Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects

Inayat, Usman; Zia, Muhammad Fahad; Mahmood, Sajid; Khalid, Haris M.; Benbouzid, Mohamed

doi:10.3390/electronics11091502

Cited by 107 publications

(54 citation statements)

References 78 publications

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“…In IDS development, different models of DL have been utilized, as high-level features can be represented by them into more intellectual features of data. Selection of appropriate features was attempted by different studies for detection of intrusions [ 14 , 91 ]. Intensive features were automatically extracted by DL approaches from records of data that were unlabeled.…”

Section: Discussionmentioning

confidence: 99%

“…The Internet of Things (IoT) is an emerging technology that allows objects to quickly and effectively share data across remote networks, such as the cloud or wireless connections. Cyber assaults, which may lead to deadly incursions, are becoming more common due to the changes and advances in the IoT ecosystem [ 91 ].…”

Section: Machine Learning and Deep Learning In Nidsmentioning

confidence: 99%

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Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Ahmed

Ngadi

Sharif

et al. 2022

Sensors

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A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning and Deep Learning In Nidsmentioning

confidence: 99%

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Ahmed

Ngadi

Sharif

et al. 2022

Sensors

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“…It is hierarchical, self-sufcient, and makes use of machine learning and regression algorithms to identify network-level intrusions and anomalies in sensor data. Subsets of IoMT are subjected to extensive testing, such as wireless body area networks and other related medical devices [47][48][49][50][51][52][53]. Trough simulations, this research demonstrates the potential for achieving high detection accuracy with little resource use.…”

Section: Related Workmentioning

confidence: 99%

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Abdulwahid

2022

Computational Intelligence and Neuroscience

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The huge number of network traffic data, the abundance of available network features, and the diversity of cyber-attack patterns mean that intrusion detection remains difficult even though many earlier efforts have succeeded in building the Internet of Healthcare Things (IoHT). The implementation of an effective algorithm to filter out most of the probable outliers of Round Trip Time (RTT) of packets recorded in the Internet environment is urgently required. Congestion and interference in networks can arise when numerous biosensors in an IoHT system all attempt to communicate at once. Internet of Health Things networks are susceptible to both intra- and internetwork interference. In this research, the Server-Side Includes (SSI) attack is a key issue because it allows for network compromise as part of Internal Attacks. Despite recent advancements, SSI detection remains difficult due to the vast amounts of network traffic data, the abundance of network features, and the diversity of cyber-attack patterns (DDoS, DoS, Satan, spoofing, etc.). With the help of sensors, physiological data may be collected and sent to distant servers, where they can be analyzed in real time by doctors to help them catch diseases in their earliest stages. This is made possible by the Internet of medical things (IoMT). Wireless data transfer, however, leaves it vulnerable to hackers, especially if the data being transferred are particularly private or sensitive. Security measures designed for devices with more storage space and processing power will not work on those with less. However, machine learning for intrusion detection can give a tailored security response to the needs of IoMT systems. For SSI detection, current methods are either inefficient because of the large number of packets that need to be caught and analyzed or unsuccessful because of outlier values in the RTTs obtained from the captured TCP packets. To the same end, “downstream detection” refers to the process of calculating the total length of all connections made after a certain point. As a means of improving the SSI detection algorithm’s throughput in a network environment, packet RTT outliers will be eliminated. Flow records are used as inputs by flow-based NIDS to determine whether or not a given flow is malicious. In order to detect middlebox-based attacks from two Medical Health IoT datasets, this paper proposes a unique architecture of explainable neural networks (XNN). The model’s accuracy in classifying attacks in dataset 1 of the IoHT is 99.7%t, besides achieving 99.4% accuracy in categorising attacks on IoHT dataset 2.

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“…Signature-based detection method plays a significant role in protecting the system from different types of malwares. This method gained much attention among researchers who conducted numerous studies focused upon its improvement in both academia and industries [10,11]. Signature is the concept built upon this concept of different malware detections.…”

Section: Introductionmentioning

confidence: 99%

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

Maray¹,

Alqahtani²,

Alotaibi³

et al. 2023

Computers, Materials &Amp; Continua

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Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity, secrecy, and accessibility of the information. The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification (OBDDBN-CMC) model. The presented OBDDBN-CMC model intends to recognize and classify the malware that exists in IoT-based cloud platform. To attain this, Zscore data normalization is utilized to scale the data into a uniform format. In addition, BDDBN model is also exploited for recognition and categorization of malware. To effectually fine-tune the hyperparameters related to BDDBN model, Grasshopper Optimization Algorithm (GOA) is applied. This scenario enhances the classification results and also shows the novelty of current study. The experimental analysis was conducted upon OBDDBN-CMC model for 3102 CMC, 2023, vol.74, no.2 validation and the results confirmed the enhanced performance of OBDDBN-CMC model over recent approaches.

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Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects

Cited by 107 publications

References 78 publications

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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