Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning DOI Creative Commons

Naseer Hameed Saadoon Al-Sarray,

Javad Rahebi, Ayşe Demi̇rhan

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 19, 2024

Abstract The Software-defined Networking (SDN) system plays a crucial role in efficiently overseeing the Internet network by segregating control and data planes. In SDN, controller manages determines policy sending setting SDN switches. Despite significant advantages, has security challenges. DDoS attacks are main challenge networks. primarily target to disrupt performance. Intrusion detection systems networks need confidential methods for message exchange coordination of controllers so that they can blacklist attacking addresses with each other. this manuscript, we introduce an approach utilizing 1D CNN LSTM detecting network, incorporating information hidden images. first stage, game theory deep learning based on GAN used increase attack accuracy balance set. second uses extract primary features, Siberian tiger optimization (STO) algorithm is applied enhance efficiency network. third step, STO selects optimal features. Finally, classifies traffic receiving selected use image encryption privacy exchanging sharing blacklists. tests performed Python datasets UNSW-NB15, CIC-IDS2017, NSL-KDD 99.49%, 99.86%, 99.91%. proposed method GAN-CL-STO demonstrates higher compared CNN-LSTM, HODNN+CRF, CNN, PSO-1D CNN+BiLSTM methods. suggested identifying more accurate than WOA, HHO, COA feature selection

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

43
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

16
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
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
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

15
Advancing Network Security with AI: SVM-Based Deep Learning for Intrusion Detection DOI Creative Commons
Khadija M. Abuali, Liyth Nissirat, Aida Al‐Samawi

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(21), P. 8959 - 8959

Published: Nov. 3, 2023

With the rapid growth of social media networks and internet accessibility, most businesses are becoming vulnerable to a wide range threats attacks. Thus, intrusion detection systems (IDSs) considered one essential components for securing organizational networks. They first line defense against online responsible quickly identifying potential network intrusions. Mainly, IDSs analyze traffic detect any malicious activities in network. Today, expanding tremendously as demand services is expanding. This expansion leads diverse data types complexities network, which may limit applicability developed algorithms. Moreover, viruses attacks changing their quantity quality. Therefore, recently, several security researchers have using innovative techniques, including artificial intelligence methods. work aims propose support vector machine (SVM)-based deep learning system that will classify extracted from servers determine incidents on media. To implement learning-based multiclass classification, CSE-CIC-IDS 2018 dataset has been used evaluation. The was subjected preprocessing techniques prepare it training phase. proposed model implemented 100,000 instances sample dataset. study demonstrated accuracy, true-positive recall, precision, specificity, false-positive F-score were 100%, 0%, respectively.

Language: Английский

Citations

13
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
Optimizing Smart Home Intrusion Detection With Harmony-Enhanced Extra Trees DOI Creative Commons
Akmalbek Abdusalomov, Dusmurod Kilichev, Rashid Nasimov

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 117761 - 117786

Published: Jan. 1, 2024

In this study, we present an innovative network intrusion detection system (IDS) tailored for Internet of Things (IoT)-based smart home environments, offering a novel deployment scheme that addresses the full spectrum security challenges. Distinct from existing approaches, our comprehensive strategy not only proposes model but also incorporates IoT devices as potential vectors in cyber threat landscape, consideration often neglected previous research. Utilizing harmony search algorithm (HSA), refined extra trees classifier (ETC) by optimizing extensive array hyperparameters, achieving level sophistication and performance enhancement surpasses typical methodologies. Our was rigorously evaluated using robust real-time dataset, uniquely gathered 105 devices, reflecting more authentic complex scenario compared to simulated or limited datasets prevalent literature. commitment collaborative progress cybersecurity is demonstrated through public release source code. The underwent exhaustive testing 2-class, 8-class, 34-class configurations, showcasing superior accuracy (99.87%, 99.51%, 99.49%), precision (97.41%, 96.02%, 96.07%), recall (98.45%, 87.14%, 87.1%), f1-scores (97.92%, 90.65%, 90.61%) firmly establish its efficacy. Thiswork marks significant advancement security, providing scalable effective IDS solution adaptable intricate dynamics modern networks. findings pave way future endeavors realm defense, ensuring homes remain safe havens era digital vulnerability.

Language: Английский

Citations

4