On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

Otoum, Safa; Kantarcı, Burak; Mouftah, Hussein T.

doi:10.1109/lnet.2019.2901792

Cited by 235 publications

(117 citation statements)

References 15 publications

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“…However, since the proposed algorithm mainly considers mitigation of latency and load congestion, further studies on trust, security, privacy, forensic, etc. [52][53][54][55] are needed, such as intrusion awareness [56][57][58][59] and secure data aggregation [60]. We expect that applying those would provide more secure data caching.…”

Section: Discussionmentioning

confidence: 99%

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EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

Kim

Huh

2019

Applied Sciences

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This paper explores data caching as a key factor of edge computing. State-of-the-art research of data caching on edge nodes mainly considers reactive and proactive caching, and machine learning based caching, which could be a heavy task for edge nodes. However, edge nodes usually have relatively lower computing resources than cloud datacenters as those are geo-distributed from the administrator. Therefore, a caching algorithm should be lightweight for saving computing resources on edge nodes. In addition, the data caching should be agile because it has to support high-quality services on edge nodes. Accordingly, this paper proposes a lightweight, agile caching algorithm, EDCrammer (Efficient Data Crammer), which performs agile operations to control caching rate for streaming data by using the enhanced PID (Proportional-Integral-Differential) controller. Experimental results using this lightweight, agile caching algorithm show its significant value in each scenario. In four common scenarios, the desired cache utilization was reached in 1.1 s on average and then maintained within a 4–7% deviation. The cache hit ratio is about 96%, and the optimal cache capacity is around 1.5 MB. Thus, EDCrammer can help distribute the streaming data traffic to the edge nodes, mitigate the uplink load on the central cloud, and ultimately provide users with high-quality video services. We also hope that EDCrammer can improve overall service quality in 5G environment, Augmented Reality/Virtual Reality (AR/VR), Intelligent Transportation System (ITS), Internet of Things (IoT), etc.

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Section: Discussionmentioning

confidence: 99%

“…We expect that applying those would provide more secure data caching. Meanwhile, since AI-based data caching [61][62][63] and AI-based edge management [59,64] are computationally intensive, this paper assumes that it is not appropriate for edge nodes. However, it can be effective for data caching when multiple factors are considered.…”

Section: Discussionmentioning

confidence: 99%

EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

Kim

Huh

2019

Applied Sciences

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“…Another important challenge is privacy issues that can have a fatal impact on the user. Some interesting approaches to solving such issues are adopting deep learning and machine learning techniques [39,40]. Such techniques can be used to secure user data and detect intruders that violate the user's privacy.…”

Section: Open Issues and Challengesmentioning

confidence: 99%

ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management

2019

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Recently, new mobile applications and services have appeared thanks to the rapid development of mobile devices and mobile network technology. Cloud computing has played an important role over the past decades, providing powerful computing capabilities and high-capacity storage space to efficiently deliver these mobile services to mobile users. Nevertheless, existing cloud computing delegates computing to a cloud server located at a relatively long distance, resulting in significant delays due to additional time to return processing results from a cloud server. These unnecessary delays are inconvenient for mobile users because they are not suitable for applications that require a real-time service environment. To cope with these problems, a new computing concept called Multi-Access Edge Computing (MEC) has emerged. Instead of sending all requests to the central cloud to handle mobile users' requests, the MEC brings computing power and storage resources to the edge of the mobile network. It enables the mobile user device to run the real-time applications that are sensitive to latency to meet the strict requirements. However, there is a lack of research on the efficient utilization of computing resources and mobility support when mobile users move in the MEC environment. In this paper, we propose the MEC-based mobility management scheme that arranges MEC server (MECS) as the concept of Zone so that mobile users can continue to receive content and use server resources efficiently even when they move. The results show that the proposed scheme reduce the average service delay compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because mobile users can continuously receive services, even when they move frequently.

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“…The authors in [21] introduced a deep-learning-based methodology for monitoring critical infrastructures using restricted boltzmann machine-based clustered intrusion detection system (RBC-IDS). The RBC-IDS technique classifies intruders from sensory data collected from wireless sensor networks (WSNs).…”

Section: Related Workmentioning

confidence: 99%

HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services

et al. 2019

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Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care.

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On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

Cited by 235 publications

References 15 publications

EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management

HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services

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