Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

IEEE Access, Год журнала: 2024, Номер 12, С. 63584 - 63597

Опубликована: Янв. 1, 2024

The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation today's smart services. However, inherent resource-constrained nature IoT nodes poses significant challenges embedding advanced algorithms for cybersecurity, leading to an escalation cyberattacks against these nodes. Contemporary research Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, innovative and practically deployable Deep Learning (DL)-based IDS. It employs Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units trained CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS ideal candidate edge-server deployment, acting as guardian between Denial Service (DoS), Distributed (DDoS), Brute Force (BRF), Man-in-the-Middle (MITM), Replay (RP) Attacks. A distinctive aspect this trade-off analysis intrusion detection rate false alarm rate, facilitating real-time Deep-IDS. system demonstrates exemplary 96.8% overall classification accuracy 97.67%. Furthermore, achieves precision, recall, F1-scores 97.67%, 98.17%, 97.91%, respectively. On average, requires 1.49 seconds identify mitigate attempts, effectively blocking malicious traffic sources. remarkable efficacy, swift response time, design, novel defense strategy not only secure but also interconnected sub-networks, thereby positioning IoT-enhanced computer networks.

Язык: Английский

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MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

Internet of Things, Год журнала: 2024, Номер 26, С. 101231 - 101231

Опубликована: Май 19, 2024

Язык: Английский

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Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

IEEE Access, Год журнала: 2024, Номер 12, С. 82443 - 82468

Опубликована: Янв. 1, 2024

ABSTRACT Background and Motivation: The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount data generated by IoT devices creates larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. comprised is multivariate, highly complex, non-stationary, nonlinear. extract complex patterns from data, we require most robust, optimized tools. Methods: Machine learning (ML) deep (DL) emerged as powerful tools IDSs, offering high accuracy detecting preventing security breaches. This research delves into anomaly detection, technique that identifies deviations normal system behavior, potentially indicating attacks. Given complexity explore methods to improve performance. Proposed Approach: investigates design evaluation novel IDS. We leverage optimize supervised ML like tree-based Support Vector Machines (SVM), ensemble methods, neural networks (NN) alongside cutting-edge DL approach long short-term memory (LSTM) vision transformers (ViT). hyperparameters algorithms using Bayesian optimization approach. Results: implemented models achieved impressive training accuracy, with Random Forest Ensemble Bagged Tree surpassing 99.90% an AUC 1.00, F1-score, balanced Matthews Correlation Coefficient (MCC) 99.78%. initial LSTM model yielded 99.97%, proposed ViT architecture significantly boosted performance 100% all metrics, along validation 78.70% perfect accuracy. Conclusion: Our findings demonstrate potential enhanced detection. improved capability be instrumental safeguarding integrity, identifying fraudulent activity, optimizing networks.

Язык: Английский

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A Novel Data Preprocessing Model for Lightweight Sensory IoT Intrusion Detection

International Journal of Mathematical Engineering and Management Sciences, Год журнала: 2024, Номер 9(1), С. 188 - 204

Опубликована: Янв. 14, 2024

IoT devices or sensor nodes are essential components of the machine learning (ML) application workflow because they gather abundant information for building models with sensors. Uncontrollable factors may impact this process and add inaccuracies to data, raising cost computational resources data preparation. Choosing best method pre-processing stage can lessen complexity ML wasteful bandwidth use cloud processing. Devices in ecosystem limited provide an easy target attackers, who make these create botnets spread malware. To repel attacks directed towards IoT, robust lightweight intrusion detection systems need hour. Furthermore, preprocessing remains first step modish models, ensemble techniques, hybrid methods developing anti-intrusion applications IoT. This article proposes a novel model as core structure using Extra Tree classifier feature selection two classifiers LSTM 1D-CNN classification. The dataset used research is CIC 2023 34 attack classes SMOTE (Synthetic Memory Oversampling Technique) has been class balancing. evaluates performance on 23 classification metrics. proposed approach obtained 92% accuracy 99.87% accuracy.

