Abstract
Currently, the use of internet-connected applications for storage by different organizations have rapidly increased with the vast need to store data, cybercrimes are also increasing and have affected large organizations and countries as a whole with highly sensitive information, countries like the United States of America, United Kingdom and Nigeria. Organizations generate a lot of information with the help of digitalization, these highly classified information are now stored in databases via the use of computer networks. Thus, allowing for attacks by cybercriminals and state-sponsored agents. Therefore, these organizations and countries spend more resources analyzing cybercrimes instead of preventing and detecting cybercrimes. The use of network forensics plays an important role in investigating cybercrimes; this is because most cybercrimes are committed via computer networks. This paper proposes a new approach to analyzing digital evidence in Nigeria using a proactive method of forensics with the help of deep learning algorithms - Convolutional Neural Networks (CNN) to proactively classify malicious packets from genuine packets and log them as they occur.
Highlights
Nowadays the increased usage of mobile devices and smart wares has dramatically increased the generation of data, usage of these devices has become our everyday lives
Base on (2020 Internet Crime Report, n.d.) the top 20 countries with most cybercrime victims index, Nigeria ranked 16th with 443 crimes recorded, while countries like the United Kingdom and India were top of the list with an average crime of 216,633 crimes recorded in the UK and 5399 in Canada
Nigeria has a large population of over 150 million people and of that 104.4 million people use the internet, with the large amount of people on the internet, there is an opportunity of cyber criminals to take advantage of these amounts.(Digital in Nigeria: All the Statistics You Need in 2021 — DataReportal – Global Digital Insights, n.d.) Currently, cybercrime is committed by people ranging from as young as 10 years to as old as 60 years
Summary
Nowadays the increased usage of mobile devices and smart wares has dramatically increased the generation of data, usage of these devices has become our everyday lives These smart devices hold valuable information that includes; bank records, private and commercial data which are transferred via computer (wired and wireless) networks and these networks are vulnerable to attacks such as ‘man in the middle attacks and spoofing attacks’ of which form a major part of criminal activities (Chowdhury et al, 2016). As reported by Alharbi et al, in 2011, proactive network forensics method reduces the resources, time and cost of the investigation by identifying potential evidence as the crime is committed. This is used in preliminary analysis of cybercrime and it helps to speed up decision-making processes by the organization. According to the proactive network forensic concepts, as mentioned by the first five stages work proactively for the reason that they work during the time the crime is ongoing, the other four stages in the model work after the investigative phase and work as reactive processes (Alharbi et al, 2011)
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