Artificial Intelligence outflanks all other machine learning classifiers in Network Intrusion Detection System on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing

Kanimozhi, V.; Jacob, T. Prem

doi:10.1016/j.icte.2020.12.004

Cited by 48 publications

(40 citation statements)

References 4 publications

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“…All classifiers except C5 were used in prior work reviewed in this study; in particular, C1, C2, C8, C9, and C10 are commonly used in IDS operating in a range of networked environments including CC [15,18,28,57,58]. Classifiers C4, C6, and C7 were considered by Maza and Touahria [15] for benchmarking the performance of their proposed multiobjective FS method; C4 was used in Step Description…”

Section: Resultsmentioning

confidence: 99%

“…Output the final feature set in Array E as the ensemble features. [47,48,50], C6 was used in [24], and C7 was also used in [28]. Classifier C3 was used in [28] and is a preferred classifier for distributed processing environments similar to CC [59].…”

Section: Step 11mentioning

confidence: 99%

“…[47,48,50], C6 was used in [24], and C7 was also used in [28]. Classifier C3 was used in [28] and is a preferred classifier for distributed processing environments similar to CC [59]. e algorithm C5 was added as it is one of the primary methods for achieving high accuracy in multicategory classification [60].…”

Section: Step 11mentioning

confidence: 99%

“…However, the XGBoost model training time was relatively high. Similarly, the ANN-based intrusion detection model described in [28] that used the MLP classifier performed better than five other ML-based models (RF, KNN, SVM, AdaBoost, and Naïve Bayes); the experimental work was done on the realistic cyber dataset CSE-CIC-IDS2018. A benchmarking review of DL methods used in extant research and a performance comparison of IDS deploying DL and ML approaches (as single methods) can be found in [26].…”

Section: Introductionmentioning

confidence: 99%

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Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

Ogwara

Petrova

Yang

2022

Journal of Computer Networks and Communications

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Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.

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Section: Resultsmentioning

confidence: 99%

Section: Step 11mentioning

confidence: 99%

Section: Step 11mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

Ogwara

Petrova

Yang

2022

Journal of Computer Networks and Communications

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“…Many contributions have been made throughout this field of operational technologies by the process automation and control group in particular. CPS (Cyber-Physical Systems) is a term used to describe the mixture of computational, communication, and physical components [5] [6]. Cyber-Physical Systems CPS is a modeling tool that can be used to simulate a wide range of applications, including sophisticated critical infrastructures.…”

Section: Introductionmentioning

confidence: 99%

A Review on Conceptual Model of Cyber Attack Detection and Mitigation Using Deep Ensemble Model

Prabhu

Nethravathi

2022

IJAEML

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Purpose: When communication networks and the internet of things are integrated into business control systems, they become more vulnerable to cyber-attacks, which can have disastrous consequences. An Intrusion Detection System is critical for identifying and blocking attacks in IoT networks. As a result, utilizing a unique Classification and Encryption approach, this article offered a novel architecture for attack node mitigation. Design/Methodology/Approach: This study reviews the current status of various cyber-attack detection models and their mitigation techniques. The proposed model works so that the system is first trained on the dataset, including the DDoS attack and ransomware components. The model examines if it contains malware from DDoS or Ransomware. When tested, we use trained information or a data set to provide the results on attack existence and what sort of attack we offer the extracted characteristics of the input. When the model identifies the attacker node, it is removed via the BAIT technique from the network. Findings/Result: Recognizing the importance of information security is critical to combating cybercrime and encouraging cyber security. There are numerous tactics, strategies, and equipment currently in use to detect intrusion in a computer network, and continuing research is being conducted to improve their ability to detect intrusion. The basic version of a cyber-assault detection and mitigation system using the BRELU-RESNET method was evaluated in this study. Originality/Value: This review-based research article examines the present state of cyber-attack detection and mitigation, as well as the research gaps and research goals. Paper Type: Review-based research analysis

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An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

Varghese

Jose

2022

Concurrency and Computation

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Cloud system is a set of resources meant to provide cloud customers with on-demand services. Accessibility to cloud environment is provided through internet services, making data stored on cloud more accessible to attackers internally and externally. Several intrusion detection systems and authentication methods have been developed in previous studies to identify the intruders, however they are generally unsuccessful with certain drawbacks. Numerous existing researchers have focused on machine learning techniques for identifying intrusions. This article intends to introduce a new intrusion detection model in the cloud and it involves three processes like preprocessing, feature extraction and detection. Initially, preprocessing takes place and the preprocessed data are subjected to feature extraction process. The flow-based features, statistical, and higher-order statistical features with improved holoentropy features, and the technical indicators are extracted. After extracting the features, they are subjected to a detection process, where the optimized radial bias function neural network (RBF-NN) is exploited. For precise detection, the weight of RBF-NN is tuned optimally by (harmonic mean based poor and rich optimization) HMPRO Algorithm. The main aim of the proposed model is to detect attacks in the cloud.Finally, the performance of the presented scheme is computed over the existing approaches using the CICDDoS2019 and UNSW-NB_15 dataset in terms of different metrics.

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Artificial Intelligence outflanks all other machine learning classifiers in Network Intrusion Detection System on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing

Cited by 48 publications

References 4 publications

Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

A Review on Conceptual Model of Cyber Attack Detection and Mitigation Using Deep Ensemble Model

An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

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