A Survey on Intrusion Detection System: State of the Art Review

Amudhavel, J.; Brindha, V. Evelyn; Anantharaj, B.; Karthikeyan, P.; Bhuvaneswari, B.; Vasanthi, M. S.; Nivetha, D.; Vinodha, D.

doi:10.17485/ijst/2016/v9i11/89264

Cited by 11 publications

(4 citation statements)

References 36 publications

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“…Furthermore, have been presented some recent research works related to machine learning-based security designs for IoT and Industrial IoT. FARAHNAKIAN and Haikonen [21] presented a deep autoencoder model for identifying network attacks. The researchers used the KDD-CUP 99 dataset to evaluate their proposed design.…”

Section: Related Workmentioning

confidence: 99%

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Improvement of intrusion detection in IoT Networks using a hybrid Machine and Metaheuristic Algorithm

Rahmani,

Arefi,

Seyyed Shojae

et al. 2024

Preprint

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In recent years, extensive research has been conducted on the IoT, one of its challenges has been the field security and penetration in these networks. security solutions it Require more planning and awareness to ensure system security and privacy protection.it has also been shown that adjusting the weights of neural networks can help improve the accuracy of detection to what extent. the challenge in the attack detection field is to increase the accuracy of attack detection by machine learning methods, in this paper, a new method is presented to adjust weights of the random neural network for attack detection. Evaluations of the proposed method show that compared to random neural network methods, nearest neighbor, and support vector machine, the accuracy of attack detection improves up to 99.49 percent, and the random neural network method has been able to improve by 99.01 percent, placing the best methods together in these experiments in the form of a multi-learning model showed that the results also had an increase in accuracy up to 99.56 percent, while the training time in the proposed model is less compared to the random neural network method.

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Section: Related Workmentioning

confidence: 99%

“…A higher detection rate indicates be er system performance. Accuracy is calculated using equation (21), representing how well the system has correctly identified a ack detections. =…”

Section: Source Locationmentioning

confidence: 99%

Improvement of intrusion detection in IoT Networks using a hybrid Machine and Metaheuristic Algorithm

Rahmani,

Arefi,

Seyyed Shojae

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Simply, firewalls are the predecessor of IDS. Even though firewall provides security, it has many shortcomings [12].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Collaborative Trust Framework Based on Hy IDS Firewalls and Mobile Agents to Achieve Effective Security Mechanisms in Cloud Environment

Eddaoui¹,

Marzak²,

Toumi³

et al. 2016

Fourth International Conference on Advances in Information Processing and Communication Technology - IPCT 2016

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Abstract-The cloud has emerged as a successful computing paradigm allowing users and organizations to rely on external providers for storing and processing their data and making them available to others. Undoubtedly, security is one of the significant concerns in cloud computing. However, one of the major security concerns is to protect against network intrusions that affect confidentiality, availability and integrity Cloud resources and offered services. In this paper, we present a new framework, that allows collaboration between Hybrid Intrusion Detection System (Hy-IDS), Firewalls and Mobile Agents. Deploying security mechanisms in cloud computing environment to detect and stop intrusion attempts through our framework. However, the security mechanisms unfolds in four phases: using mobile agents in a virtual environment to collect malicious data in order to detect intrusions. Then, generation of appropriate response actions local and remote from malicious data, which were collected in the first phase. In addition, dynamic deployment of response actions through virtual or physical firewall. Finally, dynamic deployment of updates in cloud computing based on mobile agents. Therefore, the collaborative framework of security management could identify and address new distributed attacks more quickly and effectively

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“…A DoS assault is among the greatest famous kinds of network intrusion, to degrade the service offered by a particular target to other genuine customers [11], [12], [13]. There are various types of DoS attacks, including blackhole, wormholes, flooding, and gray hole [14], [15], [16], [17]. Each leverages a unique security flaw in the network and wreaking havoc on variables like connection interruption, traffic flooding, system interruption, and access blocking in the wireless link [18].…”

Section: Introductionmentioning

confidence: 99%

An Effective Intrusion Detection in Mobile Ad-hoc Network Using Deep Belief Networks and Long Short-Term Memory

Hanafi,

Saheed,

Arowolo

2023

Int. J. Interact. Mob. Technol.

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A Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue operations, environmental monitoring, tactical operations, and so on, because they let people communicate without the usage of permanent infrastructure. This flexibility, however, creates additional security vulnerabilities. Because of its benefits and expanding demand, MANETs have attracted a lot of interest from the scientific community. They do, however, seem to be more vulnerable to numerous attacks that wreak havoc on their performance than any network. Traditional cryptography techniques cannot entirely defend MANETs in terms of fresh attacks and vulnerabilities due to the distributed architecture of MANETs; however, these issues can be overcome by using machine learning approaches-based intrusion detection systems (IDS). IDS, typically screening system processes and identifying intrusions, are commonly employed to supplement existing security methods because preventative techniques are never enough. Because MANETs are continually evolving, their highly limited nodes, and the lack of central observation stations, intrusion detection is a complex and tough process. Conventional IDSs are difficult to apply to them. Existing methodologies must be updated for MANETs or new approaches must be created. This paper aims to present a novel concept founded on deep belief networks (DBN) and long shortterm memory (LSTM) for MANET attack detection. The experimental analysis was performed on the probe, root to local, user to root, and denial of service (DoS) attacks. In the first phase of this paper, particle swarm optimization was used for feature selection, and subsequently, the DBN and LSTM were used for the classification of attacks in the MANET. The experimental results gave an accuracy reaching 99.46%, a sensitivity of 99.52%, and a recall of 99.52% for DBN and LSTM accuracy reaching 99.75%, a sensitivity of 99.79%, and a recall of 99.79%.

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A Survey on Intrusion Detection System: State of the Art Review

Cited by 11 publications

References 36 publications

Improvement of intrusion detection in IoT Networks using a hybrid Machine and Metaheuristic Algorithm

Improvement of intrusion detection in IoT Networks using a hybrid Machine and Metaheuristic Algorithm

Collaborative Trust Framework Based on Hy IDS Firewalls and Mobile Agents to Achieve Effective Security Mechanisms in Cloud Environment

An Effective Intrusion Detection in Mobile Ad-hoc Network Using Deep Belief Networks and Long Short-Term Memory

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