High dimensional nearest neighbor search considering outliers based on fuzzy membership

Prajapati, Gend Lal; Bhartiya, Rupali

doi:10.1109/sai.2017.8252127

Cited by 6 publications

(7 citation statements)

References 20 publications

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“…e method of dimension reduction can eliminate some features and reduce the time complexity, but each feature represents a different outlier value. If the features are selected incorrectly, it will get the wrong outlier value, which will produce an approximate result that is not suitable for future calculation [7]. e complexity, sparsity, and diversity of high-dimensional data restrict the traditional mining algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

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Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection

Shen

Sun

Chen

et al. 2021

Mathematical Problems in Engineering

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As the Internet applications are growing rapidly, the intrusion detection system is widely used to detect network intrusion effectively. Aiming at the high-dimensional characteristics of data in the intrusion detection system, but the traditional frequent-pattern-based outlier mining algorithm has the problems of difficulty in obtaining complete frequent patterns and high time complexity, the outlier set is further analysed to get the attack pattern of intrusion detection. The NSL-KDD dataset and UNSW-NB15 dataset are used for evaluating the proposed approach by conducting some experiments. The experiment results show that the method has good performance in detection rate, false alarm rate, and recall rate and effectively reduces the time complexity.

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Section: Literature Surveymentioning

confidence: 99%

“…Prajapati and Bhartiya [10] proposed a nearest neighbour search algorithm based on the advantages of K-mean algorithm and fuzzy C-mean (FCM) algorithm to solve the problem of uneven data and rigid clustering in high-dimensional data, which can realize nearest neighbour search in a shorter time.…”

Section: Literature Surveymentioning

confidence: 99%

Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection

Shen

Sun

Chen

et al. 2021

Mathematical Problems in Engineering

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“…Where Precision represents the fraction of correctly classified records to that totally classified by IDS as attacks FN) . (12) This metric measures how sensitive is the IDS against attacks traffic over the network system. As FN increases, the Recall of the model decreases .…”

Section: Referring Tomentioning

confidence: 99%

“…A realistic insight into the future of malicious activities we face reveals a notable direction towards intelligent intrusion detections that have the artificial intelligence to detect the novel, unknown attacks. Intelligent intrusion detection systems lie in one of four major categories, ie, (1) Clustering techniques, 3-7 (2) Genetic algorithms, 8-11 (3) Fuzzy logic, 12-16 and (4) Artificial neural networks, ie, supervised 22-26 and unsupervised 22-26 …”

Section: Introductionmentioning

confidence: 99%

Cascaded hybrid intrusion detection model based on SOM and RBF neural networks

Almiani

AbuGhazleh

Al‐Rahayfeh

et al. 2019

Concurrency and Computation

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Cybercriminal activities over computer network systems are considered one of the preponderant issues that humanity will face in the coming two decades. The development steps in the design of intrusion detection systems must be carried out in analogous manner to sophistication levels of intrusions developed by hackers. This work proposes a layered hybrid intrusion detection model uses cascaded layers of Clustered Self-Organized Map (CSOM) and Radial Basis Function (RBF) neural networks to improve the efficiency of detecting frequent and least frequent intrusions. K-means clustered SOM was used to filter attacks as a first layer, whereas RBF-based neural network worked as second filtering and attacked classification layer leading to significance reduction in time required to process connection records and notable improvements in the performance of intrusion detection. A new balanced version of cleansed NSL-KDD dataset was used to validate and evaluate the proposed model. Compared with other existing schemes; the proposed model shows high detection performance in terms of accuracy 97.73% and false positive rate as low as 0.023%. In particular, for detecting least and most harmful attacks, U2R and R2L, the system achieved detection rate of 88.6% with false positive rate of 0.016. Comparative results showed that CSOM-RBF model is more suitable for real-life implementation than other many existing state-of-the-art intrusion detection models.

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“…Many approaches to unsupervised intrusion detection follow k‐means clustering and Euclidean distance for weighting the presence of anomalies ; other approaches are emerging as well, relying on other clustering techniques and density‐based definition of anomalies . Quite a few investigate rule extraction techniques through decision tree (DT) .…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning and rule extraction for Domain Name Server tunneling detection

Aiello

Mongelli

Muselli

et al. 2018

Internet Technology Letters

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The paper deals with k-means clustering and logic learning machine (LLM) for the detection of Domain Name Server (DNS) tunneling. As the LLM shows more versatility in rule generation and classification precision with respect to traditional decision trees, the approach reveals to be robust to a large set of system conditions. The detection algorithm is designed to be applied over streaming data, without accurate tuning of algorithms' parameters. An extensive performance evaluation is provided with respect to different tunneling tools and applications; silent intruders are considered. Results show robustness on a test set that exhibits a different behavior from training. KEYWORDS covert channel, rule extraction, unsupervised learning 1 Internet Technology Letters. 2019;2:e85.wileyonlinelibrary.com/journal/itl2

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High dimensional nearest neighbor search considering outliers based on fuzzy membership

Cited by 6 publications

References 20 publications

Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection

Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection

Cascaded hybrid intrusion detection model based on SOM and RBF neural networks

Unsupervised learning and rule extraction for Domain Name Server tunneling detection

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