This aspect of cybersecurity focuses on developing methods and strategies to detect malicious activities within an organization’s network and systems. It involves creating, deploying, and maintaining detection mechanisms such as alerts, signatures, and behavioural analytics to identify potential threats and anomalies.
Let’s explain the main types of detection engineering:
- Signature-Based Detection
This type uses predefined patterns or known Indicators of Compromise (IOCs) to identify malicious activity. These patterns can include specific sequences of bytes, known malicious IP addresses, or file hashes. It’s effective at detecting known threats quickly and with accuracy, but it’s very limited in detecting new or unknown threats (zero-day exploits) and can be easily evaded by slight modification in the attack patterns.
- Anomaly-Based Detection
This detection method involves establishing a baseline of normal behaviour for users, systems, and networks, and then identifying deviations from this baseline as potential threats. It’s a strong mechanism to detect previously unknown threats and sophisticated attacks that do not match known signatures, but on the other hand there’s a high likelihood of false positives, as deviations from normal behaviour can occur due to benign reasons.
- Behavioural Detection
It’s a similar to Anomaly-based detection, but it focuses on identifying unusual behaviours that may indicate malicious activities, including abnormal access patterns, unusual data transfers or atypical user actions. It’s effective at identifying insider threats and sophisticated attacks that don’t have a clear signature, but it can generate false positives and requires continuous learning and adaptation to maintain accurate results.
- Heuristic-Based Detection
This type uses rules and algorithms to identify potentially malicious activities based on known techniques, tactics, and procedures (TTPs) of attackers, these can be derived from past incidents and threat intelligence. It’s very good at identifying new variations of known attacks and provide a broader scope of detection, but it might not catch highly sophisticated or custom-build malware, and can produce false positives.
- Machine Learning-Based Detection
Using machine learning algorithms to analyse vast amounts of data and identify patterns that may indicate malicious activities. These algorithms can be trained on historical data to improve detection capabilities over time. Can dynamically adapt to new threat patterns by handling large datasets, reducing the need for manual updates. But it requires a significant amount of data for training and can be complex to implement and maintain, it might also produce false positives if the model is not properly tuned.
- Rule-Based Detection
This one relies on predefined rules and logic to detect suspicious activities. These rules are often based on compliance requirements, threat intelligence, and known attack patterns. It’s very easy to implement and manage, and is an effective way to detecting straightforward attack scenarios, but due to its limited flexibility it might miss more sophisticated or subtle threats that do not match the predefined rules.
- Hybrid Detection
Combines multiple detection methods to enhance the overall detection capability. This approach leverages the strengths of each method and mitigate their weaknesses. It provides a comprehensive coverage and improves the accuracy and reliability of threat detection. It can be very complex to implement and manage, and may require significant resources to maintain.
Detection engineering is essential to build a good security posture, as it gives organizations the power to identify and respond to threats in a timely manner. By employing a mix of these detection methods, security teams can improve their ability to detect a wide range of malicious activities and reduce the risk of successful attacks.
