## 5 Experimental Evaluation
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Sniper Configuration**} \\ \hline Instruction set architecture & x86-64 \\ \hline Operate System & Ubuntu 16.04.5 LTS \\ \hline Number of cores & 4 \(\times\) 4, 8 \(\times\) 8 \\ \hline Number of SMT threads per core & 2 \\ \hline Frequency of CPUs (MHz) & 2000 \\ \hline Benckmarks of PARSEC & Blackscholes, Canneal, Fluidanimate, Streamcluster, Swaptions, X-264, Dedup, Freqmine \\ \hline Benckmarks of SPLASH-2 & Raytrace, Barnes \\ \hline \multicolumn{2}{c}{**Configuration for an integrated Hotspot**} \\ \hline Chip thickness & 0.15 mm \\ \hline Silicon thermal conductivity & 100 W/(m\(\cdot\)K) \\ \hline Silicon specific heat capacity & 1.75 \(\times\) 106 J/(m\({}^{3}\cdot\)K) \\ \hline Heat sink side & 0.06 m \\ \hline Heat sink thickness & 6.9 mm \\ \hline Heat sink thermal conductivity & 400 W/(m\(\cdot\)K) \\ \hline Specific heat capacity of heat sink & 3.55 \(\times\) 106 J/(m\({}^{3}\cdot\)K) \\ \hline \multicolumn{2}{c}{**Configuration for TCC programs**} \\ \hline Transmission frequency & 10 Hz, 20 Hz, 50 Hz, 80 Hz, \\ \multicolumn{2}{c}{100 Hz, 150 Hz, etc.} \\ \hline Preamble of a packet & 1010101 \\ \hline Packet size in bits & 64 \\ \hline ECC method & Hamming code \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation configuration. \begin{table}
\begin{tabular}{c c} \hline \hline
**System Sizes** & \(P_{acc}\) \\ \hline
4 \(\times\) 4 & 0.975 \\ \hline
8 \(\times\) 8 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection accuracy of \(P_{acc}\). \(N_{u}\): The number of functional units within a core. \(P_{cu}\): The average power consumption for functional unit \(u\) from core \(c\). Figure 8 shows that the average BER's of our proposed countermeasure is always higher than that of the DVFS countermeasure for different system sizes. We also determined that the average power consumption increased, along with the number of extra noises. The average and maximum temperatures also follow this trend. As shown in Table 5, by increasing the number of noises we are able to reach a high BER, but it may cause a rise in the temperature average power consumption of the systems, which is not good for the systems. Table 6 indicates different aspects of considerations in TCC attacks. In thermal covert channel attacks, most of the existing methodologies did not consider high BER, low power consumption, and performance along with countermeasure. However, in the proposed work, we examine the thermal covert channel attacks in multi-core systems with the goals of optimizing two metrics: bit error rate (BER) and power consumption. We have also performed evaluations for various system sizes. This paper is intended to help the researchers and system designers in gaining deep insight into designing power efficient and secure multi-core systems in the future. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Countermeasures** & **Bit Error Rate** & **Avg. Power Consumption** \\  & & **(w)** \\ \hline TCC with periodically scanning the & & \\ frequency and add a strong noise source [12]. & 85\% & 22.27 \\ \hline TCC with DVFS (Figure 2b) [3; 5]. & 92\% & 22.31799 \\ \hline TCC with One Extra Noise (Figure 4b) & 94\% & 23.74631 \\ \hline TCC with Selective Noise (Figure 6) & 94\% & 22.81277 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results. Figure 8: BER with the proposed method and DVFS in different systems. Experimental results have confirmed that TCC attacks are possible in case of the leakage of sensitive information and data through several seconds or milliseconds. So, it seems that an application must repeatedly check the data and information that thwart such attacks, which may be costly or result in overheating of the devices. The proposed work has a wide range of benefits, which are summarised below:  
* The first and most important advantage of this work is the high bit error rate, which means that the vulnerability of the system will decrease significantly.