An edge based hybrid intrusion detection framework for mobile edge computing

Abstract

The 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.

Research on secure workload execution scheme in heterogeneous cloud environment

“…The extra functions given by the SDN innovation empower coordination of an original security instrument like the filtering of the traffic. While in the network function virtualization (NFV) scope, a couple of assessment papers focused on evaluating the presentation and believability of running virtual security machines on the edge using holders like IDS and firewalls [21]. Yet this light virtualization development showed exceptional productivity, it ended up being testing representing the asset obliged IoT devices.…”

An Intelligent Iot Attack Detection Framework Using Effective Edge Ai Based Computing

“…Two main tasks of support layer are authentication and forwarding information towards network layer. Five layer IoT architecture mainly concentrated over the constraints of security as well as memory [35]. Additional two layers are business and processing.…”

Ensemble technique of intrusion detection for IoT-edge platform

“…Cases of security breaches are widespread in IoT systems as it is overlooked in the framework design process. Figure 1 shows the conceptual overview of edge computing-based IDS detection 5 . In 2016, hackers used the Mirai virus to launch DDoS attacks on a large number of IoT devices (Source: https://www.theguardian.com/technology/2016/oct/ .).…”