In this generation of rapid growth in both technologies and threats, cyber criminals intend to develop more robust threats like Malwares.
To prevent such threats, using traditional Signature-based Malware detection softwares will not be suitable to detect the new and evolving malwares.
To overcome this, instead of the Signature-based Malware detection approach, we should deploy an AI-leveraged approach which implements Deep Learning models such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) and Machine Learning Algorithms.
Traditionally, the detection of malwares like viruses, worms, trojans or any malicious programs are being performed by signature-based softwares.
If and only if the signature present in a malware/malicious program is present in the signature database of such softwares, the malwares/malicious program getting installed will be flagged as a threat.
In this period where we are facing an enormous growth in technology like GenerativeAI, cyber criminals are intending to misuse them to develop new/unseen threats called “Zero-day Attacks or Zero-day Exploits”.
As these types of threats are newly developed, the signatures present in such attacks/malwares might not be present in the signature database of the signature-based malware detection softwares.
So when the signature of a new malware does not match any signatures present in the signature database, the software will assume that the program is legitimate and let it get installed by our system leading to the attack of the system or the internet.
This research is motivated to develop an AI-based solution which could also possess the ability to detect and classify robust and new/unseen threats.
As AI models such as Deep Learning architectures and Machine Learning models predict or detect the outcome based on the previous knowledge they gained through training, they can be deployed in the field of Malware Analytics.