## 1 Introduction
provide a classification of security attacks in an edge environment under six categories, i.e., DDoS attacks, side-channel attacks, malware injection attacks, authentication and authorization attacks, man-in-the-middle attacks, and bad-data injection attacks [18]. A threat intelligence report from \"Netscout\" reports an upsurge in DDoS attacks during the second half of 2021. About 9.7 million attacks were identified in 2021, which is 14% higher than in 2019 [19]. The number of malware attacks on IoT devices has grown from 813 million to 2.9 billion from 2018 to 2020 [20]. Current research on EC security and privacy is focused on techniques such as data privacy, lightweight security protocols, artificial intelligence (AI) integration, trust management, and collaborative security. Differential privacy (DP) adds noise to data to protect individual privacy while allowing aggregate data analysis in five critical areas: data transmission, data processing, data model training, data publishing, and location privacy [21; 22; 23]. Authors of [24] introduce a hybrid differential privacy model combined with adaptive gradient compression, providing stronger protection against inference attacks while transmitting gradient parameters. Implementing secured lightweight encryption and authentication techniques secures data from side-channel and hardware attacks [25]. [PERSON] et al. proposed an anonymous authentication protocol that utilizes elliptic curve cryptography (ECC) and signcryption techniques [26]. Several encryption models have been developed over the years using or combining various techniques like authenticated encryption (AE) with associated data (AEAD) schemes [27]. Trust management is a critical component of EC, involving the processing and storage of data at the network's edge. Blockchain and distributed ledger technologies (DLTs) enable decentralized, secure, and transparent trust management. [PERSON] et al. introduced a blockchain-based secure data aggregation strategy (BSDA) integrating a security label into the block header, which includes the task's security level (SL) and completion requirement (CR) [28]. A blockchain-based protocol introduced in [29] supports conditional anonymity and efficient key management, overcoming the limitations of traditional cryptographic pro  
Figure 2: IoT attack model. tocols. Authors of [30] propose multiple edge blockchains that interact through a cloudlet chain operating independently. Collaborative EC enhances privacy by selectively sharing data or insights among nodes or with the cloud, minimizing overall exposure to sensitive information. Techniques such as federated learning (FL) allow multiple devices to train ML models locally on their data without transmitting the raw data to others. This approach helps organizations comply with data residency and privacy regulations by ensuring that data remain within designated geographic boundaries. [PERSON] et al. have developed algorithms based on Multi-Armed Bandit (MAB) frameworks by sharing information about server security risks [31] while proposing an SDN-based framework [32]. Data disturbance and adversarial training methods are adopted in [33] for generating adversarial samples using the Firefly Algorithm (FA). AI-driven techniques enhance intrusion detection, data confidentiality, and access control in edge environments. Researchers have developed various strategies to secure EC, including machine learning (ML) algorithms and innovative methods like hybrid feature analysis. ML is especially effective in detecting real-time anomalies and potential breaches, offering robust protection against advanced attacks [34; 35]. AI chips with computational accelerators like Field Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Neural Processing Units (NPUs) are integrated into intelligent mobile devices [36]. Field programmable gate arrays (FPGAs) are suitable for implementing customized hardware logic and real-time image processing for high-performance edge computation [37].