Definition
Advanced Threat Detection (ATD) is an advanced threat analysis and mitigation approach used to identify and stop cyber threats that are invincible by standard security systems. It focuses on new vulnerabilities, highly targeted attacks and long-term infiltrations.
Use Cases of Advanced Threat Detection
- Securing cloud environments: Traditional security systems are not fully capable of handling cloud security, and that’s where ATD comes in.
- Insider threat detection: ATD can be used to identify security loopholes that authorised users can exploit.
- Risk management and compliance: It is a requirement for organizations in various industries to manage risks.
- Mitigate data breaches: ATD can investigate deeper and identify unusual activities, such as unauthorized access or transfer of sensitive data.
- Stopping advanced persistent threats (ATPs): ATPs are complex attacks trying to steal an organization’s data over a long period and ATD has a better chance of stopping them.
- Protecting against zero-day exploits: ATD can safeguard you against emerging threats that haven’t been publicly reported.
Components of Advanced Threat Detection
- Sandboxing: ATDs use an isolated space (sandbox) to test suspicious files or programs. This ensures potential threats do not spread across the system or network.
- Threat Intelligence: ATD maintains an updated knowledge base of the latest vulnerabilities, attack methods, and threats to provide the best protection.
- Anomaly detection: The system monitors all network activities to identify anomalies. Examples of anomalies are strange data movement, unexpected access requests, and unusual traffic.
- Machine learning and AI: An ATD system uses artificial intelligence to improve detection by learning from previous attacks and adapting to zero-day attacks.
- Behavioural analysis: The system tracks all users’ activities and maintains a profile of each user’s behaviour in the network. Any unusual behaviour signals a threat in the network.
- Integration with other security measures: For comprehensive security, you can integrate ATD systems with intrusion detection systems, antivirus, and firewalls.
What are the Attackers After?
- Sensitive information or Intellectual property: Some attackers target the organization’s secrets to sell them to competitors or media outlets.
- Personally identifiable information (PII): PII can be used for identity theft or to perform further attacks. Examples of PII are ID numbers, social security numbers, birth dates, etc.
- User credentials: Some attackers target user credentials to access their devices or networks as legitimate users.
- Revenge: A disgruntled ex-employee or a hacktivist group may seek revenge by attacking an organization’s network.
Advanced Threat Detection Strategies
- Continuous data collection and analysis: Continuous data collection widens the system’s knowledge base and ensures all key incidents are recorded. Then, you can use a big data solution to analyse the data, monitor progress, and find solutions.
- Include benign software behaviour: You can use automation or live recording tools to understand how benign software interacts with your system. This can help detect malware that standard security measures are unable to detect.
- Create a broad test repository: Reliable threat detection results can only come from a broad test repository representing all known threats. You should also test potential threats against dynamic behaviours, forensic data from your system, traffic profiles and malware variants.