Many business sectors are adopting machine learning to improve existing methodologies. Can you use ML algorithms to improve your IT security?
Artificial Intelligence has advanced at an astonishing rate in recent years. In fact, it has evolved so quickly that it has now become part of most people's everyday lives. You can thank machine learning (ML) for that. Although it is not the only subset of AI, ML is certainly the most popular, mainly because it allows companies to analyze information and use it to improve their processes and products.
Computers use machine learning to identify patterns and perform tasks without human intervention. This means that once you train an algorithm, it can use mathematical modeling and statistics to create personalized predictions without your input.
Machine learning has 2 approaches called supervised learning and unsupervised learning. In supervised learning, you label the data before inputting it into the algorithm, while in unsupervised learning, the algorithm makes its own connections without labels from the user.
Many companies use machine learning to implement new processes and improve existing ones. But one of the most important applications of machine learning is in cybersecurity. Organizations are now adopting custom designs such as Darktrace Antigua and Blue Hexagon to improve their cybersecurity. The use of machine learning algorithms in cybersecurity is also called cybersecurity data science.
A Forbes report said the average amount of ransomware transactions reported during 2021 was $102.3 million per month . What's more, a company falls victim to a cybersecurity attack every 14 S seconds . This is the reason why more and more companies are investing in ML to boost their cybersecurity efforts.
Using ML algorithms can help companies identify potential problems in their systems and develop strategies against them. They can also be used to perform repetitive security tasks efficiently and without errors.
So how can you use ML for cybersecurity?
Some of the popular ML use cases include:
1. Data processing and analysis
Cybersecurity systems generate a lot of data. These logs can generate a wide range of crucial insights for a company. However, analyzing large amounts of data on a daily basis is difficult and time-consuming.
Machine learning algorithms can easily comb through large data sets and report relevant insights. Companies can use these insights to identify threats, analyze usage patterns, and understand user behavior.
2. Mobile Security
With most working professionals connecting from home these days, the need for mobile security is at an all-time high. Most home networks are insecure and therefore can be easily hacked. Furthermore, many companies do not include mobile security in their cybersecurity strategy. This has caused an increase in cybersecurity attacks related to mobile devices .
Companies are now using ML to filter spam emails and combat this problem. ML algorithms can identify and report phishing/malware emails that pass filters. They can also monitor SMS messages and other forms of communication. Once they identify a source of malware, they can automatically mark the sender's contact information as unwanted and block future communications from that address.
3. Virtual assistant
Since most devices today have virtual assistants with access to storage, it is important to protect them from malicious actors. To avoid this, you can train ML algorithms to recognize normal user behavior and flag peculiar events when using voice assistants.
Since most virtual assistants like Siri and Alexa work with voice commands, they also need to be protected against voice attacks. Nowadays, AI assistants have intelligent voice detection modules that can recognize the voice of device owners. They also have anomaly detection programs to identify strange or erratic activity.
4. Antivirus and scanning
As new viruses and malware are created every day, even systems with antivirus software are susceptible to cyberattacks. This is why many companies use ML algorithms to improve their antivirus solutions. ML algorithms can perform breach detection and penetration testing to ensure stability. Additionally, they can identify trouble spots and remotely update antivirus software with new schemes.
ML algorithms can examine systems and network activities to detect problems before they turn into disasters. Companies can use them to identify suspicious domains and verify transactions during online banking. This is more economical than using human operators.
Problems when implementing ML algorithms in cybersecurity
While there are many advantages to using ML algorithms for cybersecurity, there are some challenges that prevent companies from implementing them into their systems. You need a lot of data/information to generate any type of threat profile. This includes network traffic, data endpoint information, application logs, and details about cloud environments/containers your system is running. Most companies don't have the necessary data to begin with.
Even if the company can figure out a way to collect data, there are other variables. Is the data stationary? How often are logs updated? How many data sources are there? Can you take data from multiple sources and use it effectively to create a single dataset? You need to process your data to apply any ML or decision-making algorithm on it. Many companies do not have a data collection, filtering and processing pipeline.
Additionally, you need to check the viability of the final dataset. After applying ML algorithms, is the data itself rich enough to generate any usable business insights? Often, the data is not robust enough to produce any actionable information.
What should I know from a business owner's perspective?
The first thing you need to do is decide the extent of AI/ML implementation in your system. How much automation can your system leverage?
To plan any machine learning activity, you also need to keep track of your data. This is done through dashboards and visualization software. They can also be used to monitor data endpoints for user activity.
Lastly, computers cannot do everything themselves. Although the algorithms are very sophisticated, they still need a human touch. A good cybersecurity strategy combines human and ML elements working in sync to deliver the best result. Therefore, you need human analysts along with ML algorithms for data-driven decision making.
Source: BairesDev
