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Bio-Inspired Computing and IoT Networks
Published in Sanjeev J. Wagh, Manisha Sunil Bhende, Anuradha D. Thakare, Energy Optimization Protocol Design for Sensor Networks in IoT Domains, 2023
Sanjeev J. Wagh, Manisha Sunil Bhende, Anuradha D. Thakare
The immunity system works on distributed processing mechanism as it does not possess a central controller. The detection and response are executed locally and immediately withhold consulting the central organ. Hence this mechanism is easily adopted for distributed computing in a networking environment to control and manage the networks. The immune system works on a self-regulation basis. It responds from low to very strong reaction for protections, while responding to the strong range, use a lot of resources to present the attack. Once the invader is eliminated, the immune system regulates itself by releasing the adopted resources. Hence this mechanism is strongly used in sensor networks for self-organization and configurations. As the immune system has a dedicated protection mechanism, is used in self-protection systems. The artificial immune system-based modeling is being used in many domains like solving optimization problems, computer security, design of intrusion detection system, fault detection and tolerance, pattern reorganizations, distributed learning, sensor networks, job scheduling, recommendations system, etc.
Dialectics of Nature: Inspiration for Computing
Published in Nazmul Siddique, Hojjat Adeli, Nature-Inspired Computing, 2017
The biological immune system defends the body from foreign pathogens. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, while the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. The remarkable features and information processing of immune systems have led to new computational techniques. On the basis of the principles of biological immune systems, models of artificial immune systems (AIS) were proposed. The first model of an AIS was formalized by Farmer et al. (1986) in the 1980s and stipulated the interaction between antibodies mathematically. The model was further interpreted by Bersini and Varela (1990) with numerous refinements, and the combination of these two approaches forms the cornerstone of AIS. The model was seen as having computationally useful properties, and provided a network-based approach distinct from both neural networks and GAs. Several models of the AIS have been proposed and applied for solution of real-world science and engineering problems (Hofmeyr and Forrest, 2000)
Intrusion Detection and Prevention in Wireless Sensor Networks
Published in Shafiullah Khan, Al-Sakib Khan Pathan, Nabil Ali Alrajeh, Wireless Sensor Networks, 2016
Abror Abduvaliyev, Al-Sakib Khan Pathan, Jianying Zhou, Rodrigo Roman, Wai-Choong Wong
A totally different approach from traditional anomaly detection techniques by Kim et al. [56] introduces a biologically inspired algorithm, namely artificial immune system (AIS). The authors in this work show the similarities between the properties of WSNs and a biological immune system. Analogies between sensor network with directed diffusion routing and biological tissues are presented. The concept of a danger theory–based AIS employing dendritic cell algorithm (DCA) for anomaly detection of interest cache poisoning attack is also proposed. A sensor node employing directed diffusion maintains an interest cache and a data cache tables. When a node receives a packet, directed diffusion updates both caches and extracts the signals and antigens (e.g., bogus interest packets) from the received packets and caches, and then passes to the DCA. It evaluates whether antigens are benign or malicious. The algorithm is implemented in J-Sim and also is tested in TOSSIM [57]. Drawbacks: There is no information available about performance and statistical analysis that might prove the effectiveness of the approach.
An Improved Negative Selection Algorithm-Based Fault Detection Method
Published in IETE Journal of Research, 2022
A. Abid, M. T. Khan, I. U. Haq, S. Anwar, J. Iqbal
Despite the different approaches and techniques that have been proposed for fault detection, still there is pressing need for the development of efficient fault detection techniques that are less problem-specific and model-free. Recently, artificial immune system (AIS) has attracted much of the research community’s attention for the purpose of anomaly detection. One of the prominent AIS approaches includes negative selection principle in which the immune system is trained to distinguish between nonself and self-elements [17,18]. Negative selection algorithm (NSA) is a binary classification method inspired from negative selection principle of natural immune system. The model-independent architecture of NSA also has single-class training data requirement. This paper presents an improved negative selection algorithm-based fault detection method by incorporating two stages with the detector generation process of standard NSA. The primary accomplishment of this study is improved detector coverage with the incorporation of an efficient detector clustering technique. The overall improvement manifests as higher fault detection accuracy and quick detection with reduced online detection time.
Three-Periods Optimization Algorithm: A New Method for Solving Various Optimization Problems
Published in IETE Journal of Research, 2022
Mohammad Dehghani, Pavel Trojovský, Štěpán Hubálovský, Theyab R. Alsenani, Jaswinder Singh
De Castro and Timmis designed the first artificial immune algorithms in 1986. In general, Artificial Immune Systems (AISs) are part of biology-inspired algorithms. These types of algorithms are computer algorithms whose principles and characteristics are the result of studying the adaptive properties and strength of biological samples. AIS is defined by De Castro as follows: The adaptive systems that have been developed by inspiring from the theoretical immunology principles and safety models observed in the human body system and are used to solve problems [27].