Язык: Английский

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Sql injection detection algorithm based on Bi-LSTM and integrated feature selection

The Journal of Supercomputing, Год журнала: 2025, Номер 81(4)

Опубликована: Март 12, 2025

Язык: Английский

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Advanced intrusion detection in internet of things using graph attention networks

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 21, 2025

Internet of Things (IoT) denotes a system interconnected devices equipped with processors, sensors, and actuators that capture exchange meaningful data other smart systems. IoT technology has been successfully applied across various sectors, including agriculture, supply chain management, education, healthcare, traffic control, utility services. However, the diverse range nodes introduces significant security challenges. Common safety features like encryption, authentication, access control frequently fall short meeting their desired functions. In this paper, we present novel perspective to by using Graph-based (GB) algorithm construct graph is evaluated graph-based learning Intrusion Detection System (IDS) incorporating Graph Attention Network (GAT). addition, leveraged small benchmark NSL-KDD dataset conduct detailed performance evaluation GNN model focusing on essential key metrics such as F1-score, recall, accuracy, precision guarantee comprehensive analysis. Our findings validate effectiveness GNN-based IDS in detecting intrusions, which highlights its robustness scalability mitigating evolving challenges within

Язык: Английский

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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 23, 2025

Abstract Detecting attacks in 5G software-defined network (SDN) environments requires a comprehensive approach that leverages traditional security measures, such as firewalls, intrusion prevention systems, and specialized techniques personalized to the unique characteristics of network. The attack detection SDN involves Machine learning (ML) Deep (DL) algorithms analyze large volumes data identify patterns indicative attacks. study’s main objective is develop an efficient DL model improve performance respond breaches effectively environment. integrates Particle Swarm Optimizer-Gated Recurrent Unit Layer-Generative Adversarial Network-Intrusion Detection System classifier (PSO-GRUGAN-IDS). PSO optimizes weight GAN backpropagation while generating synthetic (attack data) generator using GRU. discriminator uses PSO-optimized produce real forecast attack. Finally, deep classification (IDS) trained GRU with model-produced classify whether traffic malicious or normal. Moreover, this evaluated InSDN dataset compared existing model-based approaches results demonstrate significantly higher accuracy rate 98.4%, precision 98%, recall 98.5%, less time 2.464 s, lesser Log loss 1.0 more metrics instilling confidence effectiveness proposed method.

Язык: Английский

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Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

AI, Год журнала: 2024, Номер 5(2), С. 704 - 732

Опубликована: Май 15, 2024

The Internet of Things (IoT) is seeing significant growth, as the quantity interconnected devices in communication networks on rise. increased connectivity has heightened their susceptibility to hackers, underscoring need safeguard IoT devices. This research investigates cybersecurity context Medical (IoMT), which encompasses mechanisms used for various healthcare connected system. study seeks provide a concise overview several artificial intelligence (AI)-based methodologies and techniques, well examining associated solution approaches systems. analyzed are further categorized into four groups: machine learning (ML) deep (DL) combination ML DL Transformer-based other state-of-the-art including graph-based methods blockchain methods. In addition, this article presents detailed description benchmark datasets that recommended use intrusion detection systems (IDS) both IoMT networks. Moreover, primary evaluation metrics analysis discussed models provided. Ultimately, thoroughly examines analyzes features practicality models, while also emphasizing recent directions.

Язык: Английский

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Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning

Journal of Electrical and Computer Engineering, Год журнала: 2024, Номер 2024, С. 1 - 21

Опубликована: Июнь 1, 2024

The security of communication networks can be compromised through both known and novel attack methods. Protection against such attacks may achieved the use an intrusion detection system (IDS), which designed by training machine learning models to detect cyberattacks. In this paper, KOMIG (knapsack optimization mutual information gain) IDS was developed network intrusions. combined strengths together achieve a high performance. Specifically, comprises 2-stage feature selection procedure; first accomplished with knapsack algorithm second gain filter. particular, we model for most important features from dataset. Then, new set synthesized selected form candidate set. Next, applied filter prune out redundant features, leaving only that possess maximum gain, were used train models. proposed UNSW-NB15 dataset, is well-known evaluation resulting data, after operation, four models, namely, logistic regression (LR), random forest (RF), decision tree (DT), K-nearest neighbors (KNN). Simulation experiments conducted, results revealed our KNN-based outperformed comparative schemes achieving accuracy score 97.14%, recall 99.46%, precision 95.53%, F1 97.46%.

Язык: Английский

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Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey

Frontiers in Computer Science, Год журнала: 2024, Номер 6

Опубликована: Июнь 10, 2024

The advancement of communication and internet technology has brought risks to network security. Thus, Intrusion Detection Systems (IDS) was developed combat malicious attacks. However, IDSs still struggle with accuracy, false alarms, detecting new intrusions. Therefore, organizations are using Machine Learning (ML) Deep (DL) algorithms in IDS for more accurate attack detection. This paper provides an overview IDS, including its classes methods, the detected attacks as well dataset, metrics, performance indicators used. A thorough examination recent publications on IDS-based solutions is conducted, evaluating their strengths weaknesses, a discussion potential implications, research challenges, trends. We believe that this comprehensive review covers most advances developments ML DL-based also facilitates future into emerging Artificial Intelligence (AI) address growing complexity cybersecurity challenges.

Язык: Английский

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