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In recent times, networks have been prone to several types of attacks, such as DDoS attacks, volumetric attacks, replay attacks, eavesdropping, etc., which drastically degrade the network’s performance. Fortunately, programmable switches facilitate the network monitoring function that helps to solve several security challenges in the network. Nowadays, programmable switches rely on Machine Learning (ML) models to identify intrusions and detect network attacks at a line rate. However, the developed ML models are prone to certain security risks, such as malicious inputs designed to achieve negative outcomes, evasive attacks on the system, and data poisoning attacks. This paper presents a novel framework using the P4 programming language to overcome the above problem on the ML models. Our proposed framework identifies the important features after feature analysis and generates perturbations to showcase the evasion-based adversarial attack in the data plane switches, which an attacker might perform to disrupt the actual behavior of the deployed ML model at the data plane P4 switches. Further, we analyze the plausible impacts of such evasion-based adversarial attacks. Additionally, as part of our framework, we have also proposed a mitigation technique aimed at reducing the impact of these evasion-based adversarial attacks. The results show that the model’s classification rate, under adversarial attack when tested against CICIDS and USB-IDS Datasets, can significantly drop from 99.2% to as low as 50.14% and from 93.7% to as low as 65.1% respectively and increased by 17%,12% after the implementation of proposed mitigation technique in the data plane. [ABSTRACT FROM AUTHOR]

In smart cities, connected and automated surveillance systems play an essential role in ensuring safety and security of life, property, critical infrastructures and cyber-physical systems. The recent trend of such surveillance systems has been to embrace the use of advanced deep learning models such as convolutional neural networks for the task of detection, monitoring or tracking. In this paper, we focus on the security of an automated surveillance system that is responsible for vehicle make and model recognition (VMMR). We introduce an adversarial attack against such VMMR systems through adversarially learnt patches. We demonstrate the effectiveness of the developed adversarial patches against VMMR through experimental evaluations on a real-world vehicle surveillance dataset. The developed adversarial patches achieve reductions of up to 48 % in VMMR recall scores. In addition, we propose a lightweight defense method called SIHFR (stands for Symmetric Image-Half Flip and Replace) to eliminate the effect of adversarial patches on VMMR performance. Through experimental evaluations, we investigate the robustness of the proposed defense method under varying patch placement strategies and patch sizes. The proposed defense method adds a minimal overhead of less than 2ms per image (on average) and succeeds in enhancing VMMR performance by up to 69.28 % . It is hoped that this work shall guide future studies to develop smart city VMMR surveillance systems that are robust to cyber-physical attacks based on adversarially learnt patches. [ABSTRACT FROM AUTHOR]