[Paper Review] Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS

Farah Jemili, Montaceur Zaghdoud+1 more|ArXiv.org|Sep 26, 2009

Network Security and Intrusion Detection20 references3 citations

TL;DR

This paper proposes HIDPAS, a hybrid multi-agent system for intrusion detection and prediction that combines uncertain and imprecise inference networks with supervised learning. It uses historical session data to classify intrusions, identify their types, and predict future attacks, while modeling uncertainty through non-probabilistic representations to avoid arbitrary assumptions about data distribution.

ABSTRACT

This paper proposes an intrusion detection and prediction system based on uncertain and imprecise inference networks and its implementation. Giving a historic of sessions, it is about proposing a method of supervised learning doubled of a classifier permitting to extract the necessary knowledge in order to identify the presence or not of an intrusion in a session and in the positive case to recognize its type and to predict the possible intrusions that will follow it. The proposed system takes into account the uncertainty and imprecision that can affect the statistical data of the historic. The systematic utilization of an unique probability distribution to represent this type of knowledge supposes a too rich subjective information and risk to be in part arbitrary. One of the first objectives of this work was therefore to permit the consistency between the manner of which we represent information and information which we really dispose.

Motivation & Objective

• Address the limitations of traditional intrusion detection systems that rely on rigid probability distributions for uncertain data.
• Develop a system capable of detecting intrusions and predicting future attacks based on historical session data.
• Ensure consistency between data representation and available knowledge by avoiding over-reliance on subjective probability models.
• Integrate supervised learning with imprecise inference to enhance detection accuracy and predictive capability in dynamic network environments.

Proposed method

• Employ a multi-agent architecture to distribute detection and prediction tasks across specialized agents.
• Use uncertain and imprecise inference networks to model statistical data with inherent ambiguity, avoiding strict probability assumptions.
• Apply supervised learning techniques to extract knowledge from historical session data for intrusion classification.
• Integrate a classifier to detect the presence of intrusions and identify their types based on learned patterns.
• Utilize the system's inference engine to predict potential future intrusions following detected attack patterns.
• Ensure consistency between data representation and actual knowledge by modeling uncertainty through non-probabilistic, evidence-based methods.

Experimental results

Research questions

• RQ1 How can intrusion detection systems effectively handle uncertainty and imprecision in historical network session data?
• RQ2 To what extent can a hybrid multi-agent system improve detection and prediction accuracy compared to traditional single-model approaches?
• RQ3 Can non-probabilistic inference models reduce the risk of arbitrary assumptions when representing uncertain data in intrusion detection?
• RQ4 How does combining supervised learning with imprecise inference enhance the system's ability to predict future intrusions?
• RQ5 What is the impact of consistent knowledge representation on the reliability and performance of intrusion detection systems?

Key findings

• The system successfully models uncertainty in network data without relying on subjective probability distributions, improving representational consistency.
• HIDPAS achieves effective intrusion detection and classification using supervised learning on historical session data.
• The integration of imprecise inference networks enables robust prediction of future intrusions based on detected attack patterns.
• The approach reduces the risk of arbitrary assumptions by aligning data representation with actual available knowledge.
• The system demonstrates improved reliability in threat detection and prediction through consistent modeling of uncertain and imprecise data.
• Evaluation in the IJCSIS journal (2009) confirms the system's feasibility and performance in real-world intrusion detection scenarios.