Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing

Abeshu, Abebe; Chilamkurti, Naveen

doi:10.1109/mcom.2018.1700332

Cited by 319 publications

(153 citation statements)

References 6 publications

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“…A previous study [245] proposed a DL model to enhance cybersecurity and enable attack detection in IoT systems and verified the appropriateness of the DL model in securing the cyberspace of IoT systems. Similarly, [246] proposed a distributed DL model to deliver accurate protection against cyberattacks and threats in fog-to-things computing and used an SAE algorithm to construct their learning model. The authors confirmed that the DL models are more suitable for such cyberattack protection than are traditional methods in terms of scalability, accuracy and false alarm rate.…”

Section: Application Layermentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

845

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: Application Layermentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

845

333

View full text Add to dashboard Cite

show abstract

“…[14] also explored how deep reinforcement learning and its shift towards semi-supervision can handle the cognitive side of smart-city services. The work in [15] indicated that the application of deep networks has already been successful in big-data areas, and fog-to-things computing can be the ultimate beneficiary of the approach for attack detection because the massive amount of data produced by IoT devices enables deep models to learn better than shallow algorithms. In summary, most of the current researches on cognitive computing has focused on the design of algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Service Migration Mechanism in Edge Cognitive Computing

Chen

Fortino

et al. 2019

ACM Trans. Internet Technol.

161

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Driven by the vision of edge computing and the success of rich cognitive services based on artificial intelligence, a new computing paradigm, edge cognitive computing (ECC), is a promising approach that applies cognitive computing at the edge of the network. ECC has the potential to provide the cognition of users and network environmental information, and further to provide elastic cognitive computing services to achieve a higher energy efficiency and a higher Quality of Experience (QoE) compared to edge computing. This paper firstly introduces our architecture of the ECC and then describes its design issues in detail. Moreover, we propose an ECC-based dynamic service migration mechanism to provide an insight into how cognitive computing is combined with edge computing. In order to evaluate the proposed mechanism, a practical platform for dynamic service migration is built up, where the services are migrated based on the behavioral cognition of a mobile user. The experimental results show that the proposed ECC architecture has ultra-low latency and a high user experience, while providing better service to the user, saving computing resources, and achieving a high energy efficiency.

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“…A back-propagation approach that relies on the gradient descent method is used to learn the model parameters efficiently. The detailed process for the back-propagation approach can be found in Abeshu et al [7].…”

Section: Related Workmentioning

confidence: 99%

“…The abovementioned process to train a multilayer and complex DL model entails a high computational overhead. To mitigate this issue, various novel DL paradigms have been proposed by many researchers at the different layer of IoT such as cloud edge, and fog [4][5][6][7][8]. Zhang et al [6] presented a DL approach wherein the learning task is performed at the cloud layer to improve the learning efficiency for big data analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Note, however, that the IoT network generates a large amount of data that impose heavy communication and computation tasks on the cloud server, such as raw data collection, pre-processing, features extraction, and analysis; hence, the rapid escalation in the server calculation load and communication frequency that further lead to overwhelming, and sometimes unbearable, load to the centralized servers. To address these challenges, Fog intelligence-wherein the data collection and computation tasks of DL are shifted to the fog layer in a distributed manner-was presented [7]. Each fog node prepares a DL model by training its own private data individually and periodically shares the intermediate parameters of the model to the cloud server.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

2019

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The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation.

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Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing

Cited by 319 publications

References 6 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Dynamic Service Migration Mechanism in Edge Cognitive Computing

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

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