Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings

Huang, Zefeng; Gou, Zhonghua

doi:10.1016/j.jobe.2023.106653

Cited by 8 publications

(1 citation statement)

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“…The first step in this algorithm is to calculate the number of optimal clusters present in the data sets. For this purpose, the Bayesian information criterion (BIC) is used [45]. The determination of the number of clusters present in the dataset is highly costly computationally; for the case of attack detection, the number of clusters is two, one for the data with attack and another for data with no attack.…”

Section: Gmmmentioning

confidence: 99%

An Unsupervised Approach for the Detection of Zero-Day DDoS Attacks in IoT Networks

Roopak,

Parkinson,

Tian

et al. 2024

Preprint

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In this article, an unsupervised IDS (Intrusion Detection System) is presented for the detection of zero-day DDoS (Distributed Denial of Service) attacks for IoT (Internet of Things) networks that can detect anomalies without the need for prior knowledge or training in attack information. Attackers exploit existing undiscovered vulnerabilities in the system to launch zero-day attacks. There exist many traditional deep learning and machine learning based attack detection systems that cannot deal with and detect new zero-day attacks and mostly misclassify those attacks. Zero-day attacks are often new unknown threats that have not been encountered before, in addition, labelling of data is a time-consuming task for security experts, So there exists a need for unsupervised methods that can detect unseen cyber-attacks on the zero-day. DDoS attacks have recently adversely affected many organisations in terms of finance and services, as these attacks have become more sophisticated and damaging. The growth of IoT networks has facilitated the launch of more DDoS attacks. In this work, an unsupervised approach-based algorithm is proposed for the detection of zero-day DDoS attacks in IoT networks by exploiting random projection for the feature selection process to reduce the dimensionality of the network data and ensemble model consisting of K-means, GMM and one-class SVM for the unsupervised classification of the data as attack and normal using the hard voting technique. The CIC-DDoS2019 datasets are used for an extensive evaluation of the proposed method. The proposed method has obtained an accuracy of 94.55%, which is better than the other state-of-the-art unsupervised learning-based methods.

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Section: Gmmmentioning

confidence: 99%