# Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform

arXiv: https://arxiv.org/abs/2111.00601 · HTML: https://mlsanity.com/paper/2111.00601/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures](https://mlsanity.com/paper/1910.13312/) (2019)
- [Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis](https://mlsanity.com/paper/1902.06019/) (2019)
- [Explainable Artificial Intelligence (XAI): An Engineering Perspective](https://mlsanity.com/paper/2101.03613/) (2021)
- [Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues](https://mlsanity.com/paper/1912.03356/) (2019)
- [GNSS Time Synchronization in Vehicular Ad-Hoc Networks: Benefits and Feasibility](https://mlsanity.com/paper/1811.02741/) (2018)
- [Time synchronization in vehicular ad-hoc networks: A survey ontheory and practice](https://mlsanity.com/paper/1811.04580/) (2018)
- [Security, Privacy and Trust: Cognitive Internet of Vehicles](https://mlsanity.com/paper/2104.12878/) (2021)
- [Intelligent Systems Design for Malware Classification Under Adversarial Conditions](https://mlsanity.com/paper/1907.03149/) (2019)
- [Enhancing Vulnerable Road User Safety: A Survey of Existing Practices and Consideration for Using Mobile Devices for V2X Connections](https://mlsanity.com/paper/2010.15502/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Dependable Hybrid Machine Learning Model for Network Intrusion Detection](https://mlsanity.com/paper/2212.04546/) (2022)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN](https://mlsanity.com/paper/2303.06020/) (2023)
- [Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments](https://mlsanity.com/paper/2404.16074/) (2024)

## Abstract (arXiv)

Artificial Intelligence (AI) is one of the disruptive technologies that is shaping the future. It has growing applications for data-driven decisions in major smart city solutions, including transportation, education, healthcare, public governance, and power systems. At the same time, it is gaining popularity in protecting critical cyber infrastructure from cyber threats, attacks, damages, or unauthorized access. However, one of the significant issues of those traditional AI technologies (e.g., deep learning) is that the rapid progress in complexity and sophistication propelled and turned out to be uninterpretable black boxes. On many occasions, it is very challenging to understand the decision and bias to control and trust systems' unexpected or seemingly unpredictable outputs. It is acknowledged that the loss of control over interpretability of decision-making becomes a critical issue for many data-driven automated applications. But how may it affect the system's security and trustworthiness? This chapter conducts a comprehensive study of machine learning applications in cybersecurity to indicate the need for explainability to address this question. While doing that, this chapter first discusses the black-box problems of AI technologies for Cybersecurity applications in smart city-based solutions. Later, considering the new technological paradigm, Explainable Artificial Intelligence (XAI), this chapter discusses the transition from black-box to white-box. This chapter also discusses the transition requirements concerning the interpretability, transparency, understandability, and Explainability of AI-based technologies in applying different autonomous systems in smart cities. Finally, it has presented some commercial XAI platforms that offer explainability over traditional AI technologies before presenting future challenges and opportunities.