IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Introduction to algorithms
Instance-Based Learning Algorithms
Machine Learning
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
An Architecture for Intrusion Detection Using Autonomous Agents
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Machine learning techniques for the computer security domain of anomaly detection
Machine learning techniques for the computer security domain of anomaly detection
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Intrusion detection using sequences of system calls
Journal of Computer Security
Towards NIC-based intrusion detection
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Design and application of hybrid intelligent systems
User re-authentication via mouse movements
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
A Services Oriented Framework for Next Generation Data Analysis Centers
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 10 - Volume 11
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Data Mining and Knowledge Discovery
Computational aspects of mining maximal frequent patterns
Theoretical Computer Science
Adaptive anomaly detection with evolving connectionist systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Adaptive real-time anomaly detection with incremental clustering
Information Security Tech. Report
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
A new intrusion detection system using support vector machines and hierarchical clustering
The VLDB Journal — The International Journal on Very Large Data Bases
Answering form-based web queries using the data-mining approach
Journal of Intelligent Information Systems
Fast mining of distance-based outliers in high-dimensional datasets
Data Mining and Knowledge Discovery
Fast detection of database system abuse behaviors based on data mining approach
Proceedings of the 2nd international conference on Scalable information systems
Projected outlier detection in high-dimensional mixed-attributes data set
Expert Systems with Applications: An International Journal
Parameterless outlier detection in data streams
Proceedings of the 2009 ACM symposium on Applied Computing
A Multi-resolution Approach for Atypical Behaviour Mining
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
ACM Computing Surveys (CSUR)
The Needles-in-Haystack Problem
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Content-based methodology for anomaly detection on the web
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
A hybrid fraud scoring and spike detection technique in streaming data
Intelligent Data Analysis
Atypicity detection in data streams: A self-adjusting approach
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Online outlier detection for data streams
Proceedings of the 15th Symposium on International Database Engineering & Applications
Estimating accuracy of mobile-masquerader detection using worst-case and best-case scenario
ICICS'06 Proceedings of the 8th international conference on Information and Communications Security
An efficient SVM-Based method to detect malicious attacks for web servers
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
Network anomaly detection based on clustering of sequence patterns
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Classification of hidden network streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Intrusion detection via analysis and modelling of user commands
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Using boosting learning method for intrusion detection
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
ADWICE – anomaly detection with real-time incremental clustering
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Effective next-items recommendation via personalized sequential pattern mining
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Fmeter: extracting indexable low-level system signatures by counting kernel function calls
Proceedings of the 13th International Middleware Conference
S2MP: similarity measure for sequential patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
A methodological overview on anomaly detection
DataTraffic Monitoring and Analysis
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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Security of computer systems is essential to their acceptance and utility. Computer security analysts use intrusion detection systems to assist them in maintaining computer system security. This paper deals with the problem of differentiating between masqueraders and the true user of a computer terminal. Prior efficient solutions are less suited to real time application, often requiring all training data to be labeled, and do not inherently provide an intuitive idea of what the data model means. Our system, called ADMIT, relaxes these constraints, by creating user profiles using semi-incremental techniques. It is a real-time intrusion detection system with host-based data collection and processing. Our method also suggests ideas for dealing with concept drift and affords a detection rate as high as 80.3% and a false positive rate as low as 15.3%.