Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT

Choudhary, Sarika; Kesswani, Nishtha

doi:10.1016/j.procs.2020.03.367

Cited by 160 publications

(68 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However in some sense we might list the well performing algorithms in their suitable use-cases with the best result such as SVM, Decision Tree, Naïve Bayesian classifier. For the intrusion detection task can be presented the comparisons between classifiers and deep learning model [ 6 ]. Figure 8 shows the ROC curves of the Decision Tree, SVM and Naïve Bayesian classifier.…”

Section: Resultsmentioning

confidence: 99%

“…Common machine learning algorithms are Support Vector Machine (SVM) [ 8 , 23 , 28 ]. Naïve-Bayes classifier [ 12 , 27 ], K-nearest neighbor (KNN) [ 17 , 40 ], artificial neural network (ANN) [ 16 , 18 , 19 ], deep neural network (DNN) [ 6 ], and so on. Figure 3 shows how to select the best features by permutation feature importance.…”

Section: Intrusion Detection System For Multimedia Platformmentioning

confidence: 99%

See 1 more Smart Citation

Intrusion detection by machine learning for multimedia platform

Hsu

Wang

Qiao

2021

Multimed Tools Appl

View full text Add to dashboard Cite

The multimedia service company, Netflix, increased the number of new subscribers during the Coronavirus pandemic age. Intrusion detection systems for multimedia platforms can prevent the platform from network attacks. An intelligent intrusion detection system is proposed for the security IP Multimedia Subsystem (IMS) based on machine learning technology. For increasing the accuracy of the classifiers, it is vital to select the critical features to construct the intrusion detection system. Two-class classifiers, including the Decision Tree, Support Vector Machine, and Naive Bayesian, are selected to evaluate intrusion detection accuracy. According to the three classifiers’ accuracy values, the most critical features are selected based on the features’ ranking orders. Six critical features are selected:Service, dst_host_same_srv_rate, Flag, Protocol Type, Dst_host_rerror_rate, and Count. Numerical comparison with state_of_the_art shows that critical features improve intrusion detection accuracy, which can be better than the deep learning method.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Intrusion Detection System For Multimedia Platformmentioning

confidence: 99%

Intrusion detection by machine learning for multimedia platform

Hsu

Wang

Qiao

2021

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…In the training stage, the Validation is used by the DBN for verification, and the error is calculated by the loss function after the validation. In the verification stage, the 6-step verification method and training accuracy limitation method described in the reference [47] are used to evaluate the training results, which is followed by the testing stage. The experimental results in Table 4 and Fig.…”

Section: A Experimental Parameter Settingmentioning

confidence: 99%

Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

et al. 2021

View full text Add to dashboard Cite

Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve the detection accuracy and performance, we proposed an intrusion detection model based on improved deep belief network (DBN). Traditional neural network training methods, like Back Propagation (BP), start to train a model with preset parameters such as the randomly initialized weights and thresholds, which may bring some issues, e.g., attracting the model to the local optimal solutions, or requiring a long training period. We use the Kernel-based Extreme Learning Machine (KELM) with the supervised learning ability to replace the BP algorithm in DBN in a bid to ameliorate the situation. Considering the problem of poor classification performance usually caused by randomly initializing kernel parameters with KELM, an enhanced grey wolf optimizer (EGWO) is designed to optimize the parameters of KELM. In order to improve the search ability and optimization ability of the traditional grey wolf optimizer algorithm, a novel optimization strategy combining the inner and outer hunting is introduced. Experiments on KDDCup99, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets show that the proposed DBN-EGWO-KELM algorithm has greater advantages in terms of its accuracy, precision, true positive rate, false positive rate and other evaluation indices compared with BP, RBF, SVM, KELM, LIBSVM, CNN, DBN-KELM and other intrusion detection models, and can effectively meet the requirements of intrusion detection of complex networks.INDEX TERMS Intrusion detection, deep belief network, kernel-based extreme learning machine, grey wolf optimizer.

show abstract

“…In [ 86 ], the authors proposed a multi-variate analysis of correlations to extract geometric correlations between network traffic elements for DoS attack detection. In contrast to the closest triangular-zone neighbour solution utilizing the KDDCup99 datasets [ 87 ], this scheme raises the detection accuracy by 3%.…”

Section: Taxonomy Of Ml-based Aa For Iotmentioning

confidence: 99%

Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction

Ahmed

Tahir

Habaebi

et al. 2021

Sensors

View full text Add to dashboard Cite

With the ongoing efforts for widespread Internet of Things (IoT) adoption, one of the key factors hindering the wide acceptance of IoT is security. Securing IoT networks such as the electric power grid or water supply systems has emerged as a major national and global priority. To address the security issue of IoT, several studies are being carried out that involve the use of, but are not limited to, blockchain, artificial intelligence, and edge/fog computing. Authentication and authorization are crucial aspects of the CIA triad to protect the network from malicious parties. However, existing authorization and authentication schemes are not sufficient for handling security, due to the scale of the IoT networks and the resource-constrained nature of devices. In order to overcome challenges due to various constraints of IoT networks, there is a significant interest in using machine learning techniques to assist in the authentication and authorization process for IoT. In this paper, recent advances in authentication and authorization techniques for IoT networks are reviewed. Based on the review, we present a taxonomy of authentication and authorization schemes in IoT focusing on machine learning-based schemes. Using the presented taxonomy, a thorough analysis is provided of the authentication and authorization (AA) security threats and challenges for IoT. Furthermore, various criteria to achieve a high degree of AA resiliency in IoT implementations to enhance IoT security are evaluated. Lastly, a detailed discussion on open issues, challenges, and future research directions is presented for enabling secure communication among IoT nodes.

show abstract

Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT

Cited by 160 publications

References 8 publications

Intrusion detection by machine learning for multimedia platform

Intrusion detection by machine learning for multimedia platform

Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction

Contact Info

Product

Resources

About