## 1 Introduction  
Intrusion, detection, and prevention have always been three words that have resonated since the invention of the internet [1]. Many approaches towards these objectives have been used and trialed, and some have succeeded or failed, and others targeted, and broken. As technology advances, the need to secure data and protect them has changed. There is the continuous fear that an attacker trying to steal would always find a way around the secure lock, with or without a key. The integration of real-world objects with the internet brings cybersecurity threats to most of our daily activities [1]. Technology and methods of communications have advanced since the start of the internet and as users learn to manage systems, users will look for more ways in which they can become more powerful and learn to distribute and share their data safely. With more wired and wireless connections and devices on the market, the problem of safety increases. This study aims to look at how SMEs handle, store and protect their data using devices that are readily available in the market. With the current UK General Data Protection Regulation (GDPR) Act in operation, data storage has taken on a different dimension in its importance on how SMEs can keep safe. COVID-19 has also added to the mix of how vulnerable SME data can be due to the working environment changing from the security of the office to the vulnerability of the home network. The motivation for this study comes from various recent literature on how usage of machine learning could help make devices more robust in the way they are able to detect zero-day attacks and most important how much these devices cost. The research question explores the financial aspects of investing in devices that use machine learning technology in network devices to contribute towards the intelligence of detection, prevention, and protection of data. This is seen as useful for intrusions that are constantly hitting the SME networks, and takes a look at the vulnerability reflected in the current cyber pandemic alongside the real world COVID-19 status, and how much SMEs would need to invest in keeping their data safe and secure. In this study, the devices chosen by the SMEs were specific in that they were able to detect and prevent any intrusions coming into the network. Various devices were taken into consideration for this experiment, and finally, as pre-mentioned, three choices were made for which devices were to be observed in this experiment in both the live data centre of the SME and the working network of the home office environment. The analysis will include an examination of the variety of these devices and models, in what is referred to as Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), that leads to IDPS, a combination of both devices in one. This study will then include a demonstration of how different vendors have been able to cope with technology and break the minds of \"internet gang warfare\", that is, the world of hackers. The final implications of this experiment will reflect on the cost and answer the question of \"how much is this device, and will it protect my system?\". This study brings together the world scenario of the COVID-19 pandemic and its lasting effects on the birth of this cyber pandemic. It was during this study that the world became exposed to more prominent attacks currently leading the world such as Ransomware, Phishing, Malware, and social engineering amongst others. The realities of these threats have evolved into both technologies as well as wider policies towards managing threats. Protection of data as shown in this study is the reason why IDPS systems have come into force and are important within the SME market. Following on from this study, we can see a path in which where we could potentially bridge the gap between OpenSource code, such as SNORT and pfSense, and Commercial Network Intrusion Detection (NIDs) such as Cisco for further development.