The world has witnessed some serious data breach issues and the cybersecurity has been in question multiple times during the last few years. As per a report by Cisco, it’s harder for the professionals to detect cyber threats due to the huge volume of malicious as well as legitimate encrypted web traffic. Therefore, machine learning is now being proposed to be used for cybersecurity.
Some people think that machine learning is similar to artificial intelligence that is able to understand language and solve problems. However, it’s not quite true. Rather, machine learning is a part of artificial intelligence. It’s a process through which an algorithm is trained to learn and predict depending on the statistics input.
Monitoring doubtful traffic and responding to those:
Machine learning can help in monitoring the traffic of the network and learning the regulations of the system. Thus, a model of well-trained machine learning will be capable of spotting legitimate traffic and prevent the malicious traffic. As of now, it needs a human intervention after an anomaly is detected. However, it is able for some of the machine learning algorithms to take preventive action by themselves.
Automation of repetitive tasks:
Machine learning can really prove to be helpful in automating some of the repetitive tasks. An analyst has to handle multiple responsibilities when a security breach is detected. The analyst has to determine how the breach has happened, what has been stolen and most of all fix the network to thwart further damage. Machine learning can automatically detect the faults and thus, the fixing time is reduced significantly.
Supporting human analysts:
As per a report, machine learning shows a better result in detecting possible cyber attacks with the help of using the information gathered by human analysts. It has been seen that 85% of the threats can be prevented, which is three times improved than the earlier standard. The collaboration of human input and machine learning has also reduced the instances of false alarms regarding possible threats.
Prevention of zero-day exploitations:
Sometimes the cybercriminals use the vulnerability of some software for cyber breaching. The developers can’t stop that from happening as the patches are yet to be released. Though machine learning may not be able to get rid of such issues, it can detect such possibilities and prevent the exploitation.
Though machine learning offers the chance of increased cybersecurity, it must be remembered that the cybercriminals are also in the race of outsmarting machine learning. There’re questions regarding the ability of the technology in detecting new threats based on the data that are already used before.