IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

Wang, Jiushuang; Liu, Ying; Feng, Huifen

doi:10.3934/mbe.2022059

Cited by 18 publications

(7 citation statements)

References 32 publications

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“…Previous findings suggest that the basic FA exhibits relatively efficient exploitation, while its exploration abilities can be improved [56][57][58]. Notwithstanding that many successful implementations of enhanced/hybridized FA's version can be found in the modern literature [31,59], space for its improvements still exists. This stems from the fact that the FA's search equation, which conducts efficient intensification, can be effectively combined with novel mechanisms, as well as with procedures from other metaheuristics, in a wide variety of ways, and the practical potential for improvements is unlimited.…”

Section: Motivation and Proposed Improved Group Search Firefly Algorithmmentioning

confidence: 99%

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Jovanovic¹,

Antonijevic

Stanković

et al. 2022

Mathematics

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Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.

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Section: Motivation and Proposed Improved Group Search Firefly Algorithmmentioning

confidence: 99%

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Jovanovic¹,

Antonijevic

Stanković

et al. 2022

Mathematics

View full text Add to dashboard Cite

show abstract

“…They used an updated firefly method to find DDoS attacks to enhance the convolutional neural network (CNN). The findings showed that the proposed technique could detect innocuous traffic and DDoS activities with more than 99 percent [4]. Depending on the information entropy and deep learning, Liu et al suggested a two-level DDoS attack detection approach.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Amplification attacks, such as DNS reflection amplification attacks, can also be performed via reflection. Protocol vulnerabilities are commonly used in system resource attacks to use the victim's host resources (TCP-SYN half-connection attack employing TCP three-way handshake, for example [4]).…”

Section: Introductionmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

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“…According to the recent research, the original FA implementation demonstrates very efficient exploitation, but the exploration could be enhanced as the algorithm can converge prematurely in some runs (Bacanin, Bezdan, Venkatachalam, and Al‐Turjman, 2021; Bezdan, Cvetnic, et al, 2021; Wang et al, 2017). Although there are numerous modified and enhanced variations of the FA's basic algorithm in the recent literature (Bacanin, Zivkovic, Bezdan, et al, 2022; Wang et al, 2022), there is still plenty of space for new improvements. Therefore, research reported in this manuscript proposes FA version that has been enhanced with a mechanism that ensures better diversification of solutions during the algorithm's run.…”

Section: Introductionmentioning

confidence: 99%

Machine learning tuning by diversity oriented firefly metaheuristics for Industry 4.0

et al. 2023

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The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one of the main contributors is the Internet of Things. Smart factories and healthcare have both benefited in terms of leveraged quality of service and productivity rate. However, there is always a trade‐off and some of the largest concerns include security, intrusion, and failure detection, due to high dependence on the Internet of Things devices. To overcome these and other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion detection, computer‐aided diagnostics, and so forth. However, efficiency of machine learning models heavily depend on feature selection, predetermined values of hyper‐parameters and training to deliver a desired result. This paper proposes a swarm intelligence‐based approach to tune the machine learning models. A novel version of the firefly algorithm, that overcomes known deficiencies of original method by employing diversification‐based mechanism, has been proposed and applied to both feature selection and hyper‐parameter optimization of two machine learning models—XGBoost and extreme learning machine. The proposed approach has been tested on four real‐world Industry 4.0 data sets, namely distributed transformer monitoring, elderly fall prediction, BoT‐IoT, and UNSW‐NB 15. Achieved results have been compared to the results of eight other cutting‐edge metaheuristics, that have been implemented and tested under the same conditions. The experimental outcomes strongly indicate that the proposed approach significantly outperformed all other competitor metaheuristics in terms of convergence speed and results' quality measured with standard metrics—accuracy, precision, recall, and f1‐score.

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IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

Cited by 18 publications

References 32 publications

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Machine learning tuning by diversity oriented firefly metaheuristics for Industry 4.0

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