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The primary purpose of this paper is to introduce the drift detection method-online random forest (DDM-ORF) model for intrusion detection, combining DDM for detecting concept drift and ORF for incremental learning. The paper addresses the challenges of dynamic and nonstationary data, offering a solution that continuously adapts to changes in the data distribution. The goal is to provide effective intrusion detection in real-world scenarios, demonstrated through comprehensive experiments and evaluations using Apache Spark.

The paper uses an experimental approach to evaluate the DDM-ORF model. The design involves assessing classification performance metrics, including accuracy, precision, recall and F-measure. The methodology integrates Apache Spark for distributed computing, using metrics such as processed records per second and input rows per second. The evaluation extends to the analysis of IP addresses, ports and taxonomies in the MAWILab data set. This comprehensive design and methodology showcase the model’s effectiveness in detecting intrusions through concept drift detection and online incremental learning on large-scale, heterogeneous data.

The paper’s findings reveal that the DDM-ORF model achieves outstanding classification results with 99.96% accuracy, demonstrating its efficacy in intrusion detection. Comparative analysis against a convolutional neural network-based model indicates superior performance in anomalous and suspicious detection rates. The exploration of IP addresses, ports and taxonomies uncovers valuable insights into attack patterns. Apache Spark evaluation attests to the system’s high processing rates. The study emphasizes the scalability, availability and fault tolerance of DDM-ORF, making it suitable for real-world scenarios. Overall, the paper establishes the model’s proficiency in handling dynamic, nonstationary data for intrusion detection.

The research acknowledges certain limitations, including the potential challenge of DDM detecting only frequency changes in class labels and not complex concept drifts. The incremental random forest’s reliance on memory may pose constraints as the forest size increases, potentially leading to overfitting. Addressing these limitations could involve exploring alternative concept drift detection algorithms and implementing ensemble pruning techniques for memory efficiency. Further research avenues may investigate algorithms balancing accuracy and memory usage, such as compressed random forests, to enhance the model’s effectiveness in evolving data environments.

The study’s practical implications are noteworthy. The proposed DDM-ORF model, designed for intrusion detection through concept drift detection and online incremental learning, offers a scalable, available and fault-tolerant solution. Leveraging Apache Spark and Microsoft Azure Cloud enhances processing capabilities for large data sets in dynamic, nonstationary scenarios. The model’s applicability to heterogeneous data sets and its achievement of high-accuracy multi-class classification make it suitable for real-world intrusion detection. Moreover, the auto-scaling features of Microsoft Azure Cloud contribute to adaptability, ensuring efficient resource utilization without downtime. These practical implications underscore the model’s relevance and effectiveness in diverse operational contexts.

The DDM-ORF model’s social implications are significant, contributing to enhanced cybersecurity measures. By providing an effective intrusion detection system, it helps safeguard digital ecosystems, preserving user privacy and securing sensitive information. The model’s accuracy in identifying and classifying various intrusion attempts aids in mitigating potential cyber threats, thereby fostering a safer online environment for individuals and organizations. As cybersecurity is paramount in the digital age, the social impact lies in fortifying the resilience of networks, systems and data against malicious activities, ultimately promoting trust and reliability in online interactions.

The DDM-ORF model introduces a novel approach to intrusion detection by combining drift detection and online incremental learning. This originality lies in its utilization of the DDM-ORF algorithm, offering a dynamic and adaptive system for evolving data. The model’s contribution extends to its scalability, fault-tolerance and suitability for heterogeneous data sets, addressing challenges in dynamic, nonstationary environments. Its application on a large-scale data set and multi-class classification, along with integration with Apache Spark and Microsoft Azure Cloud, enhances the field’s understanding and application of intrusion detection, providing valuable insights for securing digital infrastructures.

The purpose of this study is to test the Internet of things (IoT) devices with respect to reliability and quality.

In this paper, the authors have presented the analysis on design metrics such as perception, communication and computation layers for a constrained environment. In this paper, based on their literature survey, the authors have also presented a study that shows multipath routing is more efficient than single-path, and the retransmission mechanism is not preferable in an IoT environment.

This paper discusses the reliability of various layers of IoT subject methodologies used in those layers. The authors ran performance tests on Arduino nano and raspberry pi using the AES-128 algorithm. It was empirically determined that the time required to process a message increases exponentially and is more than what benchmark time estimates as the message size is increased. From these results, the authors can accurately determine the optimal size of the message that can be processed by an IoT system employing controllers, which are running 8-bit or 64-bit architectures.

The authors have tested the performance of standard security algorithms on different computational architectures and discuss the implications of the results. Empirical results demonstrate that encryption and decryption times increase nonlinearly rather than linearly as message size increases.

The purpose of this paper is to ensure the anonymity and security of health data and improve the integrity and authenticity among patients, doctors and insurance providers. Simulation and validation algorithms are proposed in this work to ensure the proper implementation of the distributed system to secure and manage healthcare data. The author also aims to examine the methodology of Wireless Body Area Networks and how it contributes to the health monitoring system.

Wireless Body Area Network (WBAN) plays an important role in patient health data monitoring. In this paper, a novel framework is designed and proposed to generate data by the sensor machines and be stored in the cloud, and the transactions can be secured by blockchain. DNA cryptography is used in the framework to encrypt the hashes of the blocks. The proposed framework will ensure the anonymity and security of the health data and improve the integrity and authenticity among the patients, doctors and insurance providers.

Cloud Computing and Distributed Networking have transformed the IT industry and their amalgamation with intelligent systems would revolutionize the Healthcare Industry. The data being generated by devices is huge and storing it in the cloud environment would be a better decision. However, the privacy and security of healthcare data are still a concern because medical data is very confidential and desires to be safe and secure. The blockchain is a promising distributed network that ensures the security aspect of the data and makes the transactions authentic and transparent. In this work, the data is collected using various sensor devices and is transmitted to the cloud through the WBAN via the blockchain network.

In this paper, a framework for securing and managing the healthcare data generated by intelligent systems is proposed. As the data generated by these devices are heterogeneous and huge in nature, the cloud environment is chosen for its storage and analysis. Therefore, the transactions to and from the cloud are secured by using the blockchain-based distributed network.

The target end-users of our system are the patients to keep themselves informed and healthy, healthcare providers to monitor the conditions of their patients virtually, and the health insurance providers to have a track of the history of the patients, so that no fraudulent claims can be made.

The target end-users of our system are the patients for keeping themselves informed and healthy, healthcare providers for monitoring the conditions of their patients virtually and the health insurance providers to have a track of the history of the patients, so that no fraudulent claims can be made.