Experimental Evaluation of a Multi-layer Feed-Forward Artificial Neural Network Classifier for Network Intrusion Detection System

Al-Zewairi, Malek; Almajali, Sufyan; Awajan, Arafat

doi:10.1109/ictcs.2017.29

Cited by 70 publications

(45 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Al-Zewairi et al [13] evaluated ANN on the UNSW-NB15 dataset using the back propagation and stochastic gradient descent methods, and delivering the evaluation accuracy of 98.99% with a low false alarm rate of around 0.5%.…”

Section: B Applications Of ML Algorithmsmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Anomaly Detection

Elmrabit

Zhou

et al. 2020

2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)

105

View full text Add to dashboard Cite

Malicious attack detection is one of the critical cyber-security challenges in the peer-to-peer smart grid platforms due to the fact that attackers' behaviours change continuously over time. In this paper, we evaluate twelve Machine Learning (ML) algorithms in terms of their ability to detect anomalous behaviours over the networking practice. The evaluation is performed on three publicly available datasets: CICIDS-2017, UNSW-NB15 and the Industrial Control System (ICS) cyberattack datasets. The experimental work is performed through the ALICE high-performance computing facility at the University of Leicester. Based on these experiments, a comprehensive analysis of the ML algorithms is presented. The evaluation results verify that the Random Forest (RF) algorithm achieves the best performance in terms of accuracy, precision, Recall, F1-Score and Receiver Operating Characteristic (ROC) curves on all these datasets. It is worth pointing out that other algorithms perform closely to RF and that the decision regarding which ML algorithm to select depends on the data produced by the application system.

show abstract

Section: B Applications Of ML Algorithmsmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Anomaly Detection

Elmrabit

Zhou

et al. 2020

2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)

105

View full text Add to dashboard Cite

show abstract

“…The output layer activation function is softmax; a generalized logistic regression activation function is used. The number of output units in softmax is equivalent to the number of attack categories in addition to the normal class [60]. The deep learning architectures of TCNN, LSTM, and CNN are shown in Figure 6, and their hyperparameters are shown in Table 6.…”

Section: Feature Transformationmentioning

confidence: 99%

Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering

Derhab

Aldweesh

Emam

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In the era of the Internet of Things (IoT), connected objects produce an enormous amount of data traffic that feed big data analytics, which could be used in discovering unseen patterns and identifying anomalous traffic. In this paper, we identify five key design principles that should be considered when developing a deep learning-based intrusion detection system (IDS) for the IoT. Based on these principles, we design and implement Temporal Convolution Neural Network (TCNN), a deep learning framework for intrusion detection systems in IoT, which combines Convolution Neural Network (CNN) with causal convolution. TCNN is combined with Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC) to handle unbalanced dataset. It is also combined with efficient feature engineering techniques, which consist of feature space reduction and feature transformation. TCNN is evaluated on Bot-IoT dataset and compared with two common machine learning algorithms, i.e., Logistic Regression (LR) and Random Forest (RF), and two deep learning techniques, i.e., LSTM and CNN. Experimental results show that TCNN achieves a good trade-off between effectiveness and efficiency. It outperforms the state-of-the-art deep learning IDSs that are tested on Bot-IoT dataset and records an accuracy of 99.9986% for multiclass traffic detection, and shows a very close performance to CNN with respect to the training time.

show abstract

“…Therefore, many surveillance models are designed to distinguish anomalies in the working environment, such as a collision-free surveillance model in the Internet of things [10], data mining in a smart grid [11], and a UAV surveillance framework in the smart city [12]. However, the most typical and common surveillance learning algorithms in IDS models are the deep belief network [13], the artificial neural networks (ANN) [14], the support vector machine (SVM) [15], the extreme learning machine [16], the Convolutional Neural Network (CNN) [17], and so on, which have also made IDS achieve great progress in anomaly detection. The above-mentioned methods can be used alone or combined with other algorithms.…”

Section: Related Workmentioning

confidence: 99%

Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Gao¹,

Chai²,

Zhang³

et al. 2019

Energies

View full text Add to dashboard Cite

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

show abstract

Experimental Evaluation of a Multi-layer Feed-Forward Artificial Neural Network Classifier for Network Intrusion Detection System

Cited by 70 publications

References 2 publications

Evaluation of Machine Learning Algorithms for Anomaly Detection

Evaluation of Machine Learning Algorithms for Anomaly Detection

Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering

Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Contact Info

Product

Resources

About