## 4 Security and Privacy in Edge Frameworks
### Machine Learning in Error Detection and Correction  
An error occurs when the output information does not match the expected result during execution or when the output does not accurately reflect the input information during communication. Digital signals often develop errors due to noise during transmission or system malfunctions during execution, leading to bit errors where a binary 0 may flip to a 1, and vice versa. Factors such as noise, crosstalk, temperature variations, voltage fluctuations, and device aging can adversely affect communication networks and hardware performance, introducing errors. Additionally, external attacks that deliberately manipulate devices or data represent another significant cause of errors in information systems. Traditional error-correcting codes (ECCs) like Hamming, Reed-Solomon, Turbo, and Convolutional codes are employed in correcting errors in communication and storage systems. However, they add redundancy, parity bits, and algebraic techniques to detect and correct errors. These traditional methods face limitations as the data complexity and system noise increase, lowering their adaptability and efficiency [48]. ML has become an indispensable tool in error detection and correction in the current era across various domains, enhancing the system reliability and performance more effectively compared to traditional methods. Traditional methods, which work on a predefined set of rules, fail to adapt well to complex and evolving data patterns. ML algorithms, however, have the capability to learn from data, identify intricate patterns, and adapt to new types of errors, making them highly suitable and effective in dynamic environments. ML has been increasingly employed for detecting and correcting code errors across various programming languages. Algorithms such as K-Means, Graph Neural Networks (GNNs), K-Nearest Neighbors (KNN), Linear Regression, Naive Bayes, Random Forest, and Support Vector Machine (SVM) classifiers have been studied for their effectiveness in identifying and correcting code errors. Some models focus specifically on syntax error detection, while others offer theoretical frameworks for broader error correction strategies [49]. However, a more generalized model for automatically detecting and correcting errors irrespective of the programming language is still an open research question. ML-based error detection and corrections have been increasingly employed across various domains that involve data processing and communication. KNN-regression has been researched for error detection and correction in aircraft sensors, which are crucial for the mechanical system. The solution is a data-driven approach where the ML model uses autocorrelation online and compares with the offline library to accurately detect failures if any [50]. ML methods significantly outperform traditional approaches in handling the high variability and noise associated with Power Line Communication (PLC) channels. ML models such as SVM, Deep Learning (DL), Random Forest (RF), Decision Trees (DT), and KNN are trained on PLC datasets to predict errors, optimize network parameters, and reduce bit error rates (BER). In this context, ML-based techniques demonstrate superior performance compared to conventional error correction methods, such as Hamming codes, Reed-Solomon codes, and Turbo codes [51]. Study shows that SVMs are effective in classifying data in noisy environments for optimal code selection, DL improves feature extraction for better error correction, and RF identifies error patterns and corrects them with low computational overhead. ML is employed to address the reliability challenges of Digital Computing-in-Memory (DCIM) architectures, focusing on the effects of transient faults and circuit aging that can lead to single-event failures or performance degradation. To mitigate transient faults in NOR gates, the study utilizes model prediction techniques such as the Probabilistic Transfer Matrix (PTM) and Signal Probability Reliability (SPR) methods to analyze fault susceptibility.