Data science and machine learning continue to improve and advance. One of the areas where it is becoming more relevant is data security – AI in cybersecurity is expected to reach almost $35 billion by 2025.
Data scientists can apply their knowledge to the cybersecurity field to help protect against attacks and identify suspicious behavior. The fact that they play a versatile role of a technical expert, problem gatherer, analyst and a skilled interpreter, problem-solving is easiest for them. By using knowledge of data science, coders and programmers can also improve their techniques to create better programs to protect against cyber threats.
In addition, they get benefitted from these two things:
- Cybersecurity industry is always looking for technical resources but they need smart and sharp people who can help them solve problems in no time. So If you’re good at coding, you have a high chance of getting hired at sky-high package.
- Each year, billions are lost in data breaches. To be able to contribute to it is immensely satisfying. Isn’t it?
How data science can be used in cybersecurity programs
In cybersecurity, your goal is to identify threats, stop intrusions and attacks, properly identify malware and spam, and prevent fraud. Data science and machine learning can be used to help better identify these threats. For example, when it comes to identifying malware and spam, data from a wide range of samples can be used for deep learning and training purposes so that malware and spam are properly detected.
The goal here would be to properly identify and warn when malware and spam are detected while reducing false positives, which use up unnecessary time and energy. The same goes for identifying intrusions and attacks. When hackers want to attack a system, there will usually be smaller intrusions at first with the intent of figuring out how the system works, what its defenses are, and how they can be overcome. This is commonly the case with Ransomware, of which cases have increased by 37 percent last year.
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Data science can be used to properly identify anomalies and abnormalities in user behavior that may be caused by an intruder. Then, the proper preventative measures can be taken to stop the intrusion from getting more severe. There will often be a correlation of multiple abnormal events if an intrusion or attack is being carried out. Data science can help connect the dots between these “minor” abnormalities and use them to paint a bigger picture of what might be going on. For preventing fraud, the process is the same. Using samples from your data set, you would detect abnormalities in credit card purchases, for example, and use that information to identify fraudulent activity.
Data science and cybersecurity – Challenges to overcome
Data science can be used to overcome challenges in cybersecurity, but that comes with a set of challenges of its own that need to be overcome. Here are some of them.
1. Not relying on “lab-based” sequences
One of the main benefits of using data science for cybersecurity purposes is that larger samples of data can be used to better identify threats. For example, a common problem with cybersecurity programs is that they were essentially created in a test tube. In other words, they were built by using a preconceived sequence of events.
However, hackers rarely play by the “rules.” It is extremely important to assess all of the real data you have from real users when creating a program to identify threats so that proper normal behavior can be identified, which is essential if you want to identify abnormal behavior.
2. Having access to enough data
Identifying malware and spam is a lot easier than identifying behavioral abnormalities. There is a large sample of data available to use for training purposes to identify what is malware and spam and what is not. According to Kaspersky, over 360,000 new samples of malware are detected every day.
However, when it comes to behavioral abnormalities, there is a lot more going on. There is a lot more nuance involved. You need to assess all the real data that you possibly can in order to know what is normal and what is not instead of relying on preconceived rules.
As mentioned, data science can be used to assess all raw user behavior and connect the dots if multiple abnormalities are detected. By using large “data lakes,” you can compare real-time activity to the data in the lake to help identify threats. The challenge would be having access to all that data, which comes from many different logs and systems.
3. Focusing on the abnormalities that matter
Not every behavior that is slightly unusual is going to be relevant for cybersecurity purposes. Knowledge of why a behavior may have occurred is necessary in order to reduce false positives.
There are always going to be deviations from so-called normal activity; for example, many people may be traveling to a different country and logging in from there, using a different device to log in, or suddenly deciding to make a purchasing decision that deviates from their previous purchase history.
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A lot depends on the context as well, as the same type of behavior can mean different things depending on what is going on in the bigger picture. There can be a lot of extra noise that is not relevant, thus creating many false positives.
4. Using data science in an effective way
At an enterprise level, data science analyzes big data from the network to root out possible vulnerabilities. On the other hand, data security software, like VPN services, protects the network where the big data flows from. Thus, data science and data security have a symbiotic relationship. On a larger scale, data science can be used to identify trends and movements of malware over time so that impending threats can be anticipated.
Other ways data science can be used, for example, is by creating a baseline for each user and comparing it with real-time data. Another suggestion is that clustering can be used to create clusters of activities and behaviors which can be classified as abnormal. Data science can be used to reduce false positives and better streamline the alert process so that there isn’t an overload of alerts. If the responses to alerts are properly automated, alerts can be properly attended to in real time and the load on security teams can be lessened.
The fewer false positives, the better. False positives cost companies an average of $1.37 million a year. Once a program or model is created, it will have to be continually monitored to make sure that it is working as planned. If it is indeed working as planned, the results of the model must be monitored to make sure that they are satisfactory. When combining data science with cybersecurity, effort must be taken not to rush into things. You don’t want to potentially miss out on attacks and abnormalities due to a lack of proper training.
In addition, using more than one algorithm can provide better protection in case one algorithm is attacked or corrupted.
Wrapping it up
While there is still a long way to go, data science is the next hottest thing in the cybersecurity realm. By incorporating it into your programs, you can better detect threats and reduce false positives.
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Source : JAXenter
