Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview

Payares, Esteban; Martínez-Santos, Juan Carlos

doi:10.1117/12.2593297

Cited by 11 publications

(14 citation statements)

References 15 publications

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“…The outcome was expected to be a better and more flexible IDS that carries training-based knowledge of a wider spectrum of requests. Payares et al [13] focused on the implementation of quantum models to detect denial of service (DOS) attacks, while the focus here is on a much wider spectrum of attack vectors. Jeyakarthic et al [14] use simple quantum multi-layer perceptrons, which are known not to learn complex functions as effectively as neural networks do.…”

Section: Related Workmentioning

confidence: 99%

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

Laxminarayana

Mishra

Tiwari

et al. 2024

IEEE Trans. Artif. Intell.

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Section: Related Workmentioning

confidence: 99%

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

Laxminarayana

Mishra

Tiwari

et al. 2024

IEEE Trans. Artif. Intell.

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“…Payares and Martinez-Santos [22] demonstrated the importance of using generalized coherent states for the SVM model. SVM is a classical machine learning model, and coherent states are a calculational tool here.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing

Akter

Shahriar

Bhuiya

2023

Communications in Computer and Information Science

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Ev-ery year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely ex-ploited and lead to system compromise, data leakage, or denial of ser-vice. C and C++ opensource code are now available in order to create a large-scale, classical machine-learning and quantum machine-learning system for function-level vulnerability identification. We assembled a siz-able dataset of millions of open-source functions that point to poten-tial exploits. We created an efficient and scalable vulnerability detection method based on a deep neural network model-Long Short-Term Mem-ory (LSTM), and quantum machine learning model-Long Short-Term Memory (QLSTM), that can learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the de-pendency. Previous studies lack analyzing features of the source code that causes models to recognize flaws in real-life examples. Therefore, We keep the semantic and syntactic information using state-ofthe-art word embedding algorithms such as Glove and fastText. The embedded vectors are subsequently fed into the classical and quantum convolutional neural networks to classify the possible vulnerabilities. To measure the performance, we used evaluation metrics such as F1 score, precision, re-call, accuracy, and total execution time. We made a comparison between the results derived from the classical LSTM and quantum LSTM using basic feature representation as well as semantic and syntactic represen-tation. We found that the QLSTM with semantic and syntactic features detects significantly accurate vulnerability and runs faster than its clas-sical counterpart.⋆ Supported by organization x.

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“…Machine learning (ML) and deep learning (DL) techniques have been widely used in recent years for detecting and mitigating DDoS attacks [10], [11]. The other being statistical methods, shallow machine learning, quantum computing and artificial intelligence [12], [13], [14], [15]. Most of the statistical approaches like entropy variations and correlation take a lot of time in execution throughout the DDoS attack detection process.…”

Section: Introductionmentioning

confidence: 99%

REST-API based DDoS Detection Using Random Forest Classifier in a Platform as a Service Cloud Environment

Habib,

Khursheed

2023

IJCDS

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Cloud services are often delivered through HTTP protocol for ease and reduced cost for both service providers and users. The only drawback is that these protocols and the cloud itself are more prone to Distributed Denial of Service (DDoS) attacks. There is the need for a detection setup that is lightweight, robust and easily deployable on these architectures with an improved efficiency. We thus propose a novel multi-feature hybrid classification based DDOS detection setup that uses the Representational State Transfer (REST)-Application Programming Interface (API) for the attack prediction. The cloud architectures we are using are Heroku, one of the first developed platform as a service (paas) computing platform by salesforce and Amazon Elastic Compute Cloud, one of the most sought after and on-demand infrastructure as a service (iaas) computing platform by amazon. The GitHub repository holds the pre-trained hybrid classifier in its repository which is accessed using the REST-API by the cloud. The HTTP request and response commands are sent to the cloud architecture through the Postman API client where the final attack prediction is done. This makes our cloud burden free as the detection is done outside its domain. The hybrid classifier here is the integration of Random Forest classifier, Decision Tree classifier, Support Vector Machine and XGBoost classifier, all trained on the pre-processed Knowledge Discovery in Databases (KDD) Cup 99 and UNSW-NB15 datasets. Here we are also using two different feature ranking methods i.e., statistical feature ranking and machine learning ranking. It shows the best accuracy results with information gain on KDD Cup 99 dataset and decision tree classifier on UNSW-NB15 dataset as the feature selection technique.

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Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview

Cited by 11 publications

References 15 publications

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing

REST-API based DDoS Detection Using Random Forest Classifier in a Platform as a Service Cloud Environment

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