Towards Effective Detection of Recent DDoS Attacks: A Deep Learning Approach

Lopes, Ivandro Ortet; Zou, Deqing; Ruambo, Francis Aidan; Akbar, Saeed; Yuan, Bin

doi:10.1155/2021/5710028

Cited by 26 publications

(10 citation statements)

References 32 publications

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“…The results obtained from the proposed hybrid model are shown in Table 3 and Figure 15, which show an accuracy of 0.9981, precision of 0.9996, recall of 0.999, and an F1 score of 0.9993. These results outperformed several existing models such as GRUs [13], CyDDoS [30], DDoSNet [31], Meta-classification [32], Convolutional Neural Networks [33], and Klamn backpropagation NN [34]. The accuracy of the proposed model was higher than all other models, including those that used deep learning techniques.…”

Section: Comparison With Other Methodsmentioning

confidence: 75%

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

Elubeyd

Yiltas-Kaplan

2023

Applied Sciences

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This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.

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Section: Comparison With Other Methodsmentioning

confidence: 75%

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

Elubeyd

Yiltas-Kaplan

2023

Applied Sciences

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“…Yet, substantial progress in the battle over DDOS still needs to be made. [19] DNN 99.6% accuracy and a ROC value close to 1.…”

Section: Algorithmmentioning

confidence: 88%

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

2023

Preprint

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Wireless sensor networks (WSN) are rapidly growing in popularity, and their flexibility and ease of implementation cause an increasing number of security issues, making it critical to research network intrusion defense for wireless sensor networks. Denial of service (DoS) is a cyber-attack that shuts down the targeted network. A DoS attack on a WSN device will be fatal. It is prone to malicious attacks and difficult to prevent because every single node is independent of the others, yet there's no central or monitoring node, which is tough to avoid. Numerous lightweight authentication systems have been utilized in real-time to ensure encrypted communication. However, with the lack of synchronization between nodes during data routing, WSNs are highly prone to Denial of Service (DoS) attacks. This article combines the Adaptive Sunflower Optimization (ASFO) method with an improved Deep Convolutional Neural Network (IDCNN) to enhance the degree of security against DoS attacks on WSNs term ASFO-IDCNN. This paper initially utilizes the ASFO method to improve the initial values of IDCNN to prevent getting into the local optimum. Then, the ASFO-IDCNN technique is used to detect intrusions in WSNs. The results of numerous simulated situations are shown, and the associated data is compared. DoS protection research is precious in analyzing the anti-attack efficiency of WSN nodes. The influence of DoS attacks on the functioning of WSNs is considered in this research.

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“…The authors in [16] underline the rising expertise and motivation of cybercriminals, who target computer users using sophisticated tactics and social engineering schemes. To combat these attacks, improved IDS are critical for successfully identifying contemporary malware.…”

Section: Related Workmentioning

confidence: 99%

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

Khanan,

Abdelgadir Mohamed,

Mohamed

et al. 2024

IEEE Access

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In the wake of the expanding digital realm, the imperative for robust cybersecurity measures has burgeoned significantly. This extensive investigation digs into the complicated realm of cybersecurity datasets, with the goal of improving our understanding and implementation of these critical tools. This study's comprehensive evaluation of 37 distinct datasets shows a complicated world in which no one dataset stands out as totally suitable for all uses. A precise balance must be struck between crucial dataset qualities such as diversity, authenticity, and usefulness. Using a complete assessment technique, this paper illuminates the challenges and possibilities that developers and researchers face in the field of cybersecurity datasets. Although some databases accurately identify certain forms of cyberattacks, their coverage may not include the whole range of cyber threats. On the other hand, datasets with a strong emphasis on accurate portrayal may forgo comprehensiveness or practical use. This intricacy is heightened by the dynamic and sophisticated nature of cyber threats, emphasizing the delicate balance required between accuracy and practicality. The study emphasizes the necessity of selecting datasets strategically and contextually for cybersecurity studies, with the goal of matching research objectives with the most appropriate dataset selections. Furthermore, it emphasizes the need of continual cooperation and innovation within the cybersecurity community in developing datasets that accurately represent the ever-changing nature of cyber threats. After analyzing 37 cybersecurity datasets, it is obvious that no one dataset can meet all of the field's unique demands, demonstrating the need of a flexible, adaptable, and developing dataset for intrusion detection systems (IDS). This inquiry offers a critical assessment of dataset characteristics and their related issues, providing essential insights for academics, professionals, and dataset creators, enabling the construction of a more resilient and adaptable cybersecurity infrastructure.

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Towards Effective Detection of Recent DDoS Attacks: A Deep Learning Approach

Cited by 26 publications

References 32 publications

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

From Bytes to Insights: A Systematic Literature Review on Unraveling IDS Datasets for Enhanced Cybersecurity Understanding

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