Intrusion Detection System for SDN-enabled IoT Networks using Machine Learning Techniques

Ashraf, Javed; Moustafa, Nour; Bukhshi, Asim D; Javed, Abdullah

doi:10.1109/edocw52865.2021.00031

Cited by 12 publications

(8 citation statements)

References 23 publications

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“…The botnet C&C servers are typically Dynamic DNS (DDNS) providers. Furthermore, researchers have created IDSs to detect botnets [158][159][160]. A potential challenge in transferring these strategies to the SD-SG is the need to manage the additional overhead and optimize the placement of honeypot instances, as well as the machine learning (ML) and intrusion detection systems (IDS).…”

Section: Controller Botnet Attacksmentioning

confidence: 99%

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

Agnew,

Boamah,

Bretas

et al. 2024

Preprint

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Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

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Section: Controller Botnet Attacksmentioning

confidence: 99%

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

Agnew,

Boamah,

Bretas

et al. 2024

Preprint

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“…Software Defined Network (SDN) has opened many new possibilities for researchers to address the limited view of the data in traditional network devices [7,8]. In SDN, the control plane and data plane have been decoupled.…”

Section: Figure 1: Anti Intrusion Techniquesmentioning

confidence: 99%

Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions

Kumar¹,

Alqahtani²

2023

Computer Modeling in Engineering &Amp; Sciences

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Software-Defined Networking (SDN) enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions. Recently Machine Learning (ML) techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems (IDSs) considering logically centralized control and global view of the network provided by SDN. Many IDSs have developed using advances in machine learning and deep learning. This study presents a comprehensive review of recent work of ML-based IDS in context to SDN. It presents a comprehensive study of the existing review papers in the field. It is followed by introducing intrusion detection, ML techniques and their types. Specifically, we present a systematic study of recent works, discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN, and promising future works in this field.

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“…Ashraf et al [24] argue that when researching tip-over events, it is important to look at studies that concentrate on spotting them and anticipating when they'll happen. They can use sensors and other devices that they carry or put in addition to the sensors and cameras already present in the smart home's infrastructure.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices

Manimaran¹,

Figueroa

et al. 2023

JAIT

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Smart homes present a serious challenge for the aged and those with mobility issues due to the environment's inherent danger. Unwary people have the propensity to fall over when bending over in these settings. Here, they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for (CM-I and CM-II). The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization criteria. They compared actual and simulated data involving missteps, mishaps, and crashes to gauge the programmers’ performance. Results suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future study. The CM-II model had the highest prediction accuracy of any model identified in the literature, at 0.98 when compared to the test parameter. Since the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers, the number of hacks aimed at the IoT has grown dramatically. There is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the attacks. Pursuing a deeper understanding of the technologies, protocols, and architecture of IoT systems, as well as the potential consequences of using infected IoT devices, is the overarching goal of this study. There are many Internet of Things (IoT) systems vulnerable to cybercriminal manipulation, so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.

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Intrusion Detection System for SDN-enabled IoT Networks using Machine Learning Techniques

Cited by 12 publications

References 23 publications

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions

Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices

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