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Top1. Introduction
The Internet of Everything (IoE) marks a step forward from the Internet of Things (IoT). IoT connects various devices into internet-like networks such as RFID or NFC, to enable end-to-end data transfer from users to the cloud. It is one way of communication with the sole purpose of collecting data from the environments these gadgets are placed in. On the other hand, IoE supports bidirectional communication through intelligent networks which include both gadgets and people. The bottom layer is aware of its environment and does more than mere data collection.
With advancements in IoE, millions of devices over the internet can conduct communication. IoE (Miraz et al., 2018; Patel & Patel, 2016; Ryan & Watson, 2017) has its application in the healthcare sector, providing real-time services with reduced healthcare costs. It has enhanced the performance, precision, and accuracy of medical procedures.
Medical networks (Dong et al., 2016; Dwivedi et al., 2019; Kumar & Bairavi, 2016) comprise Internet of Medical Things (IoMT) devices that record patient information, actuators that display results, processing units to generate reports, and finally data management units such as cloud storage. Such complex systems that include manual operations and automation at higher levels see high traffic influx on a day-to-day basis. Network security in such cases poses a fair challenge for two reasons - latency and accuracy. While any security system needs to be robust, a breach of medical data may cost a patient their life, data rate cannot be compromised in serious health situations. Security checks are required to be highly optimized to deliver real-time data. If the security system has high sensitivity, any legitimate change in normal state may raise numerous false alarms. The healthcare sector has faced a massive number of cybersecurity attacks in recent decades. The security of a network majorly focuses on authentication, confidentiality, and integrity (Muhammad et al., 2017; Yeole & Kalbande, 2016). Existing Intrusion Detection Systems (IDS) (Foley, 2021; Sunke, 2008; Tiwari et al., 2017; Xu et al., 2013) are prone to stealthy attacks like Man in the Middle attack, where parameters like CPU usage and loop latency see a negligible change and end up undetected. Dynamic networks like IoE require dynamic security systems that can adapt to the new normal seamlessly, without compromising on the detection rate.
Various methods have been proposed for the implementation of IDS, the Artificial Immune System (AIS) (Balthrop et al., 2002; Dasgupta et al., 2004; Read et al., 2012) being one of them. Biological immune systems have antibody cells called lymphocytes that provide immunity to the body from pathogens. These antibody cells are closely modelled as detectors in the AIS and have the same properties as lymphocytes and other antibodies. AIS integrates the principles and fundamentals of the biological immune system (Srivastava & Lin, 2021), incorporating error resistance, dynamic adaptation, real-time self-detection, and computational facilities. Lymphocytes are referred to as negative detectors as they are qualified for binding to non-self-cells.
Like all predictive models, AIS can produce false results in the form of false negatives and false positives. A high false positives value would indicate autoimmunity, while a high false negatives count brings the detection rate down. This paper lays emphasis on optimizing the generation of dynamic detectors, using Negative Selection; that can distinguish between non-self and self-cells. Figure 1 depicts a simplified diagrammatic version of the artificial immune system, where specific detectors are generated to only detect non-self-antigens. This paper attempts to generate detectors in a multidimensional space, with each dimension representing a parameter that categorizes any point in space into self and non-self.
The main contributions to this paper are as follows: