ML Cyber security
Cybersecurity is gaining more and more attention each year. The number of cyberattacks has significantly increased since in the last decade due to the digitization of everything in the modern world. Global cybercrime costs are expected to grow by 15 percent per year. They include damage and destruction of data, stolen money, lost productivity, theft of intellectual property, theft of personal and financial data, fraud, post-attack disruption to the normal course of business, restoration and deletion of hacked data and systems, and reputational harm.
Machine learning (ML) approaches are of special interest in this area. ML is concerned with intelligent behavior in a system, including perception, reasoning, learning, communication and acting in a complex environment. The widespread interest is due to two critical factors:
- it can automate processes
- It can quickly process and analyze huge amounts of information and calculate options using many variables.
The scope of AI usage in cybersecurity is huge, starting with identifying anomalies and suspicious or unusual behaviors and ending with detecting zero-day vulnerabilities and patching known ones.
Artificial intelligence can significantly improve intrusion detection systems (IDS). They are programs that aim to detect malicious activities in a specified environment They identify known attacks by checking against a database of attack signatures. IDS are not meant to prevent such intrusions, but merely to report in case an event matches a signature to a human security officer, who will take the necessary measures. They have two major downsides which can be eliminated by ML:
- large amount of false alarms
- inability to detect new (never seen before) attacks
Another ML application in this area is the data loss/leak prevention (DLP).
DLP software solutions allow us to set business rules that classify confidential and sensitive information so that they cannot be disclosed maliciously or accidentally by unauthorized end users. These solutions can be improved by using ML algorithms and two types of examples: positive examples (i.e. content that needs to be protected) and counterexamples (i.e. documents that are similar to the positive set but should not be protected).
Our vision: cybersecurity has tremendous growth potential. We know that without utilizing the full power of ML and AI the cyberspace will not be secure enough. Our framework is accurate and flexible and can support quick model development for a wide variety of clients and scenarios. Fast measures have to be taken against the constantly changing and evolving cyber threats.
How we can help: We can build and train ML models and provide software infrastructure capable of processing thousands of variables