Threats and vulnerabilities on TCP/OBS networks

Sreenath, N.; Muthuraj, K.; Kuzhandaivelu, G. Vinoth

doi:10.1109/iccci.2012.6158832

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…By encrypting data bursts at the ingress edge router and decrypting at the egress edge router the data bursts switch transparently across the OBS core routers, so the end-to-end burst confidentiality within the OBS domain is provided [ 8 ]. In this OBS technique, when a burst is passed from ingress node to egress node through many intermediate core nodes, it can be possibly stolen by the attacker nodes [ 9 ]. How the attacker nodes use the various methods to steal the burst and how we can prevent this from happening are discussed below.…”

Section: End-to-end Data Burst Confidentialitymentioning

confidence: 99%

Quantum Key Based Burst Confidentiality in Optical Burst Switched Networks

Balamurugan

Sivasubramanian

2014

The Scientific World Journal

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The optical burst switching (OBS) is an emergent result to the technology concern that could achieve a feasible network in future. They are endowed with the ability to meet the bandwidth requirement of those applications that require intensive bandwidth. There are more domains opening up in the OBS that evidently shows their advantages and their capability to face the future network traffic. However, the concept of OBS is still far from perfection facing issues in case of security threat. The transfer of optical switching paradigm to optical burst switching faces serious downfall in the fields of burst aggregation, routing, authentication, dispute resolution, and quality of service (QoS). This paper deals with employing RC4 (stream cipher) to encrypt and decrypt bursts thereby ensuring the confidentiality of the burst. Although the use of AES algorithm has already been proposed for the same issue, by contrasting the two algorithms under the parameters of burst encryption and decryption time, end-to-end delay, it was found that RC4 provided better results. This paper looks to provide a better solution for the confidentiality of the burst in OBS networks.

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Section: End-to-end Data Burst Confidentialitymentioning

confidence: 99%

Quantum Key Based Burst Confidentiality in Optical Burst Switched Networks

Balamurugan

Sivasubramanian

2014

The Scientific World Journal

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“…In the BHP flooding attack, malicious nodes send a large number of BHPs to the network which affect the bandwidth utilization [7,8]. These BHPs take over the core switch and maliciously occupy the idle wavelength division multiplexing channels, making it impossible for normal BHPs to transmit.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, this target node is unable to transmit legitimate BHPs and discards the upcoming legitimate BHPs, in which reserved channel resources are wasted. In extreme cases, these attacks can also lead to a serious denial of service [7]. As OBS networks have been intensively studied, their network security has also received much attention.…”

Section: Introductionmentioning

confidence: 99%

A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks

2021

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An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security.

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“…is function can be exploited by attackers to send fake BHPs without DB acknowledgment. Such fake BHPs can affect the network and reduce its performance through decreasing bandwidth utilization and increasing data loss, leading to a denial of service (DoS) attack [3], which is one of the most crucial security threats to networks.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Effective Approach for Flooding Attack Detection in Optical Burst Switching Networks

ِAlmaslukh

2020

Security and Communication Networks

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Optical burst switching (OBS) networks are frequently compromised by attackers who can flood the networks with burst header packets (BHPs), causing a denial of service (DoS) attack, also known as a BHP flooding attack. Nowadays, a set of machine learning (ML) methods have been embedded into OBS core switches to detect these BHP flooding attacks. However, due to the redundant features of BHP data and the limited capability of OBS core switches, the existing technology still requires major improvements to work effectively and efficiently. In this paper, an efficient and effective ML-based security approach is proposed for detecting BHP flooding attacks. The proposed approach consists of a feature selection phase and a classification phase. The feature selection phase uses the information gain (IG) method to select the most important features, enhancing the efficiency of detection. For the classification phase, a decision tree (DT) classifier is used to build the model based on the selected features of BHPs, reducing the overfitting problem and improving the accuracy of detection. A set of experiments are conducted on a public dataset of OBS networks using 10-fold cross-validation and holdout techniques. Experimental results show that the proposed approach achieved the highest possible classification accuracy of 100% by using only three features.

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Threats and vulnerabilities on TCP/OBS networks

Cited by 10 publications

References 8 publications

Quantum Key Based Burst Confidentiality in Optical Burst Switched Networks

Quantum Key Based Burst Confidentiality in Optical Burst Switched Networks

A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks

An Efficient and Effective Approach for Flooding Attack Detection in Optical Burst Switching Networks

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