# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 4 Side-Channel Attacks
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SCA Threats** & **Method and Techniques** & **Explanation** & **Implication to** \\ \hline User interaction [46] & Gesture inference, keystroke inference, reflective inference, reflective inference, & User interaction with devices could be used to infer secret information. e.g., how keys are pressed or different gestures while using the device. & These threats are related to users and using the devices to access the DigAg applications. \\ \hline Acoustic [47; 48] & Noise inference & Audio leakage of keystrokes, voice recording for voice verification & Hardware bugs to record the acoustic data and exfiltrate for later analysis \\ \hline Virtualisation interface [51] & Multi-tenant cross-talk [52], page fault exploit, virtual machine duplication exploit & The same physical resource is shared among different applications, applications, and the attackers could recover memory traces. & These SCA threats are related to applications and to applications and on the cloud and can lead to IP, PII, and commercial data theft. \\ \hline Network interface [37] & LED interface, light induction & Physically clamping to the network card or eavesdropping on the wireless communication & Identifying communicating parties—from sending and receiving patterns, behavioural profiling to improve fingerprinting for marketing reasons \\ \hline Thermal Dissipation [53] & Thermal pattern correlation & Measuring thermal thermal dissipation and correlating it to the workload in the hardware during the execution of instructions. & Thermal cameras and heat maps can be used alongside other SCA techniques on DigAg devices \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._which could be easily accessible considering that most farms are out of the city and do not have proper physical security (CCTVs, fencing etc.). Once a malicious user has physical access, it is at the attacker's mercy to monitor the side-channels parameter, revealing the cryptographic information or inferring other information, as mentioned in Table 2. For example, a power analysis attack requires power consumption monitoring during a cryptographic operation. A simple power trace of device operations correlated with data-dependent power variations can be used to infer the cryptographic key. A high signal-to-noise ratio (SNR) requires fewer power consumption traces, and close proximity would enable capturing a high SNR trace, making it easy to differentiate traces from one another [15]. In other computing applications, hardware is physically secured, and attackers cannot have prolonged access, unlike in agriculture. Therefore, different variants of SCAs can be easily initiated, as given in Table 2.  
Further, low-cost and re-purposed hardware devices (sensors, actuators) do not have a built-in security mechanism to monitor their status, usage, or access to the memory. A secure memory (EEPROMs) is required to store the cryptographic keys securely. Physical unclonable functions (PUFs) could be used for tampering protection and low-cost authentication without relying on secure storage [54]. PUFs can derive secrets from the integrated circuit and be used in low-cost authentication and key generation, minimising secure storage requirements.