eBPF-based content and computation-aware communication for real-time edge computing

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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Integration of Minimum Energy Point Tracking and Soft Real-Time Scheduling for Edge Computing

2021 22nd International Symposium on Quality Electronic Design (ISQED) ◽

10.1109/isqed51717.2021.9424343 ◽

2021 ◽

Author(s):

Takumi Komori ◽

Yutaka Masuda ◽

Jun Shiomi ◽

Tohru Ishihara

Keyword(s):

Real Time ◽

Minimum Energy ◽

Edge Computing ◽

Real Time Scheduling ◽

Energy Point ◽

Point Tracking ◽

Time Scheduling ◽

Minimum Energy Point

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An edge based hybrid intrusion detection framework for mobile edge computing

Complex & Intelligent Systems ◽

10.1007/s40747-021-00498-4 ◽

2021 ◽

Author(s):

Ashish Singh ◽

Kakali Chatterjee ◽

Suresh Chandra Satapathy

Keyword(s):

Intrusion Detection ◽

Real Time ◽

Network Architecture ◽

Denial Of Service ◽

Edge Computing ◽

Mobile Edge Computing ◽

Hybrid Detection ◽

Game Theoretical Approach ◽

Improved Performance ◽

Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

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Using Hypothesis Testing to Evaluate Key Performance Indicators in Real Time: An Edge Computing Use Case

10.2118/195023-ms ◽

2019 ◽

Cited By ~ 1

Author(s):

Peter Kowalchuk

Keyword(s):

Hypothesis Testing ◽

Real Time ◽

Performance Indicators ◽

Key Performance Indicators ◽

Edge Computing ◽

Use Case

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Real Time Tunnel Structure Monitoring System Based on Edge Computing

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) ◽

10.1109/icaica52286.2021.9498000 ◽

2021 ◽

Author(s):

Dongdong Zhang ◽

Li Liang ◽

Wei Wang ◽

Xiaogang Song ◽

Xiaochun Ren

Keyword(s):

Real Time ◽

Monitoring System ◽

Edge Computing ◽

Tunnel Structure ◽

Structure Monitoring

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AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

Security and Communication Networks ◽

10.1155/2021/9954951 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Xiang Yu ◽

Chun Shan ◽

Jilong Bian ◽

Xianfei Yang ◽

Ying Chen ◽

...

Keyword(s):

Internet Of Things ◽

Anomaly Detection ◽

Real Time ◽

Detection Method ◽

Edge Computing ◽

Sensor Data ◽

Computing Environment ◽

The Real ◽

Edge Node ◽

Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

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Developing an edge computing platform for real-time descriptive analytics

2017 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2017.8258497 ◽

2017 ◽

Cited By ~ 9

Author(s):

Hung Cao ◽

Monica Wachowicz ◽

Sangwhan Cha

Keyword(s):

Real Time ◽

Edge Computing ◽

Computing Platform

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Using AI at the Edge and Incremental Machine Learning to Process Onboard Instrument Data

10.5957/smc-2021-048 ◽

2021 ◽

Author(s):

Nicholas Parkyn

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Real Time ◽

Data Storage ◽

High Performance ◽

Heterogeneous Computing ◽

Low Cost ◽

Machine Intelligence ◽

Edge Computing ◽

Continual Learning

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.

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