Well-Known Accidental Inventions

 Well-Known Accidental Inventions

The Internet of Things (IoT) and Cybersecurity domains have documented cases where errors or accidents have resulted in remarkable advancements. For instance, enhanced security measures have been instituted not only after the disappearance of the notorious WannaCry ransomware but also after the Mirai botnet episode. In both cases, it is clear that the cracks that appeared in the walls of security of IoT systems were the reasons for new turning concepts and methods to be developed, which would make the security of IoT systems even higher in the future.

The May 2017 WannaCry ransomware onslaught epitomizes self-inflicted blunders, wherein enormous advances of loss are achieved in establishing security bona fide. This assault took advantage of a weakness in the Microsoft Windows operating system, affecting hundreds of thousands of computers in about 150 countries, including critical entities such as hospitals and transport systems. The attack's magnitude underscored the weaknesses in IoT devices and networks and prompted a global review of cybersecurity provisions. As an aftermath, researchers' and agencies' efforts turned to devising better security features such as improved patch management and setting up advanced threat identification, detection, and response systems (Ugwuanyi & Irvine, 2020). The feature introduced by the assault created a sense of urgency and commotion in research that sought to identify and address gaps in IoT networks, which were hitherto neglected before the incident (Sáez-de-Cámara et al., 2023).

The WannaCry incident propelled the application of a federated learning approach in Cybersecurity. In other words, the training of models is performed decentralized to ensure that the data cannot be distributed in a central repository but at the local end devices, improving security and privacy. This strategy developed due to the demand for enhanced information protection systems in the IoT (Sáez-de-Cámara et al., 2023). The conclusions drawn from the observation of the WannaCry event brought out the requirement for an integrated approach in cyberspace, which led to the development of principles that enable devices to be cross-learned while maintaining data protection.

Likewise, another significant development in IoT security dates back to 2016, when the Mirai botnet attack occurred. The Mirai botnet enabled one of the most important distributed denial-of-service attacks with targeted IoT devices connected to the Internet. This incident revealed the glaring security vulnerabilities of IoT equipment, some of which, as seen, were default password enabled and had weak security settings (Tervonen et al., 2018) then emerged the need to reinforce regulations concerning the safety of IoT devices through for instance instituting the use of strong password policies and requirements for firmware updates (Giannaros, 2023).

The Mirai incident also catalyzed the emergence of novel approaches for intrusion detection systems. This particular breach pushed researchers to start researching the use of machine learning techniques to spot "unusual" behavior in IoT networks, which could lead to security breaches. This evolution toward proactive and preventive measures has been accompanied by the emergence of practical algorithms that can identify anomalies instantaneously and thus improve the immunity of IoT systems against similar threats in the future. Such inventions were required because classical security approaches could not be enough for such dynamic cyberspace scenarios.

Moreover, the incidents of WannaCry and Mirai propelled the broad discourse surrounding the need for cybersecurity training and education. Metrics that indicated human involvement in the breach of security measures in these occurrences resulted in steps being taken to enhance users' and developers' security education. Emphasis on the best practice methodologies in coding, vulnerability analysis, and incident management has increased, promoting the security culture in the IoT sector (Küçük et al., 2019). This focus on education is significant because it allows individuals and institutions to mobilize themselves to protect their IoT networks and devices.

The forces supporting these innovations derive from the amalgamation of regulatory tactics, market forces, and changing trends in cyber-attacks. Governments and other regulators have started to force IoT manufacturers to comply with stricter security standards on their products (Tervonen et al., 2018). Likewise, the emergence of the market of IoT devices led to stiff rivalry where companies had to use improved security systems. This has resulted in a shift of focus towards research and development to ensure IoT devices can be secure from design to meet the demands of emerging risks (Marheine, 2020).

In conclusion, there is a positive outcome and development of IoT and cyberspace security despite the obvious errors, such as the WannaCry ransomware and Mirai botnet' attack. This includes how a problem can be fully fixed or revised in practices, creating technological advancements, and transforming. However, as the IoT develops, the drastic nature of the threats posed by vulnerabilities and attack vectors will grow exponentially, so it is critical that the key actors within the IoT ecosystem can learn from these experiences and act preemptively.

References

Giannaros, A. (2023). Autonomous vehicles: Sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions. Journal of Cybersecurity and Privacy, 3(3), 493–543. https://doi.org/10.3390/jcp3030025

Küçük, K., Bayılmış, C., & Msongaleli, D. (2019). Designing real-time IoT system course: Prototyping with cloud platforms, laboratory experiments and term project. International Journal of Electrical Engineering Education, 58(3), 743–772. https://doi.org/10.1177/0020720919862496

Marheine, C. (2020). Governance strategies to drive complementary innovation in IoT platforms: A multiple case study. 725–740. https://doi.org/10.30844/wi_2020_g3-marheine

Sáez-de-Cámara, X., Flores, J., Arellano, C., Urbieta, A., & Zurutuza, U. (2023). Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks. https://doi.org/10.48550/arxiv.2303.15986

Tervonen, J., Hautamäki, J., Heikkilä, M., & Isoherranen, V. (2018). Survey of business excellence by knowledge gathering for industrial internet-of-things applications. International Journal of Management and Enterprise Development, 17(4), 388. https://doi.org/10.1504/ijmed.2018.10017528

Ugwuanyi, S., & Irvine, J. (2020). Security analysis of IoT networks and platforms. https://doi.org/10.1109/isncc49221.2020.9297267