Artificial Intelligence (AI) has advanced exponentially over the last few years. It's rapidly becoming a part of our day-to-day lives and transforming how humans interact with technology. At its core is Machine Learning (ML), a subset of AI that allows companies to identify patterns and perform tasks without human intervention.
Even though AI has many subsets, ML is among the most popular. It works on the idea that a machine can learn and create patterns from training data. It can then use these patterns to make inferences on a new dataset (testing data). Through ML, organizations can parse and analyze information, implement new processes and improve existing ones.
There are two popular machine learning approaches, supervised learning (in which the user labels the data before feeding it into the algorithm) and unsupervised learning (the algorithm makes its connections without user labels).
ML has a very interesting use case in cybersecurity. The scientific approach of improving a company's existing cybersecurity through machine learning methods is called cybersecurity data science. Many organizations use custom ML security solutions such as BlueHexagon and Falcon to improve their infrastructure security. However, you can also consult with a cybersecurity vendor such as Jalasoft, an award winning technology company with 20+ years if experience.
Since 2016, hackers have stolen $43 billion by compromising business emails. Needless to say, there is a dire need to implement advanced cybersecurity systems to combat security issues.
ML algorithms can help boost cybersecurity efforts by uncovering potential threats and dealing with them. You can also use ML to perform repetitive tasks efficiently and generate behavioral insights for decision-making.
Machine Learning (ML) for Cybersecurity: Popular Use Cases
1. Data handling and processing
Since cybersecurity systems generate a lot of data, you need someone to comb through the logs to generate actionable information. However, going through such large amounts of data daily is time-consuming and tedious.
Engineers can use ML algorithms to automate this process. ML algorithms can easily comb through such large datasets and generate business insights. They can also analyze patterns to create threat profiles, which can be monitored via anomaly detection software.
2. Cybersecurity for Mobile
Since many IT professionals have been working from home, the need to secure mobile connections is at an all-time high. Unfortunately, most home networks have weak security and can be easily hacked. This can cause various security issues, such as data loss and denial of service.
Many prominent organizations, such as Google and Amazon, are now using ML algorithms to analyze threats for mobile endpoints. They are also implementing advanced antivirus software solutions that can use ML algorithms to identify and report phishing scams. You can also use products such as Zimperium and Wandera to boost your mobile security.
3. Use of virtual assistants
Virtual assistants are no longer a novelty. Most of the devices we use today have some form of conversational Artificial intelligence (AI) on them. Unfortunately, even though voice assistants make things simpler, they also pose a huge security threat to its user.
Most of these products have real-time access to device storage, making them a target for malicious hackers. They can also use them to access your accounts, passwords, and other private information. Even if the assistant itself is secure, the devices connected might not be. Hence it's very important to safeguard these virtual assistants against hostile actions.
In such cases, ML algorithms can be used to recognize normal user behavior and flag any unusual activity. You can also integrate them with anomaly detection programs, which can look for specific events. Once this software identifies an anomaly, it can take automated actions against it by blacklisting the user and blocking further communication from that server address.
Since voice assistants work on voice commands, they must also be safeguarded against voice and eavesdropping attacks. These days, many virtual assistants have voice recognition modules that can identify and differentiate the voice of their owners. If they discover any strange or erratic activity, they can send a log report to the developer.
4. Antivirus and scanning
As per this report, 35000 new malware are created every day. Hence many organizations are looking for a solution to curb this problem and get ahead of its curve. One such solution is the use of ML algorithms.
ML algorithms can be used to improve existing antivirus software by regularly updating them with new schematics. These algorithms can also perform real-time system scans, breach detections, and penetration testing to ensure system stability and identify pain points. Furthermore, ML solutions are more cost-effective and quicker than using human operators.
So what's the problem?
Even though there are so many pros of using ML algorithms in cybersecurity, IT companies face a few challenges while implementing them. First, since ML algorithms work on intrinsic connections between data points to generate patterns, they need a lot of data points to train.
Such data may include system logs, download records, network traffic information, mobile data endpoint connection, and cloud/container details. Many companies don't have the required data, to begin with.
Along with ML implementation, organizations also need to create data processing pipelines that collect and process this data on a regular basis. However, such setups can be prohibitively expensive, hindering many organizations from implementing them.
Even if your organization can figure out a way to collect such a huge dataset, there are other variables to keep in mind, such as:
Is the incoming data stationary or nonstationary?
How big of a dataset is used for the algorithm? How many data sources are used?
Is the dataset rich enough to be used to generate insights?
Conclusion
The IT Landscape is rapidly changing, bringing in new threats and issues every day. It's very hard to combat these changes with a traditional approach. Even though there are a few challenges, using ML with cybersecurity is the fastest way to bridge this gap and match the pace of the ongoing change.
Secondly, even though ML algorithms are very effective, they cannot do everything themselves. Therefore, a good cybersecurity strategy should have both human and ML elements working together to deliver the best results. That's the way to go for cybersecurity data science.
