The application of deep learning technology is evident in the areas like marketing, sales, healthcare, and finance. The field where we hardly hear about its implementation is cybersecurity. Tech giants like Google, Facebook, Microsoft are already leveraging the benefits of AI and machine learning into their system while others are still trying to catch up. Cybersecurity practitioners are often made to believe that Machine learning and artificial intelligence is a magic wand that can solve all their cybersecurity-related intricacies. But, the truth is that AI and machine learning are just like tools that need to be used correctly to reveal its accurate insights.
AI for cybersecurity:
Regarding cybersecurity- we want to implement machine learning to find anomalies in a system. Now, to detect the anomalies, you need to determine the different scenarios that define what is normal and what is not. For example, if you have to find abnormal behavior for your laptop, then you need to list all the exceptional scenarios that might happen when you are using your system. It will include operations like downloading software or a game or entering a new Wi-Fi network.
Now based on these scenarios you will have to create your data sets. For a successful machine learning and cybersecurity integration, one needs to have extensively labeled data sets that will help to detect malware or corrupted files.
The implementation of AI in cybersecurity is for the following purposes:
• Anomaly detection: The datasets are collected and analyzed based on pre-defined rules and checked for any anomalies.
• Natural language processing: Convert an unstructured page into structured intelligence.
• Predictive analysis: Processing data to determine the patterns for predictive analysis and outliners.
When you dig deep into the machine learning concepts for cybersecurity then two concepts come into picture one is the supervised machine learning and the other is unsupervised machine learning.
Supervised Machine learning: It is said to be the most prominent field of machine learning that has made a significant impact on the cybersecurity sector. It primarily involves identification of malware, spam detection and evaluation of the existing files based on pre-defined samples and rules. This approach has dramatically benefited from deep learning because there is an ample amount of data available that help to label the samples whether they are malware or not. The same applies to spam detection as there is a lot of training data to teach the system about what is right and what is wrong.
Unsupervised machine learning: It is that field of learning where one needs to identify the hidden structures in a data set. The likable data or the data that is similar in some sense are put together in a subset called as clustering. But, there are a lot of technical implications that come with this methodology like explainability of the clusters and choosing the right distance function when implementing it for security purposes.
Context and Knowledge: The details mentioned above are the tools that when implemented rightly help to leverage the benefits of AI and machine learning for cybersecurity. But, to understand the data sets and the role of each entity, it is essential to know the context of the datasets, such as the devices, applications, and users. Context is added to the datasets to make sense of the available data. For example, if you know that your device needs to respond to a particular network at specific time intervals, then it is normal behavior but in case if the response is erratic, then it may be a result of an attack.
The next addition to context is to build a system with expert knowledge. It doesn’t mean that the system should be 100% effective and yield the results as expected. Expert knowledge systems can assist the analysts to be more effective and productive. Data visualization is an excellent example in this area. Instead of checking enormous rows of data they can check the visual representations of the data in the shortest possible time frame.
Machine learning and artificial intelligence is a tool that needs experts and experience to reveal the actual security insights. The development of a full-proof security system involves deep-learning and expert knowledge to determine any deviations.