An Optimized Active Learning TCM-KNN Algorithm Based on Intrusion Detection System

Batra, Reenu; Mahajan, Manish; Goel, Amit

doi:10.1007/978-981-16-9416-5_45

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Multi-layer computing is a viable option to address some of the shortcomings associated with conventional SDN [5]. The addition of cloud infrastructure to this framework makes it more effective at handling large amounts of data [6]. The "built SDN cloud" uses software-defined networking to provide network resource flexibility, scalability, and control.…”

Section: Introductionmentioning

confidence: 99%

Dual discriminator conditional sheep flock generative adversarial network-based intrusion detection system in MAC-based SDN framework

Nanavath

Bhattacharyya

2023

Preprint

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Software-defined networking (SDN) technology enables and supports the 5G ecosystem, which is expected to handle a vast amount of sensitive data and provide connectivity to many users, making them more vulnerable to cyberattacks. Security measures, such as access control, encryption, authentication, and intrusion detection systems, must be implemented to protect network security against adversarial attacks. An intrusion detection system is a security technology designed to detect vulnerabilities in computer systems. Medium Access Control (MAC) addresses are required to identify and control access to specific devices on a network. Although there are various methods for intrusion detection, the most popular approaches have low accuracy. Therefore, a dual discriminator conditional sheep flock generative adversarial network-based intrusion detection system (IDS) in MAC-based SDN architecture is proposed to solve these issues and detect and prevent attacks. Initially, the 5G users authenticate with the help of the Four-Q-Curve algorithm. Next, the ideal switches are chosen to overcome the flow table overload using the univariate ensemble feature selection technique. Following this, a dual-discriminator conditional generative adversarial network (DC-GAN) is used to categorize selected features into normal, assault, and suspicious packets. The Sheep Flock Optimization Algorithm (SFOA) is used to optimize the DC-GAN. Then, the Growing Self-Organizing Map (GSOM) classifies the suspicious packets into normal and malicious packets. The experimental results show that the proposed method outperforms the existing methods in terms of accuracy, F1 score, sensitivity, and false alarm rate while ensuring low energy consumption and higher network throughput.

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Section: Introductionmentioning

confidence: 99%

Dual discriminator conditional sheep flock generative adversarial network-based intrusion detection system in MAC-based SDN framework

Nanavath

Bhattacharyya

2023

Preprint

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An intelligent intrusion prediction and prevention system for software defined internet of things cloud networks

Kranthi

Kanchana²,

Suneetha³

2022

Peer-to-Peer Netw. Appl.

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Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Vermander,

Mancisidor,

Cabanes

et al. 2024

J NeuroEngineering Rehabil

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The number of people who need to use wheelchair for proper mobility is increasing. The integration of technology into these devices enables the simultaneous and objective assessment of posture, while also facilitating the concurrent monitoring of the functional status of wheelchair users. In this way, both the health personnel and the user can be provided with relevant information for the recovery process. This information can be used to carry out an early adaptation of the rehabilitation of patients, thus allowing to prevent further musculoskeletal problems, as well as risk situations such as ulcers or falls. Thus, a higher quality of life is promoted in affected individuals. As a result, this paper presents an orderly and organized analysis of the existing postural diagnosis systems for detecting sitting anomalies in the literature. This analysis can be divided into two parts that compose such postural diagnosis: on the one hand, the monitoring devices necessary for the collection of postural data and, on the other hand, the techniques used for anomaly detection. These anomaly detection techniques will be explained under two different approaches: the traditional generalized approach followed to date by most works, where anomalies are treated as incorrect postures, and a new individualized approach treating anomalies as changes with respect to the normal sitting pattern. In this way, the advantages, limitations and opportunities of the different techniques are analyzed. The main contribution of this overview paper is to synthesize and organize information, identify trends, and provide a comprehensive understanding of sitting posture diagnosis systems, offering researchers an accessible resource for navigating the current state of knowledge of this particular field.

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An Optimized Active Learning TCM-KNN Algorithm Based on Intrusion Detection System

Cited by 3 publications

References 19 publications

Dual discriminator conditional sheep flock generative adversarial network-based intrusion detection system in MAC-based SDN framework

Dual discriminator conditional sheep flock generative adversarial network-based intrusion detection system in MAC-based SDN framework

An intelligent intrusion prediction and prevention system for software defined internet of things cloud networks

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

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