## 4 Results
### Hardware-Based Security  
The smart grid system is useless without devices connecting to the internet. These devices must resist cyberattacks since they gather, process, and transmit data over the channel. The authors in [165] discussed some of the most critical hardware security issues. Security flaws might appear in many ways, including physical attacks, hardware Trojans, and side-channel analyses. The attacker wants to avoid being identified during the actual physical attack by the authentication procedure. System flaws were exploited using reverse engineering to plan the attack. An opponent can determine the cryptographic keys by analyzing the profile of numerous variables, including current, voltage, and frequency, using a method called \"side-channel analysis\". Any deliberate alterations or additions to a circuit are referred to as hardware Trojans. Hardware Trojans are malicious applications that steal sensitive information, change circuitry, or lower the system's dependability. According to the authors' analysis, path delay fingerprinting may be utilized to identify hardware Trojans. Smart meters, sensors, and communication devices are examples of IoT devices that battle with how much energy they can use and how little power they can use [166]. Because they allow fully secure authentication without requiring the device to have cryptographic expertise, PUFs are perfect for low-power Internet of Things (IoT) devices. Even so, by analyzing historical data and events, it is now possible to predict PUF behavior with a 95% degree of accuracy because of the development of machine learning [167]. To prevent machine learning-based attacks from breaching PUFs, the study's authors [167] developed a CTPUF, or configurable tristate PUF, using an XOR-based technique that hides the connection between the issue and the solution. The environment is too chaotic for the \begin{table}
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**References** & **Methods** & **Short Description** & **Findings** \\ \hline
[159] & A Distributed Private Energy Trading Platform. & Presented a proof-of-concept for a secure private blockchain energy transaction system. & It showed the advantages of using blockchains in the electrical network and the SPB framework, which lowers the expenses, volume, and processing times related to energy trade. \\  & Energy Trading Between Individuals Using a Virtual Power Plant Which Is Powered by Smart Contracts Stored on a blockchain & A public, sale price purchasing mechanism SC enables is recommended for energy trading. \\  & blockchain & An infrastructure built on the blockchain to close the demand-response gap between energy supply by producers and consumer demand in peer-to-peer energy trading. \\  & Blockchain-enabled Peer-to-Peer energy trading & An infrastructure built on the blockchain technology for peer-to-peer energy trading \\  & & It proposed an energy-sharing architecture based on energy prices in a community market with a shareholder energy storage system, consumers, and users \\  & Energy-backed token trading that is peer-to-peer and based on a decentralized blockchain platform for active producers and consumers & Utilize the blockchain enable peer-to-peer trading of energy tokens \\  & & & The suggested strategy ensures a global and practical resolution while requesting no private information from the participants. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._machine learning model to detect recurring patterns between the challenge and responses. The results of this study showed that the accuracy of machine learning models that used CTPUF, such as support vector machines, artificial neural networks, and logistic regression models, was about 60%. Another study [168] used machine learning models to show the shortcomings of voltage-over-scaling (VOS)-based authentication. The studies also made abuse a possibility. The authors of this work developed a VOS technique that is immune to ML by fusing earlier challenges with keys.