Machine learning, a subset of AI, has numerous use-cases, and one of them is its use in data protection, the key being its application in fighting ransomware. Although attackers who encrypt data promise to release it as soon as they are paid, there is never a guarantee that they will stick to their word. For businesses who fall victim to these attacks, this only means one thing. They will have to lose money and data to the hackers if they fail to release the files. The losses can result in lawsuits, and of course, the money paid for people that will investigate and recover critical data. There is also the problem of reputational damage and lost business, both of which far outweigh the money paid to the attackers.
Although most of these attacks are not necessarily sophisticated or dangerous, they are more aggravating and often take advantage of the weaknesses in the systems to sneak into computer systems. In most cases, they take advantage of the lack of knowledge among the employees through social engineering and spoofing. Although there are professionals and software used to scan malware, most of them are not good enough to identify new malware. Similarly, OS and software updates need to be automated to patch the vulnerabilities.
Researchers have turned to machine learning to help counter various challenges in fighting malware and ransomware attacks. This technology can learn bad and good software through past data and determine potential ransomware and which one is not. Machine learning can update itself through new data that the developers present. This new knowledge allows the machine learning system to accurately calculate the probability of a specific software being malware or not. Those that are above a specified threshold are rejected, while those below it are allowed.
Machine learning collects and analyzes large amounts of data while at the same time capturing, processing, and classifying every action of legitimate programs and users. Although behaviors keep changing, behavior modeling is one of the characteristics of AI algorithms used in fighting malware infections. Unlike humans, machine learning never tires and can analyze large datasets and generate new baseline based on models and normal behavior. Machine learning has the power to constantly see the changes and account for everything as soon as it detects a new behavior in users and programs.
With the ransomware attacks expected to evolve into more dangerous and highly sophisticated attacks, malware detection tools will also fit into the challenge. AI will play a prominent and crucial role in detecting potential attacks and removing them before losses are incurred. Although AI has its downsides, including false positives in its good-behavior models, they still promise a fighting chance. While many people cannot imagine a ransomware detection strategy without human resources being involved, recent AI advances keep showing us that it is possible to use AI and reduce overreliance on humans in fighting malware.
