Machine learning is often described as a method by which computers can learn without being programmed in a clear and exact way. Nowadays many software applications are using ML as a part of their functionality.
ML is used in a variety of applications from Cloud Computing to VR. One such application is Cybersecurity.
Rapid digitization of many industries has led to security concerns. Many important and critical data is being stored in the cloud. But this does not guarantee the safeguarding of these crucial data.
Hence, for this reason, many major tech companies have started incorporating Artificial Intelligence and Machine Learning in Cybersecurity. Along with reaping its benefits by broadening Cybersecurity’s horizon.
As Capgemini did the study in 2019, almost all industries are already using ML and AI for Cybersecurity.
There are already a couple of examples of ML used in Cybersecurity. Let’s understand how Machine Learning is applied in Cybersecurity with these use cases.
List of the Machine Learning Use Cases in Cybersecurity.
- Using ML against SMS Scams
- Using ML for Securing Mobile Endpoints
- Using ML for Enhancing Human Analysis and Safeguarding against Human Errors
- Using ML in Anti-Virus software and Malware Detection
- Using ML in Email Monitoring
- Using ML Against Bots
- Using ML in Network Threat Detection
- Using ML against AI-based Threat Mitigation
Due to the pandemic, more employees are working from home than ever before. To stay updated with the work and collaborate, employees and even college students are using text messages.
Whether it is SMS or internet-based texting application like WhatsApp or Telegram hackers under the pretense of the umbrella-term “COVID-19” are phishing and scamming people.
In this Machine learning use case, the MTD system(Mobile Threat Defense System) is used. In this, ML models are trained to segregate the hackers from genuine informational Covid-19 messages.
Like mobile, laptops, PC, etc., different endpoints are safeguarded. They are safeguarded by the Unified Endpoint Management program. UEM is highly effective for text-based applications and SMSs. Herein, the model is trained with many datasets to identify the threats amongst the authentic messages.
Machine learning is already abundant when it is concerned with mobile devices. Whether it is iOS or Android, data privacy, security patches, anti-virus applications already use ML.
Google is already using Machine Learning in security for mobile devices. ML is used to prevent cyber attacks in networks, devices, and vulnerability assessment tools themselves.
Wandera, a cybersecurity space leader, uses its ML algorithm. They detected 500 ransomware strains in the different companies’ business mobile devices.
Apple’s Siri, Google Assistant, and Amazon’s Alexa, are personal, AI-driven assistance. They have significant responsibilities of securing the voice-based commands using ML. Also, to identify the actual owner’s voice against a hacker’s control.
There is no doubt that Machine learning and AI are better than humans when identifying any loopholes or making any errors.
ML in Cybersecurity was introduced when data usage increased rapidly. For humans finding and analyzing any threats was considered as finding a needle in a haystack. MIT introduced a system called AI2. It is an adaptive machine learning security platform that helped analysts find those ‘needles in the haystack.’
This system could filter out all the malicious activities out of millions of actions taken during one day. AI2 brought down the threat rate by 85%.
Vulnerability assessment tools became common among analysts for detections of any attacks.
Latest anti-virus software use ML models that are repeatedly trained for any risks. They enhance from the baseline of behavioral actions. If something out of the ordinary occurs, then ML algorithms are programmed to flag this.
Machine learning-powered anti-virus software uses anomaly detection to track program behavior. Regular anti-virus software requires signature updates of the viruses.
But smart anti-virus systems do not need signed viruses and are enhanced with ML algorithms from scratch. Anti-virus software itself is a Machine Learning example in cybersecurity.
ML in Cybersecurity detects malware before malicious files are opened and even the types of malware. After analyzing millions of malware types, the newest and most powerful anti-virus software is created.
Many businesses have understood the importance of Cybersecurity in emailing. Machine learning-based vulnerability assessment & monitoring software can increase the speed in detecting cyber-attacks. And overtime developing detection accuracy.
Nowadays, the latest monitoring tools can detect any viruses/malware without the mail itself being opened. Also, to check for phishing efforts in emails, the patterns are matched with ordinary mails using the NLP algorithm.
Businesses can find whether the email, sender, or attachment is a phishing scam or attack using the anomaly detection software. Hence email monitoring is one of the use cases of ML in cybersecurity.
Today, bots make up 25% of all internet traffic, and that is a significant number. Most of the bots are malicious. Bots have the capability of assuming control of the whole account. They even can create fake accounts. All these activities are dangerous.
It is evident that humans can’t fight against already-automated bots alone. For that, machine learning examples in Cybersecurity are AI and ML itself.
A vast amount of data with behavioral patterns is required to distinguish ‘good bots’ from the ‘bad bots.’ Unnatural patterns, fast movement across the net, etc., are the factors of differentiation.
Network security is of utmost importance for any business. Understanding the various topology of the network security architecture is a challenge. Even for many cybersecurity specialists.
With the amount of data coming in and out of the network, it is no joking matter. Along with analyzing the data, maintaining the web, and identifying the connection behavior.
The enhanced ML-based network security system will track all outgoing and incoming calls/data. To detect any suspicious information patterns in the network.
Many software can monitor networks by using anomaly detection software. It is used to alert human authorities in case of discrepancies in data like previous cyber threats.
Along with Cybersecurity specialists, hackers too are evolving with AI and ML. Hence businesses must train ML algorithms to recognize attacks perpetrated by other ML or AI algorithms.
For example, hackers too can use ML to uncover weak-points in cybersecurity platforms and networks. Other hackers have developed smart viruses or even artificial hackers. To personalize attacks customized to victims’ specific contexts.
In the past few years, firms worldwide have been struck with ransomware and cyber attacks such as Notpetya and WannaCry. Both these are proven to have used high-level AI/ML in the development.
Conclusion
The above use cases are but a few of the many examples for ML in Cybersecurity. The tech industry is still experimenting across various use cases of ML in Cybersecurity.
While we still have a long way to go in the war against Cybersecurity, AI and ML are needed.
Using Machine learning to prevent cyber attacks is still new, yet there are many possibilities. Having ML models trained on millions of datasets in labs is one thing but using it in the teal-world is another. We can only hope for the best.
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