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Poonam Ghuli
Department of CSE, RVCE

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Anomaly Detection in Log Records Poonam Ghuli
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 1: April 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v10.i1.pp343-347

Abstract

Complex software systems are continuously generating application and server logs for the events which had occurred in the past. These logs generated and can be utilized for anomaly and intrusion detection. These log files can be used for anomaly or outlier detection. Certain types of abnormalities or exceptions such as spikes in HTTP requests, number of exceptions raised in logs, etc. All these events are logged into the log files for further analysis. These types of events are generally used for predicting the anomalies in future. The developed prototype assumes that the user inputs log records in a standard apache log format. At first the user uploads the log file for outlier detection. Next, a prototype is developed to get the number of HTTP requests for outlier detection. Then anomalies in number of HTTP requests are detected using three techniques namely InterQuartileRange method, Moving averages and Median Absolute deviation. Once the outliers are detected, these outliers are removed from the current dataset. This output is given as input to the Multilayer Perceptron model to predict the number of HTTP requests at the next timestamp 
Development of framework for detecting smoking scene in video clips Poonam Ghuli; Shashank B N; Athri G Rao
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp22-26

Abstract

According to Global Adult Tobacco Survey 2016-17, 61.9% of people quitting tobacco the reason was the warnings displayed on the product covers. The focus of this paper is to automatically display warning messages in video clips. This paper explains the development of a system to automatically detect the smoking scenes using image recognition approach in video clips and then add the warning message to the viewer.  The approach aims to detect the cigarette object using Tensorflow’s object detection API. Tensorflow is an open source software library for machine learning provided by Google which is broadly used in the field image recognition. At present, Faster R-CNN with Inception ResNet is theTensorflow’s slowest but most accurate model. Faster R-CNN with Inception Resnet v2 model is used to detect smoking scenes by training the model with cigarette as an object.
Co-Authors Athri G Rao Shashank B N