Network intrusion detection system using an optimized machine learning algorithm

The rapid growth of the data-communications network for real-world commercial applications requires security and robustness. Network intrusion is one of the most prominent network attacks. Moreover, the variants of network intrusion have also been extensively reported in the literature. Network Intrusion Detection Systems (NIDS) have already been devised and proposed in the literature to handle this issue. In the recent literature, Kitsune, NIDS, and its dataset have received approx. 500 citations so far in 2019. But, still, the comprehensive parametric evaluation of this dataset using a machine learning algorithm was missing in the literature that could submit the best algorithm for network intrusion attack detection and classification in Kitsune. In this connection, two previous studies were reported to investigate the best machine algorithm (these two studies were reported by us). Through these studies, it was concluded that the Tree algorithm and its variants are best suited to detect and classify all eight types of network attacks available in the Kitsune dataset. In this study, the hyper-parameter optimization of the optimized Tree algorithm is presented for all eight types of network attack. In this study, the optimizer functions Bayesian, Grid Search, and Random Search were chosen. The performance has been ranked based on training and testing accuracy, training and testing cost, and prediction speed for each optimizer. This study will submit the best point hyper-parameter for the respective epoch against each optimizer

Privacy-preserving Data clustering in Cloud Computing based on Fully Homomorphic Encryption

Cloud infrastructure with its massive storage and computing power is an ideal platform to perform large scale data analysis tasks to extract knowledge and support decision-making. However, there are critical data privacy and security issues associated with this platform, as the data is stored in a public infrastructure. Recently, fully homomorphic data encryption has been proposed as a solution due to its capabilities in performing computations over encrypted data. However, it is demonstrably slow for practical data mining applications. To address this and related concerns, we introduce a fully homomorphic and distributed data processing framework that utilizes MapReduce to perform distributed computations for data clustering tasks on a large number of cloud Virtual Machines (VMs). We illustrate how a variety of fully homomorphic-based computations can be carried out to accomplish data clustering tasks independently in the cloud and show that the distributed execution of data clustering tasks based on MapReduce can significantly reduce the execution time overhead caused by fully homomorphic computations. To evaluate our framework, we performed experiments using electricity consumption measurement data on the Google cloud platform with 100 VMs. We found the proposed distributed data processing framework to be highly efficient when compared to a centralized approach and as accurate as a plaintext implementation

Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System

Automation is being fueled by a multifaceted approach to technological advancements, which includes advances in artificial intelligence, robotics, sensors, and cloud computing. The use of automated, as opposed to conventional, systems, has become more popular in recent years. Modern agricultural technology has played an important role in the development of Saudi Arabia in addition to upgrading infrastructure and plans. Agriculture in Saudi Arabia is dependent upon wells, which are insufficient in terms of water supplies. Thus, irrigation is used for agricultural fields, depending on the soil type, and water is provided to the plants. Two essential elements are necessary for farming, the first is the ability to determine the soil’s fertility, and the second is the use of different technologies to reduce the dependence of water on electrical power and on/off schedules. The purpose of this study is to propose a system in which moisture sensors are placed under trees or plants. The gateway unit transmits sensor information to the controller, which then turns on the pump and recycles the water flow. A farmland’s water pump can be remotely controlled and parameters such as moisture and flow rate can be monitored using an HTTP dashboard. In order to evaluate the applicability of IOT-based automatic wastewater irrigation systems, a pilot test was conducted using the developed framework. Theoretically, such a system could be expanded by including any pre-defined selection parameters

Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System

Automation is being fueled by a multifaceted approach to technological advancements, which includes advances in artificial intelligence, robotics, sensors, and cloud computing. The use of automated, as opposed to conventional, systems, has become more popular in recent years. Modern agricultural technology has played an important role in the development of Saudi Arabia in addition to upgrading infrastructure and plans. Agriculture in Saudi Arabia is dependent upon wells, which are insufficient in terms of water supplies. Thus, irrigation is used for agricultural fields, depending on the soil type, and water is provided to the plants. Two essential elements are necessary for farming, the first is the ability to determine the soil’s fertility, and the second is the use of different technologies to reduce the dependence of water on electrical power and on/off schedules. The purpose of this study is to propose a system in which moisture sensors are placed under trees or plants. The gateway unit transmits sensor information to the controller, which then turns on the pump and recycles the water flow. A farmland’s water pump can be remotely controlled and parameters such as moisture and flow rate can be monitored using an HTTP dashboard. In order to evaluate the applicability of IOT-based automatic wastewater irrigation systems, a pilot test was conducted using the developed framework. Theoretically, such a system could be expanded by including any pre-defined selection parameters

Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis

A smart device carries a great amount of sensitive patient data as it offers innovative and enhanced functionalities in the smart healthcare system. Moreover, the components of healthcare systems are interconnected via the Internet, bringing significant changes to the delivery of healthcare services to individuals. However, easy access to healthcare services and applications has given rise to severe risks and vulnerabilities that hamper the performance of a smart healthcare system. Moreover, a large number of heterogeneous devices accumulate data that vary in terms of size and formats, making it challenging to manage the data in the healthcare repository and secure it from attackers who seek to profit from the data. Thus, smart healthcare systems are susceptible to numerous security threats and risks, such as hardware and software-based attacks, system-level attacks, and network attacks that have the potential to place patients’ lives at risk. An analysis of the literature revealed a research gap in that most security surveys on the healthcare ecosystem examined only the security challenges and did not explore the possibility of integrating modern technologies to alleviate security issues in the smart healthcare system. Therefore, in this article, we conduct a comprehensive review of the various most recent security challenges and their countermeasures in the smart healthcare environment. In addition, an artificial intelligence (AI) and blockchain-based secure architecture is proposed as a case study to analyse malware and network attacks on wearable devices. The proposed architecture is evaluated using various performance metrics such as blockchain scalability, accuracy, and dynamic malware analysis. Lastly, we highlight different open issues and research challenges facing smart healthcare systems

Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis

A smart device carries a great amount of sensitive patient data as it offers innovative and enhanced functionalities in the smart healthcare system. Moreover, the components of healthcare systems are interconnected via the Internet, bringing significant changes to the delivery of healthcare services to individuals. However, easy access to healthcare services and applications has given rise to severe risks and vulnerabilities that hamper the performance of a smart healthcare system. Moreover, a large number of heterogeneous devices accumulate data that vary in terms of size and formats, making it challenging to manage the data in the healthcare repository and secure it from attackers who seek to profit from the data. Thus, smart healthcare systems are susceptible to numerous security threats and risks, such as hardware and software-based attacks, system-level attacks, and network attacks that have the potential to place patients’ lives at risk. An analysis of the literature revealed a research gap in that most security surveys on the healthcare ecosystem examined only the security challenges and did not explore the possibility of integrating modern technologies to alleviate security issues in the smart healthcare system. Therefore, in this article, we conduct a comprehensive review of the various most recent security challenges and their countermeasures in the smart healthcare environment. In addition, an artificial intelligence (AI) and blockchain-based secure architecture is proposed as a case study to analyse malware and network attacks on wearable devices. The proposed architecture is evaluated using various performance metrics such as blockchain scalability, accuracy, and dynamic malware analysis. Lastly, we highlight different open issues and research challenges facing smart healthcare systems

Towards Secure Searchable Electronic Health Records Using Consortium Blockchain

There are significant data privacy implications associated with Electronic Health Records (EHRs) sharing among various untrusted healthcare entities. Recently, a blockchain-based EHRs sharing system has provided many benefits. Decentralization, anonymity, unforgeability, and verifiability are all unique properties of blockchain technology. In this paper, we propose a secure, blockchain-based EHR sharing system. After receiving the data owner’s authorization, the data requester can use the data provider’s keyword search to discover relevant EHRs on the EHR consortium blockchain and obtain the re-encryption ciphertext from the proxy server. To attain privacy, access control and data security, the proposed technique uses asymmetric searchable encryption and conditional proxy re-encryption. Likewise, proof of permission serves in consortium blockchains as the consensus method to ensure the system’s availability. The proposed protocol can achieve the specified security goals, according to the security analysis. In addition, we simulate basic cryptography and put the developed protocol into practice on the Ethereum platform. The analysis results suggest that the developed protocol is computationally efficient

Towards Secure Searchable Electronic Health Records Using Consortium Blockchain

There are significant data privacy implications associated with Electronic Health Records (EHRs) sharing among various untrusted healthcare entities. Recently, a blockchain-based EHRs sharing system has provided many benefits. Decentralization, anonymity, unforgeability, and verifiability are all unique properties of blockchain technology. In this paper, we propose a secure, blockchain-based EHR sharing system. After receiving the data owner’s authorization, the data requester can use the data provider’s keyword search to discover relevant EHRs on the EHR consortium blockchain and obtain the re-encryption ciphertext from the proxy server. To attain privacy, access control and data security, the proposed technique uses asymmetric searchable encryption and conditional proxy re-encryption. Likewise, proof of permission serves in consortium blockchains as the consensus method to ensure the system’s availability. The proposed protocol can achieve the specified security goals, according to the security analysis. In addition, we simulate basic cryptography and put the developed protocol into practice on the Ethereum platform. The analysis results suggest that the developed protocol is computationally efficient

SVBE: searchable and verifiable blockchain-based electronic medical records system.

Central management of electronic medical systems faces a major challenge because it requires trust in a single entity that cannot effectively protect files from unauthorized access or attacks. This challenge makes it difficult to provide some services in central electronic medical systems, such as file search and verification, although they are needed. This gap motivated us to develop a system based on blockchain that has several characteristics: decentralization, security, anonymity, immutability, and tamper-proof. The proposed system provides several services: storage, verification, and search. The system consists of a smart contract that connects to a decentralized user application through which users can transact with the system. In addition, the system uses an interplanetary file system (IPFS) and cloud computing to store patients’ data and files. Experimental results and system security analysis show that the system performs search and verification tasks securely and quickly through the network