CATcAFSMs: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems

Author:

Nawaz Alishba1,Iqbal Waseem12ORCID,Altaf Ayesha3,Almjally Abrar4,AlSagri Hatoon5ORCID,Alabdullah Bayan6

Affiliation:

1. Department of Information Security National University of Sciences and Technology (NUST) Islamabad Pakistan

2. Electrical and Computer Engineering Department, College of Engineering Sultan Qaboos University Al‐Khud Oman

3. Department of Computer Science University of Engineering and Technology Lahore Pakistan

4. Information Technology, College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia

5. Information Systems, College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia

6. Department of Information Systems, College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Abstract

AbstractFog's basic distributed nature and ability to process data in transit—that is, to make decisions in real time—make it a good fit for scenarios involving several distributed devices that need to communicate, provide real‐time data analysis, and carry out storage functions. The majority of fog computing applications are driven by the user's demands and/or their desire for functioning services, either neglecting or giving security considerations second attention. Fog computing security issues have not received enough attention. Fog computing could be exploitable due to the security difficulties associated with cloud computing. Due to its flexibility to function near the end user and independence from a centralized design, fog computing provides the dependability required by time‐sensitive smart healthcare systems. There is a need for enhanced security and privacy solutions for fog computing, where trust is essential, due to the importance of healthcare data. This research aims to develop a context‐based adaptive trust solution for the smart healthcare environment utilizing Bayesian approaches and similarity measures against bad mouthing and ballot stuffing, while context‐dependent trust solutions for fogs remain an unexplored area of study. The proposed trust model has been simulated in Contiki‐Cooja to evaluate our findings. In contrast to static weighting, adaptive weights are provided to direct and indirect trust using entropy values that ensure the least degree of trust bias, and context similarity calculations eliminate recommender nodes with malicious intent by leveraging server, colleague, and service similarities. The proposed model protects smart healthcare systems from attacks using similarity metrics, incorporates context, and also uses adaptive weighting for trust calculation. By eliminating trust bias and also detecting attacks, this solution enhances the trust calculation by 10% as compared to the previous solution. This paradigm is efficient due to its small trust computation overhead and linear complexity O(n).

Funder

Princess Nourah Bint Abdulrahman University

Publisher

Wiley

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