## 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{3}{*}{S1} & Detection of terrorist activities on & CNN (Convolutional & Images and videos of & Identification of criminal & Alert system for human \\  & social media & Neural Networks) & \multirow{3}{*}{Images and videos of } & activities through images & operators to take \\  & & social media & Neural Networks & and videos on social media & immediate action \\ \hline \multirow{3}{*}{S2} & Intrusion detection & Deep Migration Learning & \multirow{3}{*}{KDD CUP 99} & N/A & N/A \\  & & Model (DMLM) & & & \\ \hline \multirow{3}{*}{S3} & Real-time intrusion detection & CNN-LSTM (Long & \multirow{3}{*}{Bot-IoT, IoTID20} & DDoS (Distributed Denial & \multirow{3}{*}{N/A} \\  & detection & Short-Term Memory) & & & \\ \hline \multirow{3}{*}{S4} & Cyberattack detection & Artificial Neural Network (ANN) & \multirow{3}{*}{UNSW NB15} & IoT attacks, unauthorized & \multirow{3}{*}{N/A} \\  & & (ANN) & & & \\ \hline \multirow{3}{*}{S5} & Energy theft detection & CNN & Real and synthetic & N/A & Detection of cyberattacks \\  & detection & & data & & on smart meters \\ \hline \multirow{3}{*}{S6} & Intrusion detection & LSTM-CNN & KDDCup99 & N/A & Cybercrime (malicious flow) \\ \hline \multirow{3}{*}{S7} & Host intrusion detection for IoT & O-CNN (Optimized CNN) & BoT-IoT & N/A & Cybercrime: DDoS, data & \multirow{3}{*}{O-CNN (Optimized CNN)} & \\  & & & & & \\ \hline \multirow{3}{*}{S8} & Intrusion detection system (IDS) & Deep Neural Network (DNN) & IoT-23 y & \multirow{3}{*}{N/A} & Anomaly detection and \\  & system (IDS) & & MQTT-IoT-IDS2020 & & & \\ \hline \multirow{3}{*}{S9} & DDoS detection & DNN with & \multirow{3}{*}{HYParametrization} & CICDDoS 2019 & \multirow{3}{*}{N/A} & Cyberattacks (DDoS, including TCP Syn, UDP flood, and ICMP attacks) \\  & & Hyperarametrization & dataset & & \\ \hline \multirow{3}{*}{S10} & Attack detection in IoT & Deep Belief Networks (DBNs) + CNN & \multirow{3}{*}{UNSW-NB15} & N/A & Property crimes \\  & & (DBNs) & & & \\ \hline \multirow{3}{*}{S11} & Intrusion detection & DNN, Extreme Gradient Boosting (XGBoost) & \multirow{3}{*}{UNSWNB15} & N/A & Detects network \\  & & Boosting (XGBoost) & & & \\  & & & & & \\ \hline \multirow{3}{*}{S12} & IDS & Multi-Layer & \multirow{3}{*}{BlueTack dataset} & N/A & Cybercrime \\  & & Perceptron (MLP) & & & \\ \hline \multirow{3}{*}{S13} & Surveillance of & \multirow{3}{*}{Yolo7 with transfer learning} & \multirow{3}{*}{Roboflow datasets} & Street violence & Property crimes, \\  & smart homes & & & & \\ \hline \multirow{3}{*}{S14} & IoT-Defender & Genetic Algorithm (GA)-LSTM & \multirow{3}{*}{UNSW-NB15} & N/A & Cybercrime \\ \hline \multirow{3}{*}{S15} & IDS & GRU (Gated Recurrent Unit) & Data of CPS & N/A & DoS attacks \\ \hline \multirow{3}{*}{S16} & Self-adaptive traffic & \multirow{3}{*}{LSTM} & \multirow{3}{*}{ToN-IoT, InSDN} & \multirow{3}{*}{N/A} & DoS/DDDoS attacks, XSS (Cross-Site Scripting), \\  & identification intrusion & & & & (Gras Brute Force Attack), \\  & detection system & & & & MitM, Backdoor, \\  & & & & & Probe, Web \\ \hline \multirow{3}{*}{S17} & IDS & X-DeepID (Hybrid & \multirow{3}{*}{ToN-IoT} & N/A & Enhances intrusion \\  & & CNN-LSTM) & & & detection in IoT \\ \hline \multirow{3}{*}{S18} & Lightweight & Meta-Learning Ensemble & \multirow{3}{*}{KDD99} & \multirow{3}{*}{N/A} & Botnet traffic detection \\  & meta-learning BotNet & (Super Learner, Subsemble, & & & \\ attack detection & Sequential Learner) & & & & categorized as malicious \\  & & & & & network flows \\ \hline \multirow{3}{*}{S19} & IoT-based IDS with & CNN-RNN-LSTM, & \multirow{3}{*}{MQTT-IoT-IDS2020} & \multirow{3}{*}{N/A} & Message Queuing \\  & Deep Learning & DNN, LSTM & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{S20} & Attack detection in & CNN optimized with SE-Net & \multirow{3}{*}{Network traffic} & N/A & DDoS attacks, Botnets, \\  & IoT network traffic & and Capsule Networks & & & network traffic anomalies \\ \hline \hline \end{tabular}
\end{table}