Detecting DDoS Attacks in IoT-Based Networks Using Matrix Profile

Alzahrani, Mohammed Ali; AlZahrani, Abdullah A.; Siddiqui, Muhammad Shoaib

doi:10.3390/app12168294

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…Yet another study matrix profile‐based solution for detecting DDoS attacks in IoT‐based networks was conducted 8 . The matrix profile incorporates data structures and algorithms for anomaly detection and uses two components, that is, profile index and distance profile.…”

Section: Methodsmentioning

confidence: 99%

“…Yet another study matrix profile-based solution for detecting DDoS attacks in IoT-based networks was conducted. 8 The matrix profile incorporates data structures and algorithms for anomaly detection and uses two components, that is, profile index and distance profile. While profile indices are responsible for holding the index of nearest neighbors, distance profile is an array of Euclidean distances.…”

Section: Related Workmentioning

confidence: 99%

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SDN and application layer DDoS attacks detection in IoT devices by attention‐based Bi‐LSTM‐CNN

Priyadarshini

Mohanty

Alkhayyat

et al. 2023

Trans Emerging Tel Tech

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The Internet of Things (IoT) is connecting more devices every day. Security is critical to ensure that the devices operate in a trusted environment. The lack of proper IoT security encourages cybercriminals to target many smart devices across the network and gain sensitive information. Distributed Denial of Service (DDoS) attacks are common in the IoT infrastructure and involve hijacking IoT devices to consume resources and interrupt services. This may specifically vandalize the application running the service that the end users are trying to access (application layer DDoS attacks) or flood the network bandwidth leading to network failure (software defined network DDoS attacks). This article proposes a hybrid attention‐based bidirectional long short term memory (LSTM) with convolutional neural networks (CNN) to identify DDoS attacks in the application layer and SDN. We deploy several other machine learning models like logistic regression, decision trees, random forests, support vector machines, K‐nearest neighbors, extreme gradient boosting, artificial neural networks, CNN, LSTM, CNN‐LSTM to evaluate the performance of our proposed model. The evaluation metrics considered for the study are accuracy, precision, recall, and F‐1 score. The experimental analysis on multiple datasets exhibits that the proposed model performs the classification efficiently with an accuracy of 99.74% and 99.98%.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SDN and application layer DDoS attacks detection in IoT devices by attention‐based Bi‐LSTM‐CNN

Priyadarshini

Mohanty

Alkhayyat

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Factors such as signal periodicity and time series length should be considered when selecting the best subsets and window sizes. To get the ideal balance among accuracy and computational e ciency, it is advisable to conduct experiments with various subsets and window sizes, subsequently comparing the outcomes (Alzahrani et al, 2022). Section 5 will offer additional analysis and explanation.…”

Section: Features Selectionmentioning

confidence: 99%

“…In other words, it is the position of its most identical sub-sequence. Fast computation and detection times are two of the matrix pro le's most important features(Alzahrani et al, 2022;.…”

mentioning

confidence: 99%

“…Factors like signal periodicity, and time series length are considered when selecting the best window size. Experimenting with different window sizes and comparing results can help nd the best comparison between accuracy and computational e ciency(Alzahrani et al, 2022).Yeh et al(Yeh et al, 2017) introduced "mSTAMP" as an extension of the original MP framework to e ciently nd similar motifs and discord in time series data. The mSTAMP algorithm uses the MP as a fundamental to simultaneously identify discords or motifs across multiple time series samples.…”

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confidence: 99%

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Enhancing Cloud Network Security with Innovative Time Series Analysis

Al-Musawi,

ALMAZRAWE

2024

Preprint

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Cloud computing has revolutionized computing infrastructure abstraction and utilisation, characterized by its cost-effective and high-quality services. However, the challenge of securing cloud networks persists, primarily due to the extensive exchange of data and the inherent complexity of these systems. Anomaly detection emerges as a promising solution to enhance cloud network security, offering insights into system behaviour and alerting operators for further actions. This paper presents a novel time series analysis technique for detecting anomalies in cloud networks. Our approach utilises a multi-dimensional matrix profile, an innovative time series analysis method, to highlight anomalous patterns within multiple features extracted from network traffic streams. Additionally, we employ the Kneedle algorithm to pinpoint the highlighted patterns that identify anomalies. To evaluate the effectiveness of our method, we implemented timestamp-based and index-based methods to two distinct datasets: the most widely used UNSW-NB15 and the recently introduced CICIoT2023 datasets. The results highlight the efficacy of our proposed method in identifying cloud network anomalies. It achieved an impressive accuracy of 99.6% and an F1-score of 99.8% using the timestamp-based analysis method. For the index-based analysis method, the accuracy reached 98%, accompanied by an outstanding F1-score of 99.9%.

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