scholarly journals Automised flow rule formation by using machine learning in software defined networks based edge computing

2021 ◽  
Author(s):  
Saleem Iqbal ◽  
Hira Maryam ◽  
Kashif Naseer Qureshi ◽  
Ibrahim Tariq Javed ◽  
Noel Crespi
Keyword(s):  
Machine Learning ◽  
Edge Computing ◽  
Flow Rule ◽  
Software Defined Networks

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

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.

Machine Learning-Enabled Software-Defined Networks for QoE Management

2021 ◽  
Author(s):  
Chunzhi Wang ◽  
Le Yuan ◽  
Mykhailo Medvetskyi ◽  
Mykola Beshley ◽  
Andrii Pryslupskyi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Defined Networks

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

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.

The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks

2021 ◽  
pp. 959-975
Author(s):  
Derya Yiltas-Kaplan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Architectures ◽  
Software Defined Networks ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Network Problems ◽  
Usage Analysis

This chapter focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. Upon this schedule, this chapter also aims to be the first organized guide that presents the referenced studies on the SDN security and artificial intelligence together.

scholarly journals cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Afifa Maryam ◽  
Usman Ahmed ◽  
Muhammad Aleem ◽  
Jerry Chun-Wei Lin ◽  
Muhammad Arshad Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Edge Computing ◽  
Cloud Services ◽  
Hybrid Technique ◽  
Dynamic Features ◽  
Hybrid Features ◽  
Android Malware ◽  
Detection Techniques

Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application’s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography’s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.

Sign in / Sign up

Export Citation Format

Share Document