An Implementation of Intrusion Detection System Using Genetic Algorithm (1204.1336v1)

Abstract: Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.

• The paper demonstrates that using genetic algorithms in IDS improves detection rates for DoS and U2R attacks.
• It employs a two-phase methodology, precalculating chromosomes from training data and refining predictions via evolutionary operations.
• The system achieved a 95% overall detection rate, though a 30.46% false positive rate highlights a need for further optimization.

An Implementation of Intrusion Detection System Using Genetic Algorithm

The paper "An Implementation of Intrusion Detection System Using Genetic Algorithm" authored by Mohammad Sazzadul Hoque, Md. Abdul Mukit, and Md. Abu Naser Bikas, presents a paper on enhancing Intrusion Detection Systems (IDS) leveraging Genetic Algorithms (GA). The research utilizes GA to efficiently identify various types of network intrusions, seeking to address the deficiencies in existing IDS solutions.

Overview of the Proposed Intrusion Detection System

The paper begins by acknowledging the persistent threat posed by cyber intrusions and the limitations of current IDS technologies, which necessitate ongoing enhancements. The authors propose an IDS that incorporates genetic algorithms, aiming to improve detection rates while reducing false positives.

Key to this approach, GAs are utilized to evolve a population of candidate solutions against known intrusion patterns derived from training data. This technique mirrors biological evolution, where potential solutions undergo selection, crossover, and mutation to optimize the detection capabilities of the IDS.

Methodology

The research involved two main phases in the system design: the precalculation phase and the detection phase. During the precalculation phase, a set of chromosomes representing intrusion signatures is generated from training data drawn from the KDD99 dataset, a standard benchmark for evaluating IDS systems. In the detection phase, incoming network data is analyzed using these precomputed chromosomes, with genetic operations applied iteratively to determine the type of network traffic accurately.

• Precalculation Phase: Chromosomes are initialized and adjusted according to training data, where closeness within a predefined range necessitates merging, otherwise new chromosomes are created.
• Detection Phase: A population is formed for each test data instance, evaluated against the precomputed chromosomes. Iterative evolution occurring through selection, crossover, and mutation refines predictions toward the actual data classification.

Performance Evaluation

The evaluation of the proposed system on the KDD99 benchmark dataset reported a varied performance across attack types. Detection rates for DoS and U2R attacks were notably high at 99.4% and 92.0%, respectively, outperforming certain benchmarks from previous models. However, challenges were observed in correctly identifying Normal data and R2L attacks, which is attributed to the exclusion of non-numerical features in the dataset for simplicity.

The overall detection rate stood at 95.00%, with a false positive rate of 30.46%. Such quantitative evaluations emphasize the IDS's strength in handling DoS and U2R attacks, while also pointing to an area of potential improvement concerning false positives, which remains a common challenge across IDS methodologies.

Implications and Future Directions

This research contributes to the field of network security by demonstrating the utility of genetic algorithms in enhancing IDS performance. The ability to dynamically adapt to new and varied intrusion patterns offers a practical advantage over rigid rule-based systems. However, the high false positive rate indicates the need for further refinement, possibly through incorporating more sophisticated heuristics or data features.

Future developments could involve coupling the GA-based approach with machine learning models to form a hybrid system, increasing robustness against evolving threats. Additionally, integrating statistical analysis could enhance the detection of nuanced patterns missed by traditional systems. Improved algorithms for fitness evaluations within the GA process could also substantially reduce false positives, further enhancing the system's reliability.

This paper underscores the potential of genetic algorithms to enrich IDS technologies, providing a framework for future research aimed at addressing contemporary network security challenges with evolutionary computational methodologies.