Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction DOI Creative Commons
Yongchao Yang, Pan Zhao

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 24014 - 24026

Published: Jan. 1, 2024

Network security situation awareness enables networks to actively and effectively defend against network attacks, relying on the extraction of elements as an initial decisive step. In existing studies, stacked sparse autoencoder (SSAE) has been employed extract features from unlabeled flows. However, obtaining optimal hyperparameter combination is challenging due its numerous hyperparameters. To address this issue, we propose a novel approach named DBO-SSAE that leverages dung beetle optimization (DBO) select hyperparameters for SSAE automatically. Applied well-known UNSW-NB15 dataset, our model yields feature subset, which evaluated across various binary classifiers with different metrics. Experimental results demonstrate improves accuracy xmlns:xlink="http://www.w3.org/1999/xlink">F 1- xmlns:xlink="http://www.w3.org/1999/xlink">measure by 0.2% 1.5%, while reducing xmlns:xlink="http://www.w3.org/1999/xlink">false negative rate (FNR) positive (FPR) 0.06% 7%, surpassing other methods same classifier dataset. Particularly, in conjunction lightweight bidirectional long short-term memory (BiLSTM), achieves metrics 98.84% , 98.96% 1.86% xmlns:xlink="http://www.w3.org/1999/xlink">FNR 0.6% xmlns:xlink="http://www.w3.org/1999/xlink">FPR . This study could provide insights into effective representation lay groundwork high-efficiency intrusion detection system.

Language: Английский

A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks DOI Creative Commons
Mohaimenul Azam Khan Raiaan, Sadman Sakib, Nur Mohammad Fahad

et al.

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100470 - 100470

Published: April 24, 2024

Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming researchers, therefore we need efficient optimization techniques. In this systematic review, explore range of well used algorithms, including metaheuristic, statistical, sequential, numerical approaches, to fine-tune hyperparameters. Our offers an exhaustive categorization (HPO) algorithms investigates the fundamental concepts CNN, explaining role variants. Furthermore, literature review HPO employing above mentioned undertaken. A comparative analysis conducted based strategies, error evaluation accuracy results across various datasets assess efficacy methods. addition addressing current challenges HPO, our illuminates unresolved issues field. By providing insightful evaluations merits demerits objective assist researchers determining suitable method particular problem dataset. highlighting future directions synthesizing diversified knowledge, survey contributes significantly ongoing development optimization.

Language: Английский

Citations

48
Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models DOI Creative Commons
Dusmurod Kilichev, Dilmurod Turimov, Wooseong Kim

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(4), P. 571 - 571

Published: Feb. 14, 2024

In the evolving landscape of Internet Things (IoT) and Industrial IoT (IIoT) security, novel efficient intrusion detection systems (IDSs) are paramount. this article, we present a groundbreaking approach to for IoT-based electric vehicle charging stations (EVCS), integrating robust capabilities convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU) models. The proposed framework leverages comprehensive real-world cybersecurity dataset, specifically tailored IIoT applications, address intricate challenges faced by EVCS. We conducted extensive testing in both binary multiclass scenarios. results remarkable, demonstrating perfect 100% accuracy classification, an impressive 97.44% six-class 96.90% fifteen-class setting new benchmarks field. These achievements underscore efficacy CNN-LSTM-GRU ensemble architecture creating resilient adaptive IDS infrastructures. algorithm, accessible via GitHub, represents significant stride fortifying EVCS against diverse array threats.

Language: Английский

Citations

20
Advanced Temporal Deep Learning Framework for Enhanced Predictive Modeling in Industrial Treatment Systems DOI Creative Commons

S Ramya,

S Srinath,

Pushpa Tuppad

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104158 - 104158

Published: Jan. 1, 2025

Language: Английский

Citations

1
Explainable TabNet Transformer-based on Google Vizier Optimizer for Anomaly Intrusion Detection System DOI
Ibrahim A. Fares, Mohamed Abd Elaziz

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113351 - 113351

Published: March 1, 2025

Language: Английский

Citations

1
Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network DOI Creative Commons
Fatemeh Makhloughi

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 4, 2025

Language: Английский

Citations

1
Assessing landscape ecological vulnerability to riverbank erosion in the Middle Brahmaputra floodplains of Assam, India using machine learning algorithms DOI Open Access
Nirsobha Bhuyan, Haroon Sajjad, Tamal Kanti Saha

et al.

