## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Id** & **Proposal** & **Deep Learning Model Used** & **Used Dataset** & **Detected Violence Actions** & **Detected Crime Actions** \\ \hline \multirow{4}{*}{S40} & Detection of routing & \multirow{4}{*}{LSTM} & \multirow{4}{*}{ToN-IoT} & Manipulation of \\  & protocol for & & & & Destination Oriented \\  & low-power lossy & & & Simulated Dataset & Not applicable & Direct Acyclic Graph \\  & networks (RPL) & & & & (cyberattacks) & Information Object (DIO) \\  & version number & & & & & messages, \\  & attacks in IoT & & & & increased latency \\ \hline \multirow{4}{*}{S4I} & Hierarchical & \multirow{4}{*}{LSTM based on Recurrent } & \multirow{4}{*}{ToN-IoT} & \multirow{4}{*}{\(\rm N/A\)} & \multirow{4}{*}{Various cyberattacks} \\  & intrusion & & Neural Network (RNN) & & & \\  & detection system & & & & \\ \hline \multirow{4}{*}{S42} & Label health systems & \multirow{4}{*}{CNN} & Curated protest & \multirow{4}{*}{Protests turned violent:} & \multirow{4}{*}{Arson, object throwing,} \\  & in mass protests & & data extracted & & & clashes with \\ \cline{3-5}  & & from YouTube & & & security forces \\ \hline \multirow{4}{*}{S43} & Advanced security & \multirow{4}{*}{\begin{tabular}{c} Extreme Learning Machines \\ (ELMs) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Cryptocrypt Neural \\ Networks + Deep \\ Reinforcement \\ \end{tabular} } & \multirow{4}{*}{CICIDS2017} & \multirow{4}{*}{\(\rm N/A\)} & \multirow{4}{*}{\begin{tabular}{c} Distributed Denial of \\ Service (DDoS) attacks \\ \end{tabular} } \\  & smart cities & & & & \\  & & (DRL-DQN) & & & \\ \hline \multirow{4}{*}{S44} & Adversarial attacks & \multirow{4}{*}{\begin{tabular}{c} DRL-based traffic \\ signal \\ control systems \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} DRL, Proximal Policy \\ Optimization (PPO) \\ \end{tabular} } & Simulated traffic data & Vehicle collusion to alter & Traffic data manipulation \\  & & & based on Monaco & signaling times & to falsify conditions \\ \hline \multirow{2}{*}{S45} & Violence detection in & \multirow{2}{*}{\begin{tabular}{c} CNN + LSTM \\ surveillance videos \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Hockey fights \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} First fights, abuse with \\ non-projectile weapons \\ \end{tabular} } & \multirow{2}{*}{\(\rm N/A\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._accuracy and reducing computational overhead. Meanwhile, S24 employs GAs in hybrid intrusion detection models, demonstrating their efficacy in detecting DDoS and botnet attacks in real-time network traffic. These studies highlight the potential of evolutionary algorithms in deep learning-driven cybersecurity, ensuring scalable and adaptive protection against emerging threats in IoT and smart city infrastructures. RQ2--How Have Ethical Concerns Related to Privacy Been Addressed in IoT-Based Violence and Crime Detection Systems in Smart Cities? In the realm of data protection and privacy, studies emphasize the implementation of advanced security mechanisms, including encryption, data anonymization, and privacy-preserving techniques. These measures are particularly critical in IoT and smart city ecosystems, where the vast volume of collected data poses significant security and privacy risks. Notably, S3 and S21 illustrate how normalization techniques and null-value removal enhance system accuracy while minimizing risks associated with sensitive data handling. Additionally, S20, S23, and S25 explore privacy-preserving mechanisms such as differential privacy and federated learning, ensuring data protection without compromising model effectiveness. Moreover, S27 addresses data anonymization and user consent management, aligning with global privacy regulations to enhance compliance and transparency. Regarding cybersecurity, studies extensively address intrusion detection and real-time network traffic analysis.