IOT SECURITY THREATS & RISK MITIGATION

Running Head: IOT SECURITY THREATS & RISK MITIGATION 1

IOT SECURITY THREATS & RISK MITIGATION 1

Introduction: IOT SECURITY THREATS & RISK MITIGATION

Cities around the world are progressively becoming smart not surprisingly, It’s been IoT (Internet of Things) to IoE (Internet of Every Thing) era, Sapless security may affect the lives of millions of users privacy, Security and Trust. 2015 has also been the year of international cyber treaties to help impede attacks (Ali & Awad, 2018). Security threats are everywhere the typical Insider threats or Zero-day attack lasts an average of eight months or years without knowing it. That unleash attacks adequate time to steal valuable assets (Atlam & Wills, 2020). Due to number and types of vulnerabilities continuing to grow exponentially with the propagation of emergence of IoT(Internet of Things), Bring Your Own Device (BYOD).Intrusion detection system (IDS), Anti-virus(AV) and intelligence feeds generate so much data technologies to collect, analyze, and report data network architecture is only half the battle, implements controls. Today’s IoT related threats need a detailed incident response strategy when it matters to follow when you become breached (Aufner, 2020). The remaining of the paper is organized as follows. In literature survey discuss ENISA top threat Landscape trends. This proposed method and deals with the experiment setup and observations results (Ashraf & Habaebi, 2015).

Research Problem:

A physical honeypot is a genuine host machine on the system with its own particular IP are often high-interaction, so allowing the sensors to be fully compromised (Brass & Sowell, 2020), The estimation of a honeypot is controlled by the data that we can get from it, They are expensive to install and maintain for large address spaces (Mawgoud et al, 2020) it is impractical to deploy a physical honeypot for each IP address on each IoT devices such as single board computers.In that case, Deploy virtual honeypots to detect malicious behavior, NIDS (Network Intrusion Detection System) require signatures of known attacks and often fail to detect compromises that were unknown at the time it was deployed. On the other hand, honeypots can detect vulnerabilities that are not yet understood (Kuusijärvi et al., 2016). Consequently, forensic analysis of data collected from honey pots is less likely to lead to false positives than data collected by NIDS bringing honeypots back an awesome thought tempered by over decade of sublime misapplication resulting in a slow relegation to the realm of academia and slightly dubious research, But it doesn’t have to be that way because a honeypot has true production value.

Research Question:

This topic of dissertation will identify the following questions :

1. What are top emerging threats to connect with smart environment devices?

2.What are the different types of IOT risks are associated?

Research Methodology:

Modular and decentralized open source honeypot attempt to contact suspicious to analyze various attacks regarding the honeypot as an internal distributed sensor rather than a standalone alert generator (Varga et al., 2017). Each event reported is a high-quality indicator of investigation-worthy activity and each open canary instance feeds event data to a correlators which produces single alerts even in the face of network-wide scans. With such a high signal-to-noise ratio, every alert requires investigation. This is in contrast to the stream of alerts produced by tools such as anti-virus, network IDS or traditional honeypots. Implementing hashing mechanism to prevent malware

targeted to exploit the original application source code on production nodes for the proactive protection the honey token system are deployed tokens can be activated in a verity of ways, including on file I/O's, Database queries, Cloned websites, Process executions and changes in order to detect unauthorized attempts to use information breaches happens organizations to governments smart computing environment including smart city solutions requires painless way to help defenders discover they've been breached (Wheelus & Zhu, 2020).

References

Ali, B., & Awad, A. I. (2018). Cyber and physical security vulnerability assessment for IoT-based smart homes. sensors, 18(3), 817.

Atlam, H. F., & Wills, G. B. (2020). IoT security, privacy, safety and ethics. In Digital twin technologies and smart cities (pp. 123-149). Springer, Cham.

Aufner, P. (2020). The IoT security gap: a look down into the valley between threat models and their implementation. International Journal of Information Security, 19(1), 3-14.

Ashraf, Q. M., & Habaebi, M. H. (2015). Autonomic schemes for threat mitigation in Internet of Things. Journal of Network and Computer Applications, 49, 112-127.

Brass, I., & Sowell, J. H. (2020). Adaptive governance for the Internet of Things: Coping with emerging security risks. Regulation & Governance.

Mawgoud, A. A., Taha, M. H. N., & Khalifa, N. E. M. (2020). Security threats of social internet of things in the higher education environment. In Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications (pp. 151-171). Springer, Cham.

Kuusijärvi, J., Savola, R., Savolainen, P., & Evesti, A. (2016, December). Mitigating IoT security threats with a trusted Network element. In 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST) (pp. 260-265). IEEE.

Varga, P., Plosz, S., Soos, G., & Hegedus, C. (2017, May). Security threats and issues in automation IoT. In 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS) (pp. 1-6). IEEE.

Wheelus, C., & Zhu, X. (2020). Iot network security: Threats, risks, and a data-driven defense framework. IoT, 1(2), 259-285.