Vehicular networks improve quality of life, security, and safety, making them crucial to smart city development. With the rapid advancement of intelligent vehicles, the confidentiality and security concerns surrounding vehicular ad hoc networks (VANETs) have garnered considerable attention. VANETs are intrinsically more vulnerable to attacks than wired networks due to high mobility, common network medium, and lack of centrally managed security services. Intrusion detection (ID) servers are the first protection layer against cyberattacks in this digital age. The most frequently used mechanism in a VANET is intrusion detection systems (IDSs), which rely on vehicle collaboration to identify attackers. Regrettably, existing cooperative IDSs get corrupted and cause the IDSs to operate abnormally. This article presents an approach to intrusion detection based on the distributed federated learning (FL) of heterogeneous neural networks for smart cities. It saves time and resources by using the most efficient intruder detection approach. First, vehicles use a federated learning technique to develop local, deep learning-based IDS classifiers for VANET data streams. They then share their locally learned classifiers upon request, significantly reducing communication overhead with neighboring vehicles. Then, an ensemble of federated heterogeneous neural networks is constructed for each vehicle, including locally and remotely trained classifiers. Finally, the global ensemble model is again shared with local devices for their updating. The effectiveness of the suggested method for intrusion detection in VANETs is evaluated using performance indicators such as attack detection rates, classification accuracy, precision, recall, and F1 scores over a ToN-IoT data stream. The ID model shows 0.994 training and 0.981 testing accuracy.

With growing awareness and concerns regards to Cloud Computing and Information Security, there is growing awareness and usage of Security Algorithms into data systems and processes. This paper presents a brief overview and comparison of Cryptographic algorithms, with an emphasis on Symmetric algorithms which should be used for Cloud based applications and services that require data and link encryption. In this paper we review Symmetric and Asymmetric algorithms with emphasis on Symmetric Algorithms for security consideration on which one should be used for Cloud based applications and services that require data and link encryption

The state-of-the-art Credit Card Fraud Detection (CCFD) systems utilize supervised rule-based schemes to identify fraudulent behaviors in static data. Designing an efficient framework for CCFD is particularly challenging due to the imbalanced distribution of positive and negative classes in data. Conventional works overlook hidden transaction patterns and the performance evaluation methods of algorithm or system. The early models have also been criticized for over-fitting, i.e. models having less prediction accuracy than training or classification accuracy. However, the categorical accuracy is more important than overall accuracy for extremely imbalanced data; most of the previous works do not evaluate their model on the basis of categorical accuracy. In this work, we propose a predictive framework (DEAL) based on novel Deep Ensemble Learning for detecting fraudulent transaction in real-time data stream. The proposed framework is adaptive against data imbalance and robust against latent transaction patterns such as spending behavior. To build the model and predict the fraud, the transactions from real data from a major bank are passed as tensors to ensemble of Deep Learning Network. The adaptive optimization is suggested to minimize the objective function and improve fraud prediction. All Experiments are conducted in Python using scikit-learn, Google TensorFlow, and Keras Deep Learning libraries. The results demonstrate its superiority over state-of-the-art methods in catching frauds.

Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.

Cloud computing has started to gain acceptance for adoption and implementation among organizations, however, this new technology area has already started to deal with security, performance and availability challenges. Within Cloud Security issues being paramount for the corporates, private enterprises, the denial of service attacks are rated as the highest priority threat to the cloud environments. This study presents a review on the academic literature research work on the DDoS attack on Cloud, introduces a new DDoS Classification taxonomy and proposes parameters for determining an effective DDoS solution.