Feature Clustering for Anomaly Detection Using Improved Fuzzy Membership Function

Kumar, Gunupudi Rajesh; Mangathayaru, Nimmala; Narsimha, G.; Cheruvu, Aravind

doi:10.1145/3234698.3234733

Cited by 32 publications

(4 citation statements)

References 34 publications

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“…Chouhan et al 14 Dimensionality issue is the most common problem with the classification models because of the increased number of features available for training. Kumar et al 15 presented a Dimensionality Reduction (DR) mechanism to overcome the problem. The initial connection representation was transformed to reduce the noise affect and better classification or detection rates for the equivalent representation.…”

Section: Related Workmentioning

confidence: 99%

FD‐DBN: Flow directed deep belief network for accurate anomaly detection in cloud computing

Rajarao,

Sreenivasulu

2023

Int J Communication

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SummarySecurity becomes the key concern in a cloud environment, as the servers are distributed throughout the globe and involve the circulation of highly sensitive data. Intrusions in the cloud are common because of the huge network traffic that paves the way for intruders to breach traditional security systems with sophisticated software. To avoid such problems, intrusion detection systems (IDSs) have been introduced by various researchers. Each IDS was developed to achieve a particular objective, that is, providing security by detecting intrusions. Most of the available IDS are inefficient and are unable to provide accurate classification. Also, some of them are computationally expensive to be implemented in practical scenarios. This article proposes a new and efficient IDS framework that can accurately classify the intrusion type through effective training to address the existing drawbacks. The proposed framework, named flow directed deep belief network (FD‐DBN), involves three main phases: pre‐processing, clustering, and classification. In pre‐processing, certain data mining operations are carried out to clean the data. The clustering phase is carried out using the game‐based k‐means (GBKM) clustering algorithm. The clustered data is then provided as input to the FD‐DBN classification framework, where the training process is carried out. The deep belief network (DBN) training is performed with dataset features, and the flow direction algorithm is adopted for tuning the weight parameters of DBN. Through tuning, the model yielded accurate classification outcomes. The simulations are done in Python 3.6, and the results proved that the proposed framework is much more effective than the existing IDS frameworks.

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Section: Related Workmentioning

confidence: 99%

FD‐DBN: Flow directed deep belief network for accurate anomaly detection in cloud computing

Rajarao,

Sreenivasulu

2023

Int J Communication

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“…To see the future perspective of the proposed work, it can be figured out traditionally, the current deep learning applications have considered existing distance functions in the research literature for similarity computations but did not try to fit in new functions for similarity computations [46][47][48][49][50]. There is a possibility to devise threshold and similarity functions to suit deep learning applications [51][52][53][54][55]. For instance, recent research contributions propose various similarity and threshold functions for temporal pattern mining which can be redesigned to suit deep learning applications [56][57][58][59][60].…”

Section: Proposed Neural Architecturementioning

confidence: 99%

An Attention Based Neural Architecture for Arrhythmia Detection and Classification from ECG Signals

Mangathayaru¹,

Rani²,

Janaki³

et al. 2021

Computers, Materials &Amp; Continua

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Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine. Detecting arrhythmia from ECG signals is considered a standard approach and hence, automating this process would aid the diagnosis by providing fast, costefficient, and accurate solutions at scale. This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography (ECG) signals causing arrhythmia. In this era of applied intelligence, automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions. In this research, our contributions are two-fold. Firstly, the Dual-Tree Complex Wavelet Transform (DT-CWT) method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features. Next, A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters. To ensure that the model's generalizability, a set of five traintest variants are implied. The proposed model attains the highest accuracy of 98.5% for classifying 8 variants of arrhythmia on the MIT-BIH dataset. To test the resilience of the model, the unseen (test) samples are increased by 5x and the deviation in accuracy score and MSE was 0.12% and 0.1% respectively. Further, to assess the diagnostic model performance, AUC-ROC curves are plotted. At every test level, the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application. As a note, this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.

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“…Nguyen et al (2019) proposed a new collaborative and intelligent NIDS architecture for anomaly detection in SDN‐based cloud IoT networks which obtained better detection accuracy results for DDoS attacks. Gunupudi et al (2017) Kumar, Mangathayaru, Narsimha, and Cheruvu (2018) Mangathayaru, Kumar, and Narsimha (2016) Kumar, Mangathayaru, Narsimha, and Reddy (2017) propose self‐constructing feature clustering technique for anomaly detection using Gaussian‐based approach. Li et al (2019) demonstrates system statistics learning‐based IoT security and introduced a neural network classifier algorithm for anomaly detection.…”

Section: Literature Surveymentioning

confidence: 99%

A machine learning approach for imputation and anomaly detection in IoT environment

Radhakrishna

Kumar

Puligadda³

et al. 2020

Expert Systems

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The problem of anomaly and attack detection in IoT environment is one of the prime challenges in the domain of internet of things that requires an immediate concern. For example, anomalies and attacks in IoT environment such as scan, malicious operation, denial of service, spying, data type probing, wrong setup, malicious control can lead to failure of an IoT system. Datasets generated in an IoT environment usually have missing values. The presence of missing values makes the classifier unsuitable for classification task. This article introduces (a) a novel imputation technique for imputation of missing data values (b) a classifier which is based on feature transformation to perform classification (c) imputation measure for similarity computation between any two instances that can also be used as similarity measure. The performance of proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F-Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using proposed imputation technique. For experimental study in this article, we have used an open source dataset named distributed smart space orchestration system publicly available from Kaggle. Experiment results obtained are also validated using Wilcoxon non-parametric statistical test. It is proved that the performance of proposed approach is better when compared to existing classifiers when the imputation process is performed using F-Kmeans and K-Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when the proposed imputation and classification technique are applied.

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Feature Clustering for Anomaly Detection Using Improved Fuzzy Membership Function

Cited by 32 publications

References 34 publications

FD‐DBN: Flow directed deep belief network for accurate anomaly detection in cloud computing

FD‐DBN: Flow directed deep belief network for accurate anomaly detection in cloud computing

An Attention Based Neural Architecture for Arrhythmia Detection and Classification from ECG Signals

A machine learning approach for imputation and anomaly detection in IoT environment

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