## 3 Results

Table 2: Deep learning models used for the development of violence and crime detection in IoT scenarios. \begin{table}
\begin{tabular}{c 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}{*}{S21} & Intrusion detection in smart cities & & & & DoS/DDoS attacks, & \\  & & & & & ransomware, port \\  & Intrusion detection in & & & & & scanning, \\  & & & & & Man-in-the-Middle \\  & & & & & (MITM) attacks, malware, \\  & & & & & injection attacks \\ \hline \multirow{4}{*}{S22} & \multicolumn{4}{c}{Cascaded Adaptive} & & & \\  & & Neuro-Fuzzy Inference & & & & Botnet malware attacks \\  & & System (CANFIS) + Modified & & & network intrusion & (Mirai), UDP Flooding, \\  & & Deep Reinforcement & & & & SMTP spam \\  & & Learning (MDRL) & & & \\ \hline \multirow{4}{*}{S23} & Decision support & Spiral-Net (Spiral Neural & & & \\  & system for facial & Network), SNET (Spiking & CUFS, CUFSF, IIT & & \\  & & Neural Network-based & & photo-sketch dataset & recognition for & \\  & & Intrusion Detection System) & & & forensic support & \\ \hline \multirow{4}{*}{S24} & IDS & GA-CNN & MQTT-IoT-IDS2020 & N/A & DDoS, injection, \\  & & & & & & ransomware \\ \hline \multirow{4}{*}{S25} & IDS & DHLNN optimized with & & & \\  & HCSGA (Hybrid Chicken & & & \\  & Swarm Genetic Algorithm) & & & & \\ \hline \multirow{4}{*}{S26} & Smart urban & & & & \\  & management system & CNN & Images of CCTV & Detection of & \\  & & & & & weapons (guns) & N/A \\ \hline \multirow{4}{*}{S27} & IoT intrusion & Hybrid CNN-BILSTM with & & N\_BaIoT (data of & \\  & detection & Transfer Learning & attacks botnet). & N/A & \begin{tabular}{c} Detection and \\ classification of Mirai and \\ BASHLITE attacks \\ \end{tabular} \\ \hline \multirow{4}{*}{S28} & Violence detection in & & & & & \\  & industrial & & & & \\  & surveillance videos & & & & \\  & using IoT & & & & \\ \hline \multirow{4}{*}{S29} & IoT-based smart & & & & \\  & security system & & & & \\  & for homes & & & & \\ \hline \multirow{4}{*}{S30} & Intrusion detection & & & & \\  & using explainable & & & & \\  & artificial & & & & \\  & intelligence (XAI) & & & & \\ \hline \multirow{4}{*}{S31} & Multi-cloud intrusion & & & & \\  & detection & & & & \\  & system (MCIDS) & & & & \\ \hline \multirow{4}{*}{S32} & Intrusion detection & DNN & CICDDoS2019 & N/A & DDoS attacks \\ \hline \multirow{4}{*}{S33} & Electricity & Stacked CNN + Random & & & \\  & theft detection & Forest & & & \\ \hline \multirow{4}{*}{S34} & Intrusion detection system & Voting Classifier & ToN-IoT telemetry & N/A & \
\begin{tabular}{c} Attacks like DDoS, \\ transomware, XSS, etc. \\ \end{tabular} \\ \cline{2-6}  & Detection of DDoS & Hybrid (Restricted Boltzmann & & & \\  & and Replay attacks & Machin (RBM) + CNN) & Smart soil & N/A & DDoS, Replay \\ \hline \multirow{4}{*}{S36} & Intrusion detection & LSTM & Edge-IIoT & N/A & DDoS, SQL injection, \\  & in IoT & & & & \\ \hline \multirow{4}{*}{S37} & Attack detection & & & & \\  & in IoT & Hybrid (CNN + DBN) & N/A & N/A &
\begin{tabular}{c} DDoS attacks, \\ transformer, \\ SQL injections \\ \end{tabular} \\ \hline \multirow{4}{*}{S38} & Hybrid deep learning & Stacked Autoencoder (SAE) & & & Not applicable (focused on \\  & & and BiLSTM & & detection of \\  & classification of & (Bidirectional LSTM) & malicious URLs) & & \\ \hline \multirow{4}{*}{S39} & Hybrid deep learning & & & & \\  & with Blockchain and & & & \\  & IoT for smart & Machine (SVM) & & & \\ \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)Additionally, hybrid CNN-LSTM architectures have demonstrated remarkable efficiency in intrusion detection and threat classification, as they enable the extraction of both spatial and temporal attack characteristics. Studies such as S3, S6, S17, and S45, highlight this approach, where CNNs classify malicious activities while LSTM networks capture sequential attack behaviors.