Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

Laskov, Pavel; Schäfer, Christin; Kotenko, Igor

doi:10.1515/piko.2004.228

Cited by 65 publications

(91 citation statements)

References 13 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the normal samples could be contained within a sphere in the projected data space. When the data space contains only positive values, this problem reduces to a special type of SVM called one-class quarter-sphere SVM [Laskov et al 2004], which is represented in Fig. 3.…”

Section: Anomaly Detection Techniquesmentioning

confidence: 99%

Detecting Malicious Data Injections in Wireless Sensor Networks

Illiano

Lupu

2015

ACM Comput. Surv.

View full text Add to dashboard Cite

Wireless Sensor Networks are widely advocated to monitor environmental parameters, structural integrity of the built environment and use of urban spaces, services and utilities. However, embedded sensors are vulnerable to compromise by external actors through malware but also through their wireless and physical interfaces. Compromised sensors can be made to report false measurements with the aim to produce inappropriate and potentially dangerous responses. Such malicious data injections can be particularly difficult to detect if multiple sensors have been compromised as they could emulate plausible sensor behaviour such as failures or detection of events where none occur. This survey reviews the related work on malicious data injection in wireless sensor networks, derives general principles and a classification of approaches within this domain, compares related studies and identifies areas that require further investigation.

show abstract

Section: Anomaly Detection Techniquesmentioning

confidence: 99%

Detecting Malicious Data Injections in Wireless Sensor Networks

Illiano

Lupu

2015

ACM Comput. Surv.

View full text Add to dashboard Cite

show abstract

“…In order to reduce high computational cost of the quadratic optimization, Campbell et al [23] have formulated a linear programming approach for the hyperplane-based SVM in [106], which is based on attracting the hyperplane towards the average of the distribution of mapped data vectors. Laskov et al [68] have extended work in [122] by proposing a quarter-sphere one-class SVM, which converts the quadratic optimization problem to a linear optimization problem by fitting a hypersphere centered at the origin, and thus reducing the effort of computational complexity of learning the normal boundary.…”

Section: Related Workmentioning

confidence: 99%

“…Rajasegarar et al [99] use the quarter-sphere one-class SVM proposed in [68] to present a distributed outlier detection technique for WSNs. They achieve approximately similar outlier detection results compared with a centralized approach.…”

Section: Related Workmentioning

confidence: 99%

“…Although we use the same quarter-sphere one-class SVM as presented in [68], our proposed outlier detection techniques simplify the process of modeling quartersphere SVM for WSNs and identify outliers in an online manner. They are also able to detect changes occurred in normal behavior of sensor data in real-time, which significantly reduces the detection delay.…”

Section: Related Workmentioning

confidence: 99%

“…In this chapter, we use quarter-sphere one-class SVM [68] approach to identify outliers in multivariate sensor data and resource-constrained WSNs. Our proposed outlier detection techniques are designed to operate in a distributed…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Observing the unobservable : distributed online outlier detection in wireless sensor networks

Zhang¹

View full text Add to dashboard Cite

This research was conducted within the EU projects e-SENSE and SENSEI. Copyright c 2010 by Yang Zhang, Enschede, The Netherlands. All rights reserved. No part of this book may be reproduced or transmitted, in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without the prior written permission of the author.Printed by Wöhrmann Print Service. AbstractThe generation of wireless sensor networks (WSNs) makes human beings observe and reason about the physical environment better, easier, and faster. The wireless sensor nodes equipped with sensing, processing, wireless communication and actuation capabilities can be densely deployed in a wide geographical area and measure various parameters continuously from the physical world. Compared with traditional environmental sensing technologies, such densely deployed WSNs enable collection of fine-grained high spatial and temporal resolution data with less installation, maintenance, and operation costs.However, raw sensor observations often have low data quality and reliability due to both internal and external factors including low quality of cheap sensors, dynamicity of network conditions, and harshness of the deployment environment. Use of low quality sensor data in any data analysis and decision making process will not only negatively impact analysis results and decisions made but also waste huge amount of valuable and limited network resources such as energy, as many incorrect values are transmitted. Low quality sensor data also prevents WSNs to fulfill their promises in terms of reliable real-time situation-awareness, as the low quality sensor data may generate large number of false alarms.Motivated by the need to improve quality of data analysis and decision making, enhance efficiency of using WSNs resources by preventing unnecessary transmission of erroneous sensor observations, and increase effectiveness of monitoring and situation-awareness capabilities of the WSNs, in this thesis we focus on online identification of outliers whenever and wherever they occur. Outliers in WSNs are those observations that represent erroneous values (errors) or indicate particular phenomenal changes (events). Our outlier detection techniques, which are based on distributed in-network data processing, identify sensor observations that do not conform to normal behavior of sensor data without using a pre-defined threshold or triggering conditions.Our main research objective is to design and implement effective and efficient outlier detection techniques for WSNs to identify outliers in an online and disv tributed manner and distinguish between errors and events with high accuracy and low false alarm, while maintaining the communication, computation and memory complexity low. Main contributions of this thesis can be summarized as: 3. Statistical-Based outlier detection techniques for WSNs. We take two approaches in designing our outlier detection techniques. One approach originates fr...

show abstract

Learning Intrusion Detection: Supervised or Unsupervised?

Laskov¹,

Düssel²,

Schäfer³

et al. 2005

Lecture Notes in Computer Science

180

113

View full text Add to dashboard Cite

Abstract. Application and development of specialized machine learning techniques is gaining increasing attention in the intrusion detection community. A variety of learning techniques proposed for different intrusion detection problems can be roughly classified into two broad categories: supervised (classification) and unsupervised (anomaly detection and clustering). In this contribution we develop an experimental framework for comparative analysis of both kinds of learning techniques. In our framework we cast unsupervised techniques into a special case of classification, for which training and model selection can be performed by means of ROC analysis. We then investigate both kinds of learning techniques with respect to their detection accuracy and ability to detect unknown attacks.

show abstract

Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

Cited by 65 publications

References 13 publications

Detecting Malicious Data Injections in Wireless Sensor Networks

Detecting Malicious Data Injections in Wireless Sensor Networks

Observing the unobservable : distributed online outlier detection in wireless sensor networks

Learning Intrusion Detection: Supervised or Unsupervised?

Contact Info

Product

Resources

About