An adaptive distributed Intrusion detection system architecture using multi agents

Riyad, A M; Ahmed, Mohiuddin; Khan, R.L. Raheemaa

doi:10.11591/ijece.v9i6.pp4951-4960

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Distributed intrusion detection using mobile agents is discussed in [14]. Each mobile agent analyses the traffic and detects the threats independently.…”

Section: Deep Learning-based Intrusion Detectionmentioning

confidence: 99%

A Multi-Agent Adaptive Deep Learning Framework for Online Intrusion Detection

Mahdi¹,

Khajavi²,

Siavoshani³

et al. 2023

Preprint

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The network security analyzers use intrusion detection systems (IDSes) to distinguish malicious traffic from benign ones. The deep learning-based (DL-based) IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction process. However, this new generation of IDSes still suffers from a number of challenges. One of the main issues of an IDS is facing traffic concept drift which manifests itself as new (i.e., zero-day) attacks, in addition to the changing behavior of benign users/applications. Furthermore, a practical DL-based IDS needs to be conformed to a distributed architecture to handle big data challenges.In this paper, we propose a framework for adapting DL-based models to the changing attack/benign traffic behaviors, considering a more practical scenario (i.e., online adaptable IDSes). This framework employs continual deep anomaly detectors in addition to the federated learning approach to solve the above-mentioned challenges. Furthermore, the proposed framework implements sequential packet labeling for each flow, which provides an attack probability score for the flow by gradually observing each flow packet and updating its estimation. We evaluate the proposed framework by employing different deep models (including CNN-based and LSTM-based) over the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. Through extensive evaluations and experiments, we show that the proposed distributed framework is well adapted to the traffic concept drift. More precisely, our results indicate that the CNN-based models are well suited for continually adapting to the traffic concept drift (i.e., achieving an average detection rate of above 95% while needing just 128 new flows for the updating phase), and the LSTM-based models are a good candidate for sequential packet labeling in practical online IDSes (i.e., detecting intrusions by just observing their first 15 packets).

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“…Distributed intrusion detection using mobile agents is discussed in [14]. Each mobile agent analyses the traffic and detects the threats independently.…”

Section: Deep Learning-based Intrusion Detectionmentioning

confidence: 99%

A Multi-Agent Adaptive Deep Learning Framework for Online Intrusion Detection

Mahdi¹,

Khajavi²,

Siavoshani³

et al. 2023

Preprint

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“…Approach Dataset Accuracy [26] Naïve Bayes KDD99 0.950 [27] K-Means and ANN KDD99 0.975 [28] Convolutional neural network KDD99 0.9984 [29] Meanshift clustering algorithm KDD99 0.8120 [30] Multi-agent system KDD99 0.9582 AID4I Auto-Selected Model: CatBoost KDD99 0.9998 [31] C4.5, Naïve Bayes, Random Forest NSL-KDD 0.9965 [32] k-NN, K-Means NSL-KDD 0.9943 [33] Random forest NSL-KDD 0.9870 AID4I Auto-Selected Model: XGBoost NSL-KDD 0.9993 [34] Decision Tree, Naïve Bayes, ANN, Logistic Regression, EM Clustering UNSW-NB15 0.8556 [35] MSCNN-LSTM UNSW-NB15 0.9560 [8] Fuzzy C-means UNSW-NB15 0.9890 AID4I Auto-selected model: Decision tree UNSW-NB15 0.9995…”

Section: Studymentioning

confidence: 99%

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

Sezgin,

Boyacı

2023

Computers, Materials &Amp; Continua

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By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our groundbreaking architecture, known as AID4I, makes use of automatic machine learning methods for intrusion detection. Through automation of preprocessing, feature selection, model selection, and hyperparameter tuning, the objective is to identify an appropriate machine learning model for intrusion detection. Experimental studies demonstrate that the AID4I framework successfully proposes a suitable model. The integrity, security, and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities. With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security, AID4I is a powerful and effective instrument for intrusion detection. In preprocessing module, three distinct imputation methods are utilized to handle missing data, ensuring the robustness of the intrusion detection system in the presence of incomplete information. Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm. The Parameter Optimization module encompasses a diverse set of 14 classification methods, allowing for thorough exploration and optimization of the parameters associated with each algorithm. By carefully tuning these parameters, the framework enhances its adaptability and accuracy in identifying potential intrusions. Experimental results demonstrate that the AID4I framework can achieve high levels of accuracy in detecting network intrusions up to 14.39% on public datasets, outperforming traditional 2122 CMC, 2023, vol.76, no.2 intrusion detection methods while concurrently reducing the elapsed time for training and testing.

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“…In fact, healthcare applications nowadays plays an essential role for tremendous people from different age and health levels [5,6]. Assume a group of people with chronical diseases, presenting a healthcare monitoring system that can check up their medical situation would be a promising solution for early alarm which can save hundreds of lives [7][8][9][10]. Several research studies have addressed the health monitoring task using various techniques.…”

Section: Introductionmentioning

confidence: 99%

IoT health monitoring system for preventing and controlling risk in confined space using microcontrollers

Saeed¹,

Mazlan²,

Ibrahim³

et al. 2019

IJET

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Healthcare monitoring is a field that caught many researchers from the computer science community in the last decade. In the literature, various levels of people have been considered when proposing a health monitoring system. However, some aspects are still not adequately tackled such as monitoring workers’ health status within confined space where workers would be located in underground environment with less oxygen and a lot of dust. This paper proposes an IoT health monitor system for worker in confined places. The proposed system utilizes four types of microcontroller sensors including LM35 for measuring body temperature, heart beat rate sensor, blood pressure sensor and LPG gas sensor. All the aforementioned sensors are being connected via a GPS module in order to transmit the readings into a smartphone application. A simulation has been conducted to test the proposed sensors where competitive commercial measures have been used as a benchmark. Result of simulation showed that the sensors have fair accuracy that is near-identical to the benchmark.

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An adaptive distributed Intrusion detection system architecture using multi agents

Cited by 7 publications

References 17 publications

A Multi-Agent Adaptive Deep Learning Framework for Online Intrusion Detection

A Multi-Agent Adaptive Deep Learning Framework for Online Intrusion Detection

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

IoT health monitoring system for preventing and controlling risk in confined space using microcontrollers

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