An Intrusion Detection System for SDN Using Machine Learning

Logeswari, G.; Bose, Sanjay K.; Anitha, T.

doi:10.32604/iasc.2023.026769

Cited by 54 publications

(18 citation statements)

References 20 publications

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“…Security of communication can be provided by using various methods including access control [53], optimization-based secure routing techniques [54], agent-based methods [55], temporal analysis [56], intrusion detection techniques developed for feature selection and classifcation, key management techniques, encryption and decryption methods, trust management techniques, frewalls, and application considerations [57][58][59][60][61][62][63][64][65]. Various authors have proposed a variety of mechanisms for providing security in IoT environments through IDS.…”

Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day-to-day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality-of-service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning-based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.

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Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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“…D. Musleh et al [18] innovatively applied feature extraction techniques using VGG-16 and DenseNet on intrusion datasets and, through the employment of ML models such as Random Forest, K-Nearest Neighbors, and Support Vector Machine (SVM), achieved an accuracy of 92.40%. Other notable IDSs, including those developed by G. Logeswari et al [19], J. O. Mebawondu et al [20], and A. Abdelkhalek et al [21], have reported accuracies of 82.20%, 76.96%, and 83.50%, respectively. Moreover, the Secured Automatic Two-level Intrusion Detection System (SATIDS) introduced by A. R. Elsayed et al [22] showcases a remarkable accuracy of 96.56%.…”

Section: Literature Reviewmentioning

confidence: 98%

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

Racherla,

Sripathi,

Faruqui

et al. 2024

IEEE Access

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

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“…The model maps the data records in the database into predefined categories for forecasting. Such as in [87] and [88], where it is shown that RF is a good classification algorithm for IDS. And in [89], it is argued that SVM outperforms other classification algorithms in comparison to SVM.…”

Section: Feature Extraction and Classifiersmentioning

confidence: 99%

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

Liao,

Murah,

Hasan

et al. 2024

IEEE Access

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Internet of Things (IoT) is transforming how we live and work and its applications are widespread, spanning smart homes, industrial monitoring, smart cities, healthcare, agriculture, and retail Considering its wide range applications, it is vital to address the security challenges arising from the massive collection and transmission of user data by IoT devices. Intrusion detection systems (IDS) based on deep learning techniques offer new means and research directions for resolving IoT security issues. Deep learning models can process large volumes of data and extract complex patterns, making them generally more effective than traditional rule based IDSs. While deep learning techniques are gradually gaining popularity in IDS applications, current research lacks a comprehensive summary of deep learning-based IDS in the context of IoT. This paper provides an introduction to intrusion detection technologies, followed by a detailed comparison, analysis, and discussion of deep learning models, datasets, feature extraction and classifiers, data preprocessing techniques, and experimental design of the models. It also highlights the current challenges and issues associated with deep learning models and relevant techniques for IDS. Finally, it concludes by providing recommendations to assist researchers in this domain.

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An Intrusion Detection System for SDN Using Machine Learning

Cited by 54 publications

References 20 publications

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

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

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

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