QEMDD: Quantum Inspired Ensemble Model to Detect and Mitigate DDoS Attacks at Various Layers of SDN Architecture

Saritha, A; Reddy, B. Ramasubba; Babu, A. Suresh

doi:10.1007/s11277-021-08805-5

Cited by 7 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These attacks are due to both quantum and classical security attacks [1,6,10]. One of the most prominent attacks that threaten cybersecurity is the DDoS attacks [2,9,32]. The idea of these attacks is to use multiple machines to send an extensive number of malicious requests to capture a network or harm the distributed system components.…”

Section: Introduction and Background Informationmentioning

confidence: 99%

“…This is because the LDAP protocol is built on top of the TCP/IP protocol and serves to set up authentication policies for a large number of users. By leveraging the LDAP protocol, it becomes possible to authenticate all employees in an organization simultaneously, eliminating the need for individual authentication policies [2,21,22]. As a result, it is crucial to implement robust security approaches for the LDAP protocol.…”

Section: Introduction and Background Informationmentioning

confidence: 99%

See 1 more Smart Citation

DEQSVC: Dimensionality Reduction and Encoding Technique for Quantum Support Vector Classifier Approach to Detect DDoS Attacks

Alomari,

Kumar

2023

IEEE Access

View full text Add to dashboard Cite

Distributed Denial of Service (DDoS) attacks pose a significant threat to the security of networking systems, as they can cause widespread disruption and even bring down entire distributed systems platforms. In this paper, we propose an approach called the DEQSVC that leverages quantum machine learning techniques to detect DDoS attacks with high accuracy. The DEQSVC integrates the most efficient dimensionality reduction techniques, a robust feature map method, and an efficient kernel estimation technique to improve data encoding, learning process, and detection accuracy. To evaluate the performance of the proposed DEQSVC, we conducted simulations using the Qiskit platform and executed the approach on an IBM quantum computer. Our results demonstrate that the DEQSVC outperforms several benchmark algorithms commonly used in intrusion detection systems. Specifically, the DEQSVC achieves a detection accuracy of 99.49, indicating its effectiveness as a highly accurate and efficient method for detecting DDoS attacks.

show abstract

Section: Introduction and Background Informationmentioning

confidence: 99%

Section: Introduction and Background Informationmentioning

confidence: 99%

DEQSVC: Dimensionality Reduction and Encoding Technique for Quantum Support Vector Classifier Approach to Detect DDoS Attacks

Alomari,

Kumar

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The application instructs the controller to reprogramme the data plane whenever an abnormality is identified [7]. Where these devices within the network contain special open interfaces that are managed by software, therefore the control and data planes run on routers dispersed across the network [8].…”

Section: Introductionmentioning

confidence: 99%

DDoS attack detection in software defined networking controller using machine learning techniques

2022

View full text Add to dashboard Cite

The term software defined networking (SDN) is a network model that contributes to redefining the network characteristics by making the components of this network programmable, monitoring the network faster and larger, operating with the networks from a central location, as well as the possibility of detecting fraudulent traffic and detecting special malfunctions in a simple and effective way. In addition, it is the land of many security threats that lead to the complete suspension of this network. To mitigate this attack this paper based on the use of machine learning techniques contribute to the rapid detection of these attacks and methods were evaluated detecting DDoS attacks and choosing the optimum accuracy for classifying these types within the SDN, the results showed that the proposed system provides the better results of accuracy to detect the DDos attack in SDN network as 99.90% accuracy of Decision Tree (DT) algorithm.

show abstract

Rational Identification of Suitable Classification Models for Detecting DDoS Attacks in Software-Defined Networks

Joshi

Haribabu

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

QEMDD: Quantum Inspired Ensemble Model to Detect and Mitigate DDoS Attacks at Various Layers of SDN Architecture

Cited by 7 publications

References 31 publications

DEQSVC: Dimensionality Reduction and Encoding Technique for Quantum Support Vector Classifier Approach to Detect DDoS Attacks

DEQSVC: Dimensionality Reduction and Encoding Technique for Quantum Support Vector Classifier Approach to Detect DDoS Attacks

DDoS attack detection in software defined networking controller using machine learning techniques

Rational Identification of Suitable Classification Models for Detecting DDoS Attacks in Software-Defined Networks

Contact Info

Product

Resources

About