Security of Things Intrusion Detection System for Smart Healthcare

Abstract

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

“…The proposed model offers maximum accuracy of 99.21%, precision of 98.93%, and a detection rate of 99.59%. Iwendi et al ( 18 ) proposed an intrusion detection system in the Security of Things (SoT) paradigm for smart healthcare and will continue to impact medical infrastructures. Their study used the NSL-KDD dataset on RF, Naive Bayes (NB), and logistic regression classifiers for machine learning.…”

Cloud-Based Bug Tracking Software DefectsAnalysis using Deep Learning

“…The development of a software defect detection model with high accuracy has proven to be a herculean task. Over the past decade, several approaches have been proposed, but most of them have not been able to meet the standards in their accuracy of predicting and detecting the defects [5,12]. According to [24]], the introduction of network computing technologies like cloud computing has provided users all around the world with an affordable and flexible network-based service provision scheme.…”

Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things

“…The proposed protocol was experimented on in the Ethereum platform, and researchers discovered that its computational efficacy was quite high. The authors of [32] discussed more on the use of blockchain in the healthcare sector and suggested a framework to execute blockchain-based technology for electronic health records.…”

“…The SHA-256 architecture is shown in Figure 9. This comprises additional modulo 2 32 and a bitwise rotation operation that uses different constants (ch, ∑ 1, Ma, and ∑ 0). The development of SHA-256 involves several phases, as discussed here.…”