yielding a local online discriminative algorithm that can quickly adapt to temporal changes. In the context of background subtraction, we achieve better, more accurate segmentation than competing methods using a model whose complexity grows with the underlying complexity of the scene, rather than the amount of time required to observe all aspect of textures. By proposed method, develop an implementation that can run efficiently on the highly parallel graphics processing unit, thus yielding good processing speed.

Keywords

Background Subtraction, Online Learning with Kernels, Segment Foreground Objects in Live Video.

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