How Can Machine Learning Be Used For Security?
Machine Learning has become key to refining and advancing many crucial technologies such as process automation, IoT, Natural Language Processing, and cybersecurity, and many more. Machine learning uses historical data and data-driven learning models to learn patterns of behavior. It then utilizes this learning to analyze, detect, and respond to potential threats in real-time.
In cybersecurity, machine learning is used to reduce the workload of security analysts by flagging behavioral anomalies. Use cases include malicious program detection and spam filtering across different platforms.
Machine Learning and cybersecurity
Machine learning models constantly learn from the data they are fed. This capability is used to recognize patterns for the detection of threats to personal or organizational data. Machine learning algorithms are able to detect phishing attacks, perimeter intrusions and malware, among others. It efficiently assesses vulnerabilities and alerts the cybersecurity experts to take appropriate remedial actions.
Cybersecurity is most useful when it works in real-time to detect attacks as they are happening. Real-time machine learning engines observe the system and identify malicious threats in real-time. Supervised learning (based on training data) helps the classifiers to accurately categorize threats.
These models are also being trained and utilized to automate potential security breaches and could become a breakthrough measure.
Phishing attacks, which are a type of social engineering attack, are an excellent use case for machine learning in cybersecurity. Traditional phishing detection techniques lack accuracy and real-time analysis of threats. Machine learning changes that. URL classification models actively detect malicious activities such as in emails. After being trained on analyzing email headers, punctuation patterns, etc. the machine learning models further classify malicious activities to identify spam.
Machine learning also aids in analyzing suspicious activities on clouds and networks. In networks, ML is useful for the analysis of previous attack datasets to train on and determine which areas in a network are prone to attacks.
Machine learning relies on training data to detect threats. When the data fed to the models is tainted, security can be compromised. Data poisoning and stealthy channel attacks can intrude before the models are trained, during the training cycles or even, or even after the training. There is also the threat of model stealing attacks, and model inversion attacks.
Machine learning models are dependent on cybersecurity protocols to generate desired outcomes from the training data. This is possible only when the data is secure and unaltered. The race between the intruders and the cyber security analysts will continue as advancements in technologies may have both positive and negative impacts.