Host Based Intrusion Detection and Prevention Model Against DDoS Attack in Cloud Computing

Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers’ mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol’s attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol’s vulnerabilities. We propose many RDoS attack’s techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks’ network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks.

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“…accuracy, precision, recall (i.e. detection rate), and F-score, to evaluate the model's performance [49].…”

Section: Discovery Engine-based Lstm Experiments Resultsmentioning

confidence: 99%

A threat intelligence framework for protecting smart satellite-based healthcare networks

Al-Hawawreh

Moustafa

Slay

2021

Neural Comput & Applic

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“…It reduces training and testing time considerably and effectively improves the prediction accuracy of support vector machines (SVM) with regard to attacks. Jaber et al [30] used principal component analysis and linear discriminant analysis with a hybrid, nature-inspired metaheuristic algorithm called Ant Lion optimization for feature selection and artificial neural networks to classify and configure the cloud server, in order to prevent DDoS attack in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Ensemble Machine Learning Model for Intrusion Detection

et al. 2019

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In recent years, advanced threat attacks are increasing, but the traditional network intrusion detection system based on feature filtering has some drawbacks which make it difficult to find new attacks in time. This paper takes NSL-KDD data set as the research object, analyses the latest progress and existing problems in the field of intrusion detection technology, and proposes an adaptive ensemble learning model. By adjusting the proportion of training data and setting up multiple decision trees, we construct a MultiTree algorithm. In order to improve the overall detection effect, we choose several base classifiers, including decision tree, random forest, kNN, DNN, and design an ensemble adaptive voting algorithm. We use NSL-KDD Test+ to verify our approach, the accuracy of the MultiTree algorithm is 84.2%, while the final accuracy of the adaptive voting algorithm reaches 85.2%. Compared with other research papers, it is proved that our ensemble model effectively improves detection accuracy. In addition, through the analysis of data, it is found that the quality of data features is an important factor to determine the detection effect. In the future, we should optimize the feature selection and preprocessing of intrusion detection data to achieve better results.

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“…The concepts of core mini-clusters and grid are useful for summarizing the data, which provides a smaller version of the data, making it feasible to provide storage and computation for clustering [42]. Other researchers have also used separated approaches for reducing data, for example, [43,44]. An existing challenge is how to handle high dimensionality without affecting the quality of clustering.…”

Section: High Dimensional Aware Machine Learningmentioning

confidence: 99%

An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

et al. 2021

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An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

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Host Based Intrusion Detection and Prevention Model Against DDoS Attack in Cloud Computing

Cited by 16 publications

References 10 publications

A threat intelligence framework for protecting smart satellite-based healthcare networks

A threat intelligence framework for protecting smart satellite-based healthcare networks

An Adaptive Ensemble Machine Learning Model for Intrusion Detection

An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

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