Improving the Intrusion Detection System for NSL-KDD Dataset based on PCA-Fuzzy Clustering-KNN

Benaddi, Hafsa; Ibrahimi, Khalil; Benslimane, Abderrahim

doi:10.1109/wincom.2018.8629718

Cited by 53 publications

(13 citation statements)

References 7 publications

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“…In summary, the event detection results of the DELM algorithm continuous [18] inspection are better than those of LSTM, DBN, CNN, and GBDT. When PT �1, each algorithm can better balance the three indicators of DR, FAR, and MTTD.…”

Section: Results Analysismentioning

confidence: 90%

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Fang

Yang

Zheng

et al. 2020

Mathematical Problems in Engineering

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In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables. This paper presents an automatic event detection algorithm based on deep cycle limit learning machine. The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network. The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search. The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved. In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer. Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods. The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information. It can save expensive computing resources and reduce the complexity of the model. Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%. It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms. It is suitable for urban expressway traffic incident detection.

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Section: Results Analysismentioning

confidence: 90%

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Fang

Yang

Zheng

et al. 2020

Mathematical Problems in Engineering

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“…For instance, to improve the performance of IDS frameworks, the authors of [75] propose a mixed strategy involving Principal Component Analysis and fuzzy clustering with KNN-based FS techniques. A correlation-based FS approach coupled with a Support Vector Machine (SVM) classifier is proposed in [76] to build a cloud-based IDS.…”

Section: Related Work On Feature Selection Applied To Ml-based Intrus...mentioning

confidence: 99%

Supervised feature selection techniques in network intrusion detection: A critical review

Mauro

Galatro²,

Fortino

et al. 2021

Engineering Applications of Artificial Intelligence

118

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Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: i) the presence of so many features leads to lengthy training processes (particularly when features are highly correlated), while prediction accuracy does not proportionally improve; ii) some of the features may introduce bias during the classification process, particularly those that have scarce relation with the data traffic to be classified. To this end, by reducing the feature space and retaining only the most significant features, Feature Selection (FS) becomes a crucial pre-processing step in network management and, specifically, for the purposes of network intrusion detection. In this review paper, we complement other surveys in multiple ways: i) evaluating more recent datasets (updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch Python-based procedure; ii) providing a synopsis of most credited FS approaches in the field of intrusion detection, including Multi-Objective Evolutionary techniques; iii) assessing various experimental analyses such as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines to network/security managers who are considering the incorporation of ML concepts into network intrusion detection, where trade-offs between performance and resource consumption are crucial.

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“…Rao and Swathi (2017) adapted two fast KNN classification algorithms i.e., Indexed Partial Distance Search k-Nearest Neighbor (IKPDS), Partial Distance Search k-Nearest Neighbor (KPDS) and comparing with traditional KNN classification for Network Intrusion Detection on NSL-KDD dataset 2017 (NSL-KDD, 2009). Benaddi et al (2018), the authors propose to use PCA-fuzzy Clustering-KNN method which ensemble of Analysis of Principal Component and Fuzzy Clustering with K-Nearest Neighbor feature selection technics to detect anomalies.…”

Section: Machine Learning Models To Network Anomaly Detectionmentioning

confidence: 99%

Toward an Online DoS/DDoS Classification: An Empirical Study for Network Intrusion Detection Systems

Hai¹,

Khiem²,

Phúc³

2021

Journal of Computer Science

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Improving the Intrusion Detection System for NSL-KDD Dataset based on PCA-Fuzzy Clustering-KNN

Cited by 53 publications

References 7 publications

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Supervised feature selection techniques in network intrusion detection: A critical review

Toward an Online DoS/DDoS Classification: An Empirical Study for Network Intrusion Detection Systems

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