With the advent of smart city networks and increased utilization of vehicles, the Internet of Vehicles (IoV) has attracted more attention from researchers. But, providing security to this type of network is one of the challenging and demanding tasks in the present day. For this purpose, the conventional works developed many networking frameworks and methodologies to enhance the privacy and security of smart city systems. Still, it has the significant limitations of high complexity in algorithm design, requires more time consumption for processing, reduced maintenance, and does not require proper authentication verification. Therefore, the primary purpose of this work is to develop a new security model for smart city networks using a combination of methodologies. Here, the Collaborative Mutual Authentication (CMA) mechanism is used to validate the identity of users based on the private key, public key, session key, and generated hash function. In addition, the Meta-heuristic Genetic Algorithm – Random Forest (MGA-RF) technique is deployed to detect the attacks in the network, ensuring the security of the smart city. During an evaluation, the proposed authentication-based security mechanism’s performance is validated using various parameters, and the results are compared with the recent state-of-the-art models.

With the increase of cyber-attacks and security threats in the recent decade, it is necessary to safeguard sensitive data and provide robust protection to information systems and computer networks. In this paper, an anomaly-based network outlier detection system (NODS) is proposed and optimized to check and classify the incoming network traffic stream’s behaviours that affect the computer networks. The proposed NODS has high classification efficiency. Network connection events classified as outliers are reported to the network admin to drop and block its packets. The NSL-KDD and CICIDS2017 intrusion datasets were employed to build the proposed system and test its detection capabilities. Sequential scenarios were implemented to optimize the system’s effectiveness. Network features were normalized by min-max and Z-Score approaches, while the relevant features were selected individually by the principal component analysis (PCA) and correlated features selection (CFS) techniques. Support vector machine (SVM) and Gaussian Naive Bayes (GNB) algorithms are used to build the detection model, while the Genetic algorithm (GA) was employed to tune their control parameters. The obtained evaluation results proved that the proposed SVM based NODS is characterized by low false alarms and detection time as well as high classification accuracy. Furthermore, a comparative analysis was conducted with other existing techniques, and the results obtained demonstrate the effectiveness of the proposed SVM-IDS.

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC.