Scanning petabytes of information to detect potential cybersecurity threats cannot be a man’s task anymore. It has been quite some time since machines started to master the task. Gartner predicts that by 2025, Machine Learning will be part of every other security solution. On the other hand, around 5.99 billion malware attacks were reported in the first half of 2018 and the events that need to be handled are doubling.
As per a study by Paradoxes, Inc. for Oracle only 33 % of C-Suite executives and around 20% of lawmakers have adopted AI and ML. However, all of them believe that ML is vital in warding off cybersecurity threats.
Read more:4 Tips for Scaling Your Event Programs in the GDPR Era
How Vital Is ML for Big Companies?
Google processes an enormous number of spam emails and blocks 99% of them using Machine Learning. Apple makes use of Machine Learning to ensure security to its platform. IBM’s ‘Watson’ serves as the wall of Troy that stops malware attack on installations like banks and financial institutions. Watson, for instance, helped to thwart around 200 million cyber-attack events that targeted Wimbledon 2017. ABI Research tells that Machine Learning in cybersecurity will boost spending on Artificial Intelligence to $96 billion by 2021. The technology giants are investing big and acquiring companies in the safety and security of their clients.
Several companies specialize in building ML-based security solutions. Barracuda Network, Inc. for instance use Machine Learning to study their customer’s business, analyze communication pattern and zero in threats and malicious emails. Paladion, make use of Big Data analytics and AI to analyze attacks from proxy servers and emails.
Pattern Recognition Is the Key
Recognizing a pattern and acting on cyberattacks is what ML largely does. AI algorithms absorb their company’s activity logs of weeks and months to become competent in detecting anomalies. They set a baseline for normal performance using the data. Any event like hacking is judged based on the digression from the normal baseline.
The Instances of Attacks That Machines Thwart Include:
Spear Phishing Attack
The ML algorithm identify the received e-mail, studies its headers, body, punctuations and compare it for similarities with the pre-existing bank of malicious e-mails to detect threat. The ML is trained to identify links in an e-mail that expose users to risks. It can also identify spoof emails sent by impersonators.
Watering Hole Attack
In a watering hole attack, the hackers infest websites and gain access to the record of users and their credentials. Machine Learning algorithms can take care of websites security by analyzing the directory traversals. It can identify whether users are being taken away into malicious websites while traversing through the destination path. An unusual pattern of re-direction into the customer’s website also can be identified.
Webshell Attack
Webshells are codes maliciously loaded into a website to make alterations on the webroot directory. Machine Learning can be trained to differentiate normal behavior from a malicious one. The ML isolates the codes from the system before they can stage an attack and neutralize it.
Ransomware Attack
Ransomware basically locks up the files of a website or an organization and lets them unlock it only once a ransom is paid. Neural Networks and Deep Learning algorithms can trace micro behavior and nip ransomware as soon as they start affecting files. A security solutions provider named Darktrace, for instance, thwarted several attempts by WannaCry ransomware to infiltrate into their customers’ systems.
Cloud Integration Makes ML a Necessity
Instead of investing in hardware to save data, most organization big and small now depend on cloud-based platforms like Microsoft Azure for storage. This essentially means larger chunks of critical data is centralized. AI and Machine Learning algorithms have become important to ensure that malware does not ruin the systems. Machine Learning can analyze suspicious cloud app login activities, detect location-based anomalies and conducting IP reputation analysis to identify threats and risks in cloud apps and platforms. Apart from security ML helps in sorting and defragmenting data in cloud-based systems.
