Digital Twins-enabled Zero Touch Network: A smart contract and explainable AI integrated cybersecurity framework

Abstract

Data-driven modeling using Artificial Intelligence (AI) is envisioned as a key enabling technology for Zero Touch Network (ZTN) management. Specifically, AI has shown huge potential for automating and modeling the threat detection mechanism of complicated wireless systems. The current data-driven AI systems, however, lack transparency and accountability in their decisions, and assuring the reliability and trustworthiness of the data collected from participating entities is an important obstacle to threat detection and decision-making. To this end, we integrate smart contracts with eXplainable AI (XAI) to design a robust cybersecurity framework for ZTN. The proposed framework uses a blockchain and smart contract-enabled access control and authentication mechanism to ensure trust among the participating entities. Additionally, with the collected data, we designed Digital Twins (DTs) for simulating the attack detection operation in the ZTN environment. Specifically, to provide a platform for analysis and the development of an Intrusion Detection System (IDS), the DTs are equipped with a variety of process-aware attack scenarios. A Self Attention-based Long Short Term Memory (SALSTM) network is used to evaluate the attack detection capabilities of the proposed framework. Furthermore, the explainability of the proposed AI-based IDS is achieved using the SHapley Additive exPlanations (SHAP) tool. The experimental results using N-BaIoT and a self-generated DTs dataset confirm the superiority of the proposed framework over some baseline and state-of-the-art techniques. • A new robust cybersecurity framework for ZTN is proposed by integrating smart contracts with eXplainable AI. • To ensure secure communication, a novel blockchain-enabled key establishment and access control mechanism is proposed that authenticates the participating entities in ZTN. The temper-proof property of blockchain ensures high integrity of the data enrichment and builds trust between the participating entities of blockchain network. A smart contract enabled Proof-of-Authority (PoA) consensus mechanism is used to verify and validate the transactions or data. • The authenticated data from smart contract and blockchain-enabled authentication scheme is used to design DT for simulating the attack detection operation in ZTN environment. In particular, the DT is set up with a range of process aware attack scenarios to provide a platform for study and the creation of Intrusion Detection System (IDS). To assess the proposed framework’s ability to identify attacks, a Self Attention-based Long Short Term Memory (SALSTM) network is deployed. Additionally, utilizing the SHapley Additive exPlanations (SHAP) tool, the proposed AI-based IDS is made explainable. • Through experiments using an actual DT simulated dataset and state-of-the-art intrusion dataset (N-BaIoT) is used to evaluate the proposed framework. The outcomes are compared with some baseline and state-of-the-art techniques to show the effectiveness of the proposed cybersecurity framework.

Topics & Concepts