A Framework for implementing an ML or DL model to improve Intrusion Detection Systems (IDS) in the NTMA context, with an example on the dataset (CSE-CIC-IDS2018)

Azeroual, Hakim; Belghiti, Imane Daha; Berbiche, Naoual

doi:10.1051/itmconf/20224602005

Cited by 2 publications

(5 citation statements)

References 11 publications

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“…The results of each model were evaluated using precision, recall, F 1 score, specificity, and classification accuracy. Precision is the number of true positives as a percentage of the number of predicted positives [59]. Recall is the number of true positives as a percentage of the number of all positives [59].…”

Section: Resultsmentioning

confidence: 99%

“…Precision is the number of true positives as a percentage of the number of predicted positives [59]. Recall is the number of true positives as a percentage of the number of all positives [59]. The F 1 score is the harmonic mean of recall and precision [59].…”

Section: Resultsmentioning

confidence: 99%

“…Recall is the number of true positives as a percentage of the number of all positives [59]. The F 1 score is the harmonic mean of recall and precision [59]. Specificity is the percentage of true negatives as a percentage of all negatives [60].…”

Section: Resultsmentioning

confidence: 99%

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Priority Detector and Classifier Techniques Based on ML for the IoMT

A. Alsemmeari,

Yehia Dahab,

Alturki

et al. 2023

Computers, Materials &Amp; Continua

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Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients' health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees' stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner. However, overwhelming the fog layer with extensive data will increase the load on fog nodes, leading to computational challenges. This study aims to reduce fog computation by proposing machine learning (ML) models with two phases. The first phase of the ML model assesses the priority of the situation based on the stress level. In the second phase, a classifier determines the cause of stress, which was either interruptions or time pressure while completing a task. This approach reduced the computation cost for the fog node, as only high-priority records were transferred to the fog. Low-priority records were forwarded to the cloud. Four ML approaches were compared in terms of accuracy and prediction speed: Knearest neighbors (KNN), a support vector machine (SVM), a bagged tree (BT), and an artificial neural network (ANN). In our experiments, ANN performed best in both phases because it scored an F 1 score of 99.97% and had the highest prediction speed compared with KNN, SVM, and BT.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Priority Detector and Classifier Techniques Based on ML for the IoMT

A. Alsemmeari,

Yehia Dahab,

Alturki

et al. 2023

Computers, Materials &Amp; Continua

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“…For the CSE-CIC-IDS2018 dataset, Azeroual et al [11] provided a framework for implementing the DL algorithm to Improve Intrusion Detection Systems (IDS). Using CNN, the model accuracy on validation data decreased to 83.55% after 50 iterations, but it returned a respectable accuracy of 92% after 30 iterations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To accurately evaluate the effectiveness of HXG-BLSTM, we conducted in-depth comparison with the most recent DL algorithms that were employed in the cyber threat detection literature. In this comparison, we used deep learning algorithms from earlier studies such as [11], [28], [10], [32], [1], [9], BWO-CONV-LSTM [27], [48], [45], [6], [18], [21], [42], and [1]. The results of the comparison for deep learning approaches are shown in Table 9.…”

Section: Hxgblstm Vs Other Algorithmsmentioning

confidence: 99%

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

Amin,

El-Taweel,

Ali

et al. 2023

mendel

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The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXGBLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Algorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been mitigated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC -IDS2018 dataset, which contains the latest network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8\%, with F1-score of 99.83\%, precision of 99.85\%, and recall of 99.82\%, in extensive and detailed experiments conducted on a real dataset. The best accuracy, F1-score, precision, and recall for multi-class classification were all around 100\%, which does give the proposed algorithm an advantage over the compared ones.

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A Framework for implementing an ML or DL model to improve Intrusion Detection Systems (IDS) in the NTMA context, with an example on the dataset (CSE-CIC-IDS2018)

Cited by 2 publications

References 11 publications

Priority Detector and Classifier Techniques Based on ML for the IoMT

Priority Detector and Classifier Techniques Based on ML for the IoMT

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

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