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%.
Highlights
Our main goal was to find the optimal feature subset using the genetic algorithm, and for this, we proposed a new fitness function, which was based on Random Forest (RF); This study proposed the following weights for the genetic algorithm to obtain the optimal features from the NSL-KDD and CSE-CIC-IDS2018 datasets used in wireless communications systems
Our proposed model, which was a combination of the genetic algorithm and RF (GA-RF), outperformed these results at 98.81% Detection Rate (DR) and 0.8% false alarm rate (FAR), respectively; The results showed that the proposed Genetic Algorithms (GAs) was greatly optimized, in which the average precision was optimized by 5.65%, and the average F1-score was optimized by 8.2%
This study performed an intrusion detection analysis for false alarm rate detection using machine learning feature selection, which was integrated in a genetic algorithm with the combination of random forest in a Security of Things (SoT) model, and we further proposed a secured communication system suitable for smart healthcare
Summary
The origin of smart healthcare can be traced back to the idea of smart Earth projected by the International Business Machines Corporation (IBM) in 2009, which made reference to the unified utilization of artificial intelligence, big data, cloud computing and, mostly, the Internet of Things (IoT) to develop an interactive framework for distributing medical-related data and allowing communication among medical equipment (such as smart devices, applications and local networks), medical staff/institutions and patients. One little change of the security protocol can permit an intruder to inject unwanted packets or gain access to confidential information unnoticed. These undesirable frameworks relating to all hypothetically destructive packets and incessantly apprising the ruleset are unrealistic and enormously capital intensive [5]. A survey carried out on the latest intrusion detection system used in IoT models showed how improving ML’s efficiency and reliability is on important task [7]
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