Volume 9, Issue 2, February 2017

 

 

Security and Privacy in Big Data
 

Security and Privacy in Big Data
Pages: 24-29 (6) | [Full Text] PDF (265 KB)
MS Al-kahtani
Dept. of Computer Engg., Prince Sattam bin Abdulaziz University, Saudi Arabia

Abstract - Providing security and privacy in big data analytics is significantly important along with providing quality of services (QoS) in big data networks. This paper presents the current state-of-the-art research challenges and possible solutions on big data network security. More specifically, we present network security approaches (i.e., intrusion detection, network threat monitoring systems), classify and compare threats and their defense mechanisms that help mitigate the network vulnerabilities from big data and software defined networks (SDN).
 
Index Terms - Big Data, MapReduce, Hadoop, SDN, Intrusion Detection, Vulnerabilities, Threats

Citation - MS Al-kahtani . "Security and Privacy in Big Data." International Journal of Computer Engineering and Information Technology 9, no. 2 (2017): 24-29.
A Novel Real-time Human Activity Based Anomaly Detection Model Using Graph Based Clustering and Classification Model
 

A Novel Real-time Human Activity Based Anomaly Detection Model Using Graph Based Clustering and Classification Model

Pages: 30-36 (7) | [Full Text] PDF (469 KB)
D Kishore, MC Mohan, AA Rao
Asst.Prof, Department of Computer Science & Engineering, Andhrapradesh, India
Professor, Department of Computer Science & Engineering, JNTUH, Hyderabad, Telangana State, India
Professor in CSE, Director,Academic & Planning, JNTUA,anantapuramu, Andhrapradesh, India

Abstract - Detecting online abnormality in the video surveillance is a challenging issue due to streaming, video noise, outliers and resolution. Traditional trajectory based anomaly detection model which analyzes the video training patterns for anomaly detection. This paper aims to solve the problem of video noise and anomaly detection .In this paper, a novel filtered based ensemble clustering and classification model was implemented using the threshold based method, graph based clustering algorithm and classification model. Experimental results proved that the proposed model has high computation detection rate compared to traditional real-time anomaly detection models.
 
Index Terms - Anomaly Detection, Video Anomaly, Graph Based Clustering Model

Citation - D Kishore, MC Mohan, AA Rao. "A Novel Real-time Human Activity Based Anomaly Detection Model Using Graph Based Clustering and Classification Model ." International Journal of Computer Engineering and Information Technology 9, no. 2 (2017): 30-36.
Text Parsing with Markov Logic Network
 

Text Parsing with Markov Logic Network
Pages: 37-40 (4) | [Full Text] PDF (554 KB)
N Wang
University of Michigan, 599 N Mathilda Ave, Sunnyvale, CA 94085, USA

Abstract - This document describes a novel way to extract structure information from plain text using Markov Decision Process. In the age of big data, unstructured information such as text, photos and videos becomes abundant. However, data warehouse requires structured data with well-defined schema. It has been a challenge for the computer science community to extract useful data under strict schema from unstructured data schema. Here we proposed an automated system that is able to understand and infer the most likely counterpart in text stream that corresponds to a filed under the requested schema. The designed algorithm formulated the plain text using context dependent grammar with various weights, which would be sued to decide which field of the structured schema a particular piece of unstructured data belongs to. A machine-learning algorithm is used to learn the weights from training data. We implemented this automated system and applied it to extract schema data from plain US bankruptcy petition forms.
 
Index Terms - Information Retrieval, Markov Logic Network, Regular Expressions, Big Data

Citation - N Wang. "Text Parsing with Markov Logic Network." International Journal of Computer Engineering and Information Technology 9, no. 2 (2017): 37-40.