From statistical- to machine learning-based network traffic prediction

Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large-scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical- and Machine Learning (ML)-based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.publishedVersio

Novel MEC based Approaches for Smart Hospitals to Combat COVID-19 Pandemic

COVID-19 or Coronavirus has thrilled the entire world population with uncertainty over their survival and well-being. The impact this pathogen has caused over the globe has been profound due to its unique transmission features; that urges for contact-less strategies to interact and treat the infected. The impending 5G mobile technology is immersing the applications that enable the provisioning of medical and healthcare services in a contact-less manner. The edge computing paradigms offer a de-centralized and versatile networking infrastructure capable of adhering to the novel demands of 5G. In this article, we are considering Multi-Access Edge Computing (MEC) flavour of the edge paradigms for realizing the contact-less approaches that assist the mediation of COVID-19 and the future of healthcare. In order to formulate this ideology, we propose three use cases and discuss their implementation in the MEC context. Further, the requirements for launching these services are provided. Additionally, we validate our proposed approaches through simulations.European CommissionAcademy of Finlan

From statistical- to machine learning-based network traffic prediction

Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large-scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical- and Machine Learning (ML)-based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP

InSDN: A Novel SDN Intrusion Dataset

Software-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center’s network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion Detection Systems (IDSs) to monitor malicious activities is a crucial part of the network architecture. Although the centralized view of the SDN network creates new opportunities for the implementation of IDSs, the performance of these detection techniques relies on the quality of the training datasets. Unfortunately, there are no publicly available datasets that can be used directly for anomaly detection systems applied in SDN networks. The majority of the published studies use non-compatible and outdated datasets, such as the KDD’99 dataset. This manuscript aims to generate an attack-specific SDN dataset and it is publicly available to the researchers. To the best of our knowledge, our work is one of the first solutions to produce a comprehensive SDN dataset to verify the performance of intrusion detection systems. The new dataset includes the benign and various attack categories that can occur in the different elements of the SDN platform. Further, we demonstrate the use of our proposed dataset by performing an experimental evaluation using eight popular machine-learning-based techniques for IDSs

A Machine Learning-Based Intrusion Detection System for IoT Electric Vehicle Charging Stations (EVCSs)

The demand for electric vehicles (EVs) is growing rapidly. This requires an ecosystem that meets the user’s needs while preserving security. The rich data obtained from electric vehicle stations are powered by the Internet of Things (IoT) ecosystem. This is achieved through us of electric vehicle charging station management systems (EVCSMSs). However, the risks associated with cyber-attacks on IoT systems are also increasing at the same pace. To help in finding malicious traffic, intrusion detection systems (IDSs) play a vital role in traditional IT systems. This paper proposes a classifier algorithm for detecting malicious traffic in the IoT environment using machine learning. The proposed system uses a real IoT dataset derived from real IoT traffic. Multiple classifying algorithms are evaluated. Results were obtained on both binary and multiclass traffic models. Using the proposed algorithm in the IoT-based IDS engine that serves electric vehicle charging stations will bring stability and eliminate a substantial number of cyberattacks that may disturb day-to-day life activities

Novel MEC based approaches for smart hospitals to combat COVID-19 pandemic

Abstract COVID-19 or Coronavirus has thrilled the entire world population with uncertainty over their survival and well-being. The impact this pathogen has caused over the globe has been profound due to its unique transmission features; that urges for contact-less strategies to interact and treat the infected. The impending 5G mobile technology is immersing the applications that enable the provisioning of medical and healthcare services in a contact-less manner. The edge computing paradigms offer a de-centralized and versatile networking infrastructure capable of adhering to the novel demands of 5G. In this article, we are considering Multi-Access Edge Computing (MEC) flavour of the edge paradigms for realizing the contact-less approaches that assist the mediation of COVID-19 and the future of healthcare. In order to formulate this ideology, we propose three use cases and discuss their implementation in the MEC context. Further, the requirements for launching these services are provided. Additionally, we validate our proposed approaches through simulations

Realizing multi-access edge computing feasibility:security perspective

Abstract Internet of Things (IoT) and 5G are emerging technologies that prompt a mobile service platform capable of provisioning billions of communication devices which enable ubiquitous computing and ambient intelligence. These novel approaches are guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. To achieve these limitations, ETSI has introduced the paradigm of Multi-Access Edge Computing (MEC) for creating efficient data processing architecture extending the cloud computing capabilities in the Radio Access Network (RAN). Despite the gained enhancements to the mobile network, MEC is subjected to security challenges raised from the heterogeneity of IoT services, intricacies in integrating virtualization technologies, and maintaining the performance guarantees of the mobile networks (i.e. 5G). In this paper, we are identifying the probable threat vectors in a typical MEC deployment scenario that comply with the ETSI standards. We analyse the identified threat vectors and propose solutions to mitigate them