With the increasing rates of interconnected Internet of Things (IoT) devices within Software-Defined Networking (SDN) environments, distributed denial of service (DDoS) attacks have become increasingly common. As a result of this challenge, novel detection and classification methods must be developed based on the unique characteristics of SDN-supported IoT networks. This paper proposes a novel approach to detecting and categorizing DDoS attacks that has been optimized specifically for such environments. As part of our methodology, we integrate convolutional neural networks (CNN) and long-short-term memory (LSTM) models into a multilevel deep neural network architecture. With this hybrid architecture, complex spatial and temporal patterns can be automatically extracted from raw network traffic data to facilitate comprehensive analysis and accurate identification of DDoS attacks. We validate the efficacy and superiority of our proposed approach over traditional machine learning algorithms by conducting rigorous experiments on real-world datasets. Our findings underscore the potential of the multi-level deep neural network approach as a robust and scalable solution for mitigating DDoS attacks in SDN-supported IoT networks. By improving network security and resilience to evolving threats, our methodology contributes to safeguarding critical infrastructures in the era of interconnected IoT ecosystems.

With the proliferation of connected devices in the Internet of Things (IoT), cybersecurity threats have increased. Identifying malicious attacks in IoT requires advanced techniques tailored to this ecosystem. Existing algorithms have limited effectiveness in detecting obfuscated IoT malware. This study proposes the Elucidating Cybersecurity-promulgated Malware Taxonomy (ECMT) framework, combining memory analysis and ensemble machine learning, to enhance IoT malware categorization. ECMT integrates Support Vector Classification, Quadratic Discriminant Analysis, and AdaBoost on forensic artifacts from memory dumps to improve detection across families like ransomware, spyware, and trojans. ECMT can enable intrusion prevention, information protection, and cybercrime deterrence in IoT environments. Experiments on a balanced dataset indicate AdaBoost achieved 96% accuracy, demonstrating ECMT’s capabilities against complex IoT threats. The integrated approach provides automated, adaptable detection scalable to large IoT deployments through efficient linear models and robust ensemble learning. ECMT addresses concept drift and interpretability via retraining and explanation techniques. Results highlight advanced memory analysis and optimized machine learning classifiers as a promising solution for robust IoT malware detection despite adversaries’ evolving tactics. Further research can extend platform support, harden models against attacks, and refine streaming input. ECMT establishes a foundation for IoT security by unifying memory forensics, optimized neural architectures, and tailored ensemble learning.

The widespread dissemination of fake news presents a critical challenge to the integrity of digital information and erodes public trust. This urgent problem necessitates the development of sophisticated and reliable automated detection mechanisms. This study addresses this gap by proposing a robust fake news detection framework centred on a transformer-based architecture. Our primary contribution is the application of the Bidirectional Encoder Representations from Transformers (BERT) model, uniquely enhanced with a progressive training methodology that allows the model to incrementally learn and refine its understanding of the linguistic nuances that differentiate factual reporting from fabricated content. The framework was rigorously trained and evaluated on the large-scale WELFake dataset, comprising 72,134 articles. Our findings demonstrate the model’s exceptional performance, achieving an accuracy of 95.3%, an F1-score of 0.953, precision of 0.952, and recall of 0.954. Comparative analysis confirms that our approach significantly outperforms traditional machine learning classifiers and other standard transformer-based implementations, highlighting its superior ability to capture complex contextual dependencies. These results underscore the efficacy of our enhanced BERT framework as a powerful and scalable solution in the ongoing fight against digital misinformation.

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

Decision making system in smart healthcare of Heart failure (HF) disease can facilitate patients remotely, which is an active research area in medical fields, industrial applications, and academia. However, implementation of HF decision making systems on the edge using consumer electronic devices face challenges in processing huge suboptimal heart disease data due to limited computational capabilities. To alleviate such challenges, we introduce an optimize method of HF prediction using four statistical parameters such as strong correlation coefficient, mutual information, analysis of variance (ANOVA) f-test and chi-squared χ2. These parameters select best features through maximum likelihood based threshold value. We then use mean value fusion approach to construct a combine feature space. Afterwards, combine features further optimized via sequential forward selection (SFS) algorithm, which provide best subset of variables. Finally, a novel deep Multi-tier long short-term memory (LSTM) trained on top of the acquired features for diagnosis of HF. We conduct extensive experimentation on Cleveland heart disease dataset, which initially consist of 13 features. Moreover, after execution of our proposed optimize strategy, the size of features restricted to 8. Consequently, these optimal features provide enormous help in diagnosis procedure of HF. The competence of our propose technique evaluated using various metrics such as accuracy, sensitivity, precision, specificity, F1-score, Mathew correlation coefficient (MCC), receiver operating characteristic (ROC) and area under curve (AUC). As shown by the experimental results, the proposed features optimization and multi-tier LSTM strategy improve accuracy of 8.9% compared with different traditional classifiers.

The growth of network technologies has led to a rise in network traffic diversity, which has resulted in increased complexity within each class. However, distinguishing between malicious and benign actions has become more challenging as the two have started to overlap. Consequently, false alarms have become a common issue, which can cause problems in detection models. We proposed a novel approach to enhance intrusion detection based on supervised contrastive learning that combines a maximum-margin-based approach for malware detection in an end-to-end process. This approach differs from traditional methods, where feature selection and detection are considered separate stages. Our model guarantees robustness and correlation between these two processes. To handle the challenges posed by Variation within a class and similarities between different classes, we use the margin-based supervised contrastive learning (MbSCL) technique. This technique involves a two-stage learning process that enables the network to learn embeddings that bring closer the similar data points and separate dissimilar ones. We employ a specialized loss function that combines supervised contrastive loss and marginal loss to improve the accuracy of the classification. This process balances the effectiveness of embedding separation and classification to improve overall performance. Mathematical theorems and proofs support the optimization process, which provides insights into contrastive loss in similarity learning. This two-stage learning approach and loss function design are particularly useful for malware detection, as they enable the identification of subtle data patterns and anomalies in network traffic.