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Explainable deep neural network-based analysis on intrusion-detection systems

Pande Sagar Dhanraj, Khamparia Aditya

Computer Science

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2023

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T. 24 (1)

97--111

EN

The research on intrusion-detection systems (IDSs) has been increasing in recent years. Particularly, this research widely utilizes machine-learning concepts, and it has proven that these concepts are effective with IDSs – particularly, deep neural network-based models have enhanced the rates of the detection of IDSs. In the same instance, these models are turning out to be very complex, and users are unable to track down explanations for the decisions that are made; this indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, this article deals with a proposed model that can explain the obtained predictions. The proposed framework is a combination of a conventional IDS with the aid of a deep neural network and the interpretability of the model predictions. The proposed model utilizes Shapley additive explanations (SHAPs) that mixes the local explainability as well as the global explainability for the enhancement of interpretations in the case of IDS. The proposed model was implemented by using popular data sets (NSL-KDD and UNSW-NB15), and the performance of the framework was evaluated by using their accuracy. The framework achieved accuracy levels of 99.99 and 99.96%, respectively. The proposed framework can identify the top-4 features using local explainability and the top-20 features using global explainability.

2

An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection

Brunner Csaba, Kő Andrea, Fodor Szabina

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 2

149--163

EN

Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.

3

Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks

Kalnoor Gauri, Gowrishankar

Journal of Telecommunications and Information Technology

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2021

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nr 3

42--49

EN

In this paper, a new reinforcement learning intrusion detection system is developed for IoT networks incorporated with WSNs. A research is carried out and the proposed model RL-IDS plot is shown, where the detection rate is improved. The outcome shows a decrease in false alarm rates and is compared with the current methodologies. Computational analysis is performed, and then the results are compared with the current methodologies, i.e. distributed denial of service (DDoS) attack. The performance of the network is estimated based on security and other metrics.

4

Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS

Shakeela Shaikh, Sai Shankar N., Mohan Reddy P., Kavya Tulasi T., Mahesh Koneru M.

International Journal of Electronics and Telecommunications

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2021

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Vol. 67, No. 2

267--275

EN

Cyber-attacks are increasing day by day. The generation of data by the population of the world is immensely escalated. The advancements in technology, are intern leading to more chances of vulnerabilities to individual’s personal data. Across the world it became a very big challenge to bring down the threats to data security. These threats are not only targeting the user data and also destroying the whole network infrastructure in the local or global level, the attacks could be hardware or software. Central objective of this paper is to design an intrusion detection system using ensemble learning specifically Decision Trees with distinctive feature selection univariate ANOVA-F test. Decision Trees has been the most popular among ensemble learning methods and it also outperforms among the other classification algorithm in various aspects. With the essence of different feature selection techniques, the performance found to be increased more, and the detection outcome will be less prone to false classification. Analysis of Variance (ANOVA) with F-statistics computations could be a reasonable criterion to choose distinctives features in the given network traffic data. The mentioned technique is applied and tested on NSL KDD network dataset. Various performance measures like accuracy, precision, F-score and Cross Validation curve have drawn to justify the ability of the method.