Outputs (2122)

Detecting Botnets Through Log Correlation
Presentation / Conference Contribution
Al-Hammadi, Y., & Aickelin, U. Detecting Botnets Through Log Correlation. Presented at Proceedings of the Workshop on Monitoring, Attack Detection and Mitigation (MonAM 2006)

Botnets, which consist of thousands of compromised machines, can cause a significant threat to other systems by launching Distributed Denial of Service attacks, keylogging, and backdoors. In response to this threat, new effective techniques are neede... Read More about Detecting Botnets Through Log Correlation.

Dempster-Shafer for Anomaly Detection
Presentation / Conference Contribution
Chen, Q., & Aickelin, U. Dempster-Shafer for Anomaly Detection. Presented at Proceedings of the International Conference on Data Mining (DMIN 2006)

In this paper, we implement an anomaly detection system using the Dempster-Shafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We furt... Read More about Dempster-Shafer for Anomaly Detection.

Modelling Immunological Memory
Book Chapter
Garrett, S., Robbins, M., Walker, J., Wilson, W., & Aickelin, U. Modelling Immunological Memory. In D. Flower, & J. Timmis (Eds.), Silico Immunology. Springer

Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an... Read More about Modelling Immunological Memory.

Dendritic Cells for Real-Time Anomaly Detection
Presentation / Conference Contribution
Greensmith, J., & Aickelin, U. Dendritic Cells for Real-Time Anomaly Detection. Presented at Proceedings of the Workshop on Artificial Immune Systems and Immune System Modelling (AISB 2006)

Dendritic Cells (DCs) are innate immune system cells which have the power to activate or suppress the immune system. The behaviour of human DCs is abstracted to form an algorithm suitable for anomaly detection. We test this algorithm on the real-time... Read More about Dendritic Cells for Real-Time Anomaly Detection.

Articulation and Clarification of the Dendric Cell Algorithm
Presentation / Conference Contribution
Greensmith, J., Aickelin, U., & Twycross, J. Articulation and Clarification of the Dendric Cell Algorithm. Presented at Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS 2006)

The Dendritic Cell algorithm (DCA) is inspired by recent
work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of compute... Read More about Articulation and Clarification of the Dendric Cell Algorithm.

Dendritic Cells for Anomaly Detection
Presentation / Conference Contribution
Greensmith, J., Twycross, J., & Aickelin, U. Dendritic Cells for Anomaly Detection. Presented at Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006)

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop
an intrusion detection system based on a novel concept in
immunology, the Dan... Read More about Dendritic Cells for Anomaly Detection.

Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling
Book Chapter
Li, J., & Aickelin, U. Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling. In M. Pelikan, K. Sastry, & E. Cantu-Paz (Eds.), Algorithms to Applications (Studies in Computational Intelligence). Springer

Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such su... Read More about Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling.

A Component Based Heuristic Search Method with AdaptivePerturbations for Hospital Personnel Scheduling
Journal Article
Li, J., Aickelin, U., & Burke, E. A Component Based Heuristic Search Method with AdaptivePerturbations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with adaptive perturbations, for a nurse scheduling problem arising at a major UK... Read More about A Component Based Heuristic Search Method with AdaptivePerturbations for Hospital Personnel Scheduling.

An Immune Network Intrusion Detection System Utilising Correlation Context
Presentation / Conference Contribution
Tedesco, G., & Aickelin, U. An Immune Network Intrusion Detection System Utilising Correlation Context. Presented at Proceedings of the Workshop on Artificial Immune Systems and Immume System Modelling (AISB 2006)

Network Intrusion Detection Systems (NIDS) are computer systems which monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDSs rely on... Read More about An Immune Network Intrusion Detection System Utilising Correlation Context.

Integrating Innate and Adaptive Immunity for Intrusion Detection
Presentation / Conference Contribution
Tedesco, G., Twycross, J., & Aickelin, U. Integrating Innate and Adaptive Immunity for Intrusion Detection. Presented at Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS 2006)

Network Intrusion Detection Systems (NIDS) monitor a net-
work with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS’s rely on having access to a data... Read More about Integrating Innate and Adaptive Immunity for Intrusion Detection.