The shortage of cybersecurity professionals brings a huge threat of cyberattacksto all organisations and their customers. This leads to a constant battlebetween cybercriminals and defenders. The current major cyber threats of 2020 include Emotet, Trickbot Trojan and Ryuk malware.
To curb this gap between criminals and defenders Artificial Intelligence (AI) and Machine Learning (ML) tools are playing a major role. These cybercriminals smartly tweak the code to escape being recognised as virus. The application of AI and ML in cybersecurity aims to detect new and unknown malware threats.
Machine Learning is the best tool to control malware attacks. Drawing information from its database about any previously detected virus, it can also find out its modified or new version and block the attack. It can even uncover malicious code mixed with some useless codes to escape the defender’s eye.
Using these ML techniques Cylance had detected and protected its users against APT32, a new malware code by the hacking team OceanLotus of Vietnam.
Deploying an AI-integrated network-monitoring tool with Machine Learning boostscybersecurity. AI tracks a user’s daily activity, thereby analysing their behaviour it can easily detect any abnormality and function accordingly.
This is what AI is expert at doing and how AI and ML are an emerging solution to cyber threats.
If a user in any network clicks on any suspicious link, the system predicts it to be an abnormal behaviour and potentially a malicious attack. With Machine Learning, this is easily spotted in seconds, blocking any abnormal actionand preventing the hacker from accessing the network.
Though very effective, still, these techniques aren’t a replacement for human power. In the end, it is a technology and there can be programming errors and chances of data being missed by the algorithm.
Along with multiple benefits, AI and ML can also lead to add-on problems. There is a possibility of cybercriminals also using the same tools in order to more effectively code the malware.
