Intrusion Detection System using MLP and Chaotic Neural Networks

Shettar, Pooja; Kachavimath, Amit V; Mulla, Mohammed Moin; Narayan, D. G.; Hanchinmani, Gururaj

doi:10.1109/iccci50826.2021.9457024

Cited by 20 publications

(6 citation statements)

References 7 publications

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“…The results showed that all the evaluated models reached high F1-scores. For example the model proposed in [3,15] reached respectively 0.998 and 0.99, the two benchmark models described in [8,17] achieved respectively, 0.996 and 0.989. As a matter of fact, the high performance of all these models indicates either the triviality of the intrusion detection task, which is clearly not true, or an inappropriate evaluation procedure.…”

Section: Resultsmentioning

confidence: 99%

Efficient Network Representation for GNN-Based Intrusion Detection

Friji

Olivereau

Sarkiss

2023

Lecture Notes in Computer Science

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The last decades have seen a growth in the number of cyberattacks with severe economic and privacy damages, which reveals the need for network intrusion detection approaches to assist in preventing cyber-attacks and reducing their risks. In this work, we propose a novel network representation as a graph of flows that aims to provide relevant topological information for the intrusion detection task, such as malicious behavior patterns, the relation between phases of multi-step attacks, and the relation between spoofed and pre-spoofed attackers' activities. In addition, we present a Graph Neural Network (GNN) based-framework responsible for exploiting the proposed graph structure to classify communication flows by assigning them a maliciousness score. The framework comprises three main steps that aim to embed nodes' features and learn relevant attack patterns from the network representation. Finally, we highlight a potential data leakage issue with classical evaluation procedures and suggest a solution to ensure a reliable validation of intrusion detection systems' performance. We implement the proposed framework and prove that exploiting the flow-based graph structure outperforms the classical machine learning-based and the previous GNN-based solutions.

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Section: Resultsmentioning

confidence: 99%

Efficient Network Representation for GNN-Based Intrusion Detection

Friji

Olivereau

Sarkiss

2023

Lecture Notes in Computer Science

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“…The performance metrics used in this study are accuracy, precision, recall, and f1-Score, based on the research from [7], [8], [9], [10], [11], and [12]. Accuracy is the percentage of samples that are correctly classified above the total number of samples.…”

Section: Performance Metricsmentioning

confidence: 99%

“…The metrics consist of accuracy, precision, FPR, and FNR. The result is that the hybrid value from MLP and Chaotic Neural Network has higher accuracy (99.21% and 94.8%) and precision (99.91% and 90.3%), and lower FPR (0.00401 and 0.00478) and FNR (0.00213 and 0.00233) from MLP itself [12].…”

Section: Introductionmentioning

confidence: 98%

Implementation of CNN-MLP and CNN-LSTM for MitM Attack Detection System

Satyanegara

Ramli

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Man in the Middle (MitM) is one of the attack techniques conducted for eavesdropping on data transitions or conversations between users in some systems secretly. It has a sizeable impact because it could make the attackers will do another attack, such as website or system deface or phishing. Deep Learning could be able to predict various data well. Hence, in this study, we would like to present the approach to detect MitM attacks and process its data, by implementing hybrid deep learning methods. We used 2 (two) combinations of the Deep Learning methods, which are CNN-MLP and CNN-LSTM. We also used various Feature Scaling methods before building the model and will determine the better hybrid deep learning methods for detecting MitM attack, as well as the feature selection methods that could generate the highest accuracy. Kitsune Network Attack Dataset (ARP MitM Ettercap) is the dataset used in this study. The results prove that CNN-MLP has better results than CNN-LSTM on average, which has the accuracy rate respectively at 99.74%, 99.67%, and 99.57%, and using Standard Scaler has the highest accuracy (99.74%) among other scenarios.

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“…Conventional approaches frequently find it difficult to keep up with the constantly shifting characteristics of cyber threats, particularly in the ever-changing financial industry. The article presents a novel framework that combines the discriminative strength of MLP [9] networks with the feature learning abilities of auto encoders in order to handle this difficulty. Our goal is to increase detection accuracy by utilizing labelled information and capturing intricate patterns in the data through the integration of these two methods into a hybrid model.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Threat Detection in Financial Cyber Security Through Auto Encoder-MLP Hybrid Models

Almahadeen,

ALMahadin,

Santosh

et al. 2024

IJACSA

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Cyber-attacks have the potential to cause power outages, malfunctions with military equipment, and breaches of sensitive data. Owing to the substantial financial value of the information it contains, the banking sector is especially vulnerable. The number of digital footprints that banks have increases, increasing the attack surface available to hackers. This paper presents a unique approach to improve financial cyber security threat detection by integrating Auto Encoder-Multilayer Perceptron (AE-MLP) hybrid models. These models use MLP neural networks' discriminative capabilities for detection tasks, while also utilizing auto encoders' strengths in collecting complex patterns and abnormalities in financial data. The NSL-KDD dataset, which is varied and includes transaction records, user activity patterns, and network traffic, was thoroughly analysed. The results show that the AE-MLP hybrid models perform well in spotting possible risks including fraud, data breaches, and unauthorized access attempts. Auto encoders improve the accuracy of threat detection methods by efficiently compressing and rebuilding complicated data representations. This makes it easier to extract latent characteristics that are essential for differentiating between normal and abnormal activity. The approach is implemented with Python software. The recommended Hybrid AE+MLP approach shows better accuracy with 99%, which is 13.16% more sophisticated, when compared to traditional approach. The suggested approach improves financial cyber security systems' capacity for prediction while also providing scalability and efficiency while handling massive amounts of data in real-time settings.

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Intrusion Detection System using MLP and Chaotic Neural Networks

Cited by 20 publications

References 7 publications

Efficient Network Representation for GNN-Based Intrusion Detection

Efficient Network Representation for GNN-Based Intrusion Detection

Implementation of CNN-MLP and CNN-LSTM for MitM Attack Detection System

Enhancing Threat Detection in Financial Cyber Security Through Auto Encoder-MLP Hybrid Models

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