CS0-002 Exam Questions Tutorials

If an analyst observes evidence of an attack but cannot find an exploit that adequately explains the observations, it may indicate the presence of a zero-day vulnerability, which is an unknown vulnerability that attackers can exploit to gain unauthorized access to systems. In such cases, traditional security tools may not be able to detect or prevent the attack. Therefore, the analyst should investigate further to identify and mitigate the vulnerability to prevent further exploitation.

Supervised and unsupervised machine-learning algorithms are two types of machine-learning methods that are used in cybersecurity applications. Machine learning is a branch of artificial intelligence that enables systems to learn from data and improve their performance without explicit programming.

Supervised machine-learning algorithms are trained on labeled data, which means that each data point has a known outcome or class. Supervised algorithms learn to map input data to output data by finding patterns or rules from the training data. Supervised algorithms require security analyst feedback to provide labels for the data and evaluate the accuracy of the algorithm’s predictions. Examples of supervised machine-learning algorithms are classification and regression.

Unsupervised machine-learning algorithms are trained on unlabeled data, which means that each data point has no known outcome or class. Unsupervised algorithms learn to discover hidden structures or patterns from the data without any guidance or feedback. Unsupervised algorithms do not require security analyst feedback, as they do not rely on predefined labels or outcomes. Examples of unsupervised machine-learning algorithms are clustering and anomaly detection.

Insecure application programming interfaces (APIs) can lead to data compromise when using a PaaS solution. APIs are interfaces that allow applications to communicate with each other and with the underlying platform. APIs can expose sensitive data or functionality to unauthorized or malicious users if they are not properly designed, implemented, or secured. Insecure APIs can result in data breaches, denial of service, unauthorized access, or code injection .

Lessons learned is a critical stage of incident response that involves evaluating the effectiveness of the response, identifying gaps and areas for improvement, and updating the incident response plan accordingly1. Company leadership should be involved in this process to ensure they are aware of the incident, its impact, and the actions taken to prevent or mitigate future incidents. Additionally, company leadership can provide support and guidance for implementing the recommendations from the lessons learned session2.