Smart Cities Data Indicator-Based Cyber Threats Detection Using Bio-Inspired Artificial Algae Algorithm

Smart Cities Data Indicator-Based Cyber Threats Detection Using Bio-Inspired Artificial Algae Algorithm

Vineeta S. Chauhan, Jaydeep Chakravorty, Alex Khang
DOI: 10.4018/978-1-6684-8851-5.ch024
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Abstract

Smart cities have benefited greatly from the quick development of information technology, such as cloud computing, sensors, and the IoT. Smart cities improve living services and analyze massive amounts of data, which increases privacy and security concerns. But managing security and privacy issues is crucial for a smart city that encourages businesses to adopt new computing paradigms. In recent years, there has been a proliferation of literature on security and privacy, covering topics like end-to-end security, reliable data acquisition, transmission, and processing, legal service provisioning, and privacy of personal data, as well as the application of bio inspired computing techniques to system design and operation. Effective computing systems have been developed by utilizing bio-inspired computing approaches for intelligent decision support. The indicator-based threat detection system with BAAA algorithm is the quickest and most efficient way to scope an environment after observation, and its usefulness is greatly influenced by the adversary rate of change.
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Introduction

One of the most significant challenges for smart cities is cyber threats. Giving the everchanging risk landscape, emerging in smart cities could be targeted for a variety of adversary interests. The possibility of a malicious attempt to damage or disrupt a computer network or system is referred to as a cyber threat. This definition is incomplete without mentioning the attempt to access files and infiltrate or steal data and can be solved with proposed method of Indicator Based cyber– threats detection for data of smart Cities using Bio-Inspired Artificial Algae Algorithm. However, the threat is more closely associated with the adverse attempt to gain access to a system in the cyber security community. The combination of data and corresponding advances is producing urban conditions that are very different from anything we have seen before. Cities are becoming more intelligent not just in terms of how to automate routine capacities serving specific people, structures, and traffic frameworks, but also in ways that enable us to screen, comprehend, examine, and plan the city to continuously improve the efficiency, value and personal satisfaction for its residents. This algorithm is based on the behavior of algae, which exhibit characteristics such as self-organization, adaptation, and resilience. These same characteristics are valuable in detecting and mitigating cyber threats in smart city systems.

A new understanding of urban problems is that smart cities are intricate frameworks that are greater than the sum of their parts and are created through many individual and aggregate choices from the bottom up (Trevor et al., 2018). The multifaceted nature sciences are required for their comprehension, which is a moving goal in that urban communities themselves and are becoming increasingly perplexing as a result of the very advancements for utilizing them. Figure 1 depicts the issues associated with the development of smart cities (Darwish, 2018; David & Wanger, 2017).

Figure 1.

Problems related to smart cities development

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Problem Statement

Detecting cyber threats in smart cities can be a challenging task, given the vast amount of data generated by various sensors and devices. One way to address this issue is by using bio-inspired algorithms, which mimic the behavior of natural organisms to solve complex problems. Smart city technologies are no exception, as they are overwhelmed by a variety of security vulnerabilities and risks, and an ongoing battle has emerged between the cyber security industry and criminals and variously motivated hackers. While the fundamental motivations for breaking into these systems may remain constant (e.g., theft, extortion, impersonation, destruction, malicious disruption), the nature of their performance has changed. List given below depicts the classification of cyber threats (Creery & Byres, 2005).

  • 2 Social engineered

  • 2 Trojans

  • 2 Unpatched software

  • 2 Phishing

  • 2 Network traveling worms

  • 2 Advanced persistent threats

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Overview On Security

Threats in our world of increasing interdependence cannot be handled by the human and cyber security measures now in use. Global terror networks are changing the challenges to human security. Due to its quick and covert procedures, cyber security is probably an even bigger concern that is inadequately addressed by current solutions. The need to address contemporary issues with human and cyber security is driving the creation of fundamentally novel methodologies. The scattered nature of new difficulties is a crucial aspect. The issue of maintaining security at any area is made more difficult by the ability of distributed groups of people to wreak significant physical or informational damage, thanks to global transportation and communication technologies. On the one hand, conventional police agencies with local jurisdiction alone are unable to respond to links and associations across borders.

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