CATENA, Journal Year: 2023, Volume and Issue: 234, P. 107581 - 107581

Published: Oct. 9, 2023

Language: Английский

Citations

17
A Novel Approach for Real-Time Server-Based Attack Detection Using Meta-Learning DOI Creative Commons
Furqan Rustam, Ali Raza,

Muhammad Qasim

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 39614 - 39627

Published: Jan. 1, 2024

Modern networks are crucial for seamless connectivity but face various threats, including disruptive network attacks, which can result in significant financial and reputational risks. To counter these challenges, AI-based techniques being explored protection, requiring high-quality datasets training. In this study, we present a novel methodology utilizing Ubuntu Base Server to simulate virtual environment real-time collection of attack datasets. By employing Kali Linux as the attacker machine Wireshark data capture, compile Server-based Network Attack (SNA) dataset, showcasing UDP, SYN, HTTP flood attacks. Our primary goal is provide publicly accessible, server-focused dataset tailored research. Additionally, leverage advanced AI methods detection proposed meta-RF-GNB (MRG) model combines Gaussian Naive Bayes Random Forest predictions, achieving an impressive accuracy score 99.99%. We validate efficiency MRG using cross-validation, obtaining notable mean 99.94% with minimal standard deviation 0.00002. Furthermore, conducted statistical t-test evaluate significance compared other top-performing models.

Language: Английский

Citations

7
Intrusion detection systems for IoT based on bio-inspired and machine learning techniques: a systematic review of the literature DOI

Rafika Saadouni,

Chirihane Gherbi, Zibouda Aliouat

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(7), P. 8655 - 8681

Published: April 14, 2024

Language: Английский

Citations

7
Enhancing Network Intrusion Detection Through the Application of the Dung Beetle Optimized Fusion Model DOI Creative Commons
Yue Li, Jiale Zhang,

Yiting Yan

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 9483 - 9496

Published: Jan. 1, 2024

With the rapid development of information communication and mobile device technologies, smart devices have become increasingly popular, providing convenience to households enhancing level intelligence in daily life. This trend is also driving innovation progress various fields, including healthcare, transportation, industry. However, as technology continues proliferate, network security concerns prominent, making protection digital life data an urgent priority. Intrusion detection has always played important role field security. Traditional intrusion systems predominantly rely on anomaly identify potential intrusions by detecting abnormal patterns traffic. technological advancements, machine learning-based methods emerged cornerstone modern detection, enabling more precise identification behaviors learning normal In response these challenges, this paper introduces innovative model that amalgamates Attention-CNN-BiLSTM (ACBL) Temporal Convolutional Network (TCN) architectures. The ACBL TCN models excel processing spatial temporal features within traffic data, respectively. integration harnesses diverse neural structures elevate overall performance accuracy. Furthermore, a unique approach inspired dung beetles' natural behavior, incorporating Tent mapping-enhanced Dung Beetle Optimization Algorithm (TDBO), leveraged for both optimizing feature selection parameters searching optimal hyperparameters. obtained from TDBO are then combined with importance ranking Random Forest algorithm, ensuring can be better selected enhance performance. novel model, TDBO-ACBLT validates its using UNSW-NW15 dataset. excels compared common algorithms achieves superior parameter optimization accuracy over Harris's Hawk (HHO), Particle Swarm (PSO), (DBO). proposed higher than prevalent models.

Language: Английский

Citations

5
SoK: quantum computing methods for machine learning optimization DOI
Hamza Baniata

Quantum Machine Intelligence, Journal Year: 2024, Volume and Issue: 6(2)

Published: July 24, 2024

Language: Английский

Citations

5