Smart SDN Management of Fog Services to Optimize QoS and Energy

Fröhlich, Piotr; Gelenbe, Erol; Fiołka, J.; Chęciński, J.; Nowak, Mateusz; Filus, Zdzisław

doi:10.3390/s21093105

Cited by 14 publications

(7 citation statements)

References 55 publications

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“…Such studies can also examine the possibility of identifying the sources of the Botnet and carrying out preventive blocking/dropping of traffic at the sources themselves as was suggested in some earlier work [57]. Another important issue that is worth consiering, is the energy consumption consequences of the AD itself [58], [59] as well as of the mitigation actions that are taken.…”

Section: Discussionmentioning

confidence: 98%

Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices

Gelenbe

Nakıp

2022

IEEE Access

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A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and trains itself online during normal operation with an Auto-Associative Dense Random Neural Network (AADRNN) using traffic metrics measured as traffic arrives. As it operates, the AADRNN is trained with auto-associative learning only using traffic that it estimates as being benign, without prior collection of different attack data. The experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Experimental comparisons show that the AADRNN with sequential (online) auto-associative learning, provides the best performance among six different state-of-the-art machine learning models. Thus CDIS can provide crucial effective information to prevent the spread of Botnet attacks in IoT networks having multiple devices and IP addresses.

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Section: Discussionmentioning

confidence: 98%

Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices

Gelenbe

Nakıp

2022

IEEE Access

Self Cite

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“…In the selected fog region, the fog server manager connects to other fog nodes within the fog region to balance the loads of the tasks. The fog server manager keeps track of all available free resources within the fog region and provides the updated information required by the load balancer and the SDN controller [45]. The load balancer and SDN check the available fog regions to schedule the tasks.…”

Section: Load-balancing Mechanismmentioning

confidence: 99%

Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing

Singh

Amhoud

et al. 2022

Sustainability

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The number of client applications on the fog computing layer is increasing due to advancements in the Internet of Things (IoT) paradigm. Fog computing plays a significant role in reducing latency and enhancing resource usage for IoT users’ tasks. Along with its various benefits, fog computing also faces several challenges, including challenges related to resource overloading, security, node placement, scheduling, and energy consumption. In fog computing, load balancing is a difficult challenge due to the increased number of IoT devices and requests, which requires an equal load distribution throughout all available resources. In this study, we proposed a secure and energy-aware fog computing architecture, and we implemented a load-balancing technique to improve the complete utilization of resources with an SDN-enabled fog environment. A deep belief network (DBN)-based intrusion detection method was also implemented as part of the proposed techniques to reduce workload communication delays in the fog layer. The simulation findings showed that the proposed technique provided an efficient method of load balancing in a fog environment, minimizing the average response time, average energy consumption, and communication delay by 15%, 23%, and 10%, respectively, as compared with other existing techniques.

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“…The SerIoT project that was started in 2018 [19] also produced valuable results [48]. Its technical scope included SerCPN [29,30], a specific secure network [49] for managing geographically distributed IoT devices and services using the principles of the Cognitive Packet Network (CPN) tested in several experiments [28,59,61,62,64]. CPN uses "Smart" Packets (SPs) to search for paths and measure QoS while the network is in operation, via Reinforcement Learning using a Random Neural Network, and based on the QoS Goal pursued by the end user.…”

Section: Contributions To the Security Of The Iotmentioning

confidence: 99%

AI and Quality of Service Driven Attack Detection, Mitigation and Energy Optimization: A Review of Some EU Project Results

Caglayan

2022

Communications in Computer and Information Science

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This article summarizes briefly the contributions presented in this EuroCyberSecurity Workshop 2021 which is organized as part of the series of International Symposia on Computer and Information Sciences (ISCIS), with the support of the European Commission funded IoTAC Project, that was held on November and in NIce, France, and sponsored by the Institute of Teoretical and Applied Informatics of the Polish Academy of Sciences. It also summarizes some of the research contributions of several EU Projects including NEMESYS, GHOST, KONFIDO, SDK4ED and IoTAC, primarily with a cybersecurity and Machine Learning orientation. Thus subjects covered include the cybersecurity of Mobile Networks and of the Internet of Things (IoT), the design of IoT Gateways and their performance, the security of networked health systems that provide health services to individuals across the EU Member states, as well as the issues of energy consumption by ICT which are becoming increasingly important, including in the cybersecurity perspective, as we focus increasingly on climate change and the needed transition towards highly reduced emissions. Many of the techniques and results discussed in this article are based either on Machine Learning (ML) methods, or on methods for the performance modeling and optimization of networked and distributed computer systems.

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Smart SDN Management of Fog Services to Optimize QoS and Energy

Cited by 14 publications

References 55 publications

Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices

Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices

Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing

AI and Quality of Service Driven Attack Detection, Mitigation and Energy Optimization: A Review of Some EU Project Results

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