GASF-IPP: Detection and Mitigation of LDoS Attack in SDN

Tang, Dan; Zhang, Jiliang; Liu, Boru; Jin, Wenqiang; Zhang, Jiliang

doi:10.1109/tsc.2023.3266757

Cited by 23 publications

(3 citation statements)

References 42 publications

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“…The experimental results demonstrate the accuracy and efficacy of our proposed framework. The detection and mitigation module exhibits real-time functionality with a minimal false positive rate (FPR) and an average response time of 6.77 seconds (Tang et al, 2023). The platform-centric security system, which operates on the Internet of Things (IoT), can establish real-time communication with the associated equipment.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Survey of 5g Network on Different Technologies in Network Security

Adegbenjo

2023

ijarcs

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The prominence of security has been a central concern in several telecommunications industries in recent years, mostly owing to the potential for substantial consequences linked to dangers. In forthcoming wireless systems, it is expected that confidential information will be sent across several layers, with a special focus on network security and fundamental and supporting technologies. The exposure of several instances has shed light on the potential dangers linked to a hacked wireless network. These risks not only jeopardize security and privacy but also disrupt the complex dynamics of the communication ecosystem. Consequently, there has been a significant increase in the complexity and effectiveness of security breaches in recent years, presenting a global challenge in terms of detecting and preventing acts of sabotage. This article presents a comprehensive analysis of several technologies used in the realm of network security, with particular emphasis on their ramifications for both security and privacy. This article further evaluates the relevant security measures and technologies, using insights from several standardization organizations, and provides a succinct overview of the security entities engaged in standardization. In summary, a segment has been included to promote more academic investigation, focusing on future paths and unresolved issues. This paper provides a succinct overview of the security forces engaged in the process of standardization. In summary, a segment emphasizing prospective avenues and unresolved issues has been included to foster more academic investigation.

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Survey of 5g Network on Different Technologies in Network Security

Adegbenjo

2023

ijarcs

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“…Additionally, a single LDoS attack flow disguised as a legally-formed pulse flow exhibits the same basic characteristics as normal traffic. Its average packet rate is low, 10–20% of normal data traffic, and it often submerges in normal traffic, making it difficult to be detected [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks

Liu

Sun

Zhang

et al. 2023

Sensors

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Currently, Low-Rate Denial of Service (LDoS) attacks are one of the main threats faced by Software-Defined Wireless Sensor Networks (SDWSNs). This type of attack uses a lot of low-rate requests to occupy network resources and hard to detect. An efficient detection method has been proposed for LDoS attacks with the features of small signals. The non-smooth small signals generated by LDoS attacks are analyzed employing the time–frequency analysis method based on Hilbert–Huang Transform (HHT). In this paper, redundant and similar Intrinsic Mode Functions (IMFs) are removed from standard HHT to save computational resources and to eliminate modal mixing. The compressed HHT transformed one-dimensional dataflow features into two-dimensional temporal–spectral features, which are further input into a Convolutional Neural Network (CNN) to detect LDoS attacks. To evaluate the detection performance of the method, various LDoS attacks are simulated in the Network Simulator-3 (NS-3) experimental environment. The experimental results show that the method has 99.8% detection accuracy for complex and diverse LDoS attacks.

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“…However, their deep learning models are insufficiency to learn time sequence features. Thus, Tang et al 11 used Gramian angular summation field transformation to analyze time-based features for finding out the attack, and the results were effective. With the emerging LSTM, researchers integrated it into FL framework.…”

mentioning

confidence: 99%

A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection

Yin,

Fang,

Liu

et al. 2024

Sci Rep

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Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models.

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GASF-IPP: Detection and Mitigation of LDoS Attack in SDN

Cited by 23 publications

References 42 publications

Comparative Survey of 5g Network on Different Technologies in Network Security

Comparative Survey of 5g Network on Different Technologies in Network Security

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks

A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection

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