Detection across layers

High-fidelity, cross-layer detection is crucial in cloud environments due to their complex and dynamic nature. 

Why is this important? 

The Selenium Greed attack illustrates the critical importance of multi-layered detection in cloud security. The attack began by exploiting exposed Selenium Grid services, using the WebDriver API to execute a reverse shell on the target system. This initial compromise could have been detected through runtime monitoring at the application and system level. The attacker then leveraged the compromised system to download and execute a cryptomining payload, potentially exploiting overprivileged roles in the process, which highlights the need for robust identity and access management monitoring. Finally, the attacker abused system resources for cryptomining, emphasizing the importance of monitoring resource usage patterns and implementing Data Security Posture Management (DSPM). 

By correlating signals across these layers - from the initial exploit to resource abuse - security teams could have constructed a comprehensive view of the attack progression, enabling faster detection and more effective response to the threat. 

Cross-later detection enables: 

• Accurate detection on sophisticated cloud attacks 

• Faster threat identification and response 

• Reduced risk of business impact from cloud attacks 

Benefits include: 

• Significantly reduced alert noise 

• Increased actionable insights 

• Improved efficiency of security teams 

Legacy solutions often struggle with excessive false positives and noise, overwhelming security teams and potentially causing alert fatigue. This can lead to critical threats being missed or delayed responses to genuine security incidents. By implementing advanced, cross-layer detection, organizations can overcome these challenges and maintain a robust security posture in the cloud. 

Current state and challenges with writing detection rules 

Traditional detection rules face limitations in dynamic cloud environments: 

• Difficulty in keeping pace with rapidly changing infrastructure 

• Challenges in maintaining accuracy across diverse cloud services 

• High rate of false positives due to lack of context 

High rate of false positives by detecting across layers independently  

Common challenges: 

• Writing rules that adapt to cloud scalability and elasticity 

• Writing rules based on traditional threat intel sources  

• Balancing false positives and false negatives  

• Writing rules to keep pace wand provide coverage against constantly evolving threats 

• Tracking detection coverage and maintaining rules across multi-cloud environments 

Security teams often find themselves in a constant cycle of updating and fine-tuning rules to match the evolving cloud landscape. This not only consumes valuable time and resources but can also leave temporary gaps in detection coverage during the update process. 

Desired state and benefits 

Ideal multi-layered detection approach: 

• Adaptive rules that stay up to date with evolving threat actors 

• High-fidelity detection across layers  

• Cloud native threat intel  

• Reduction in overhead related to the manual nature of rule creation 

Benefits of integrating with real-time threat intelligence: 

• Up-to-date protection against emerging threats 

• Reduced time to detect and respond to incidents 

• Enhanced accuracy in threat identification 

Out-of-the-box detection rules provide immediate value and reduce the need for extensive customization. This approach allows security teams to hit the ground running with effective detection capabilities, while still allowing for customization as needed to address organization-specific requirements.   

Cloud Threat Intelligence (TI) 

Leveraging cloud-specific TI: 

• Analysis of cloud-focused attacker TTPs 

• Real-time updates on emerging cloud threats 

• Integration with cloud-native security tools 

Role of behavioral and signature-based detections: 

1. Behavioral: Identifying unusual patterns in cloud resource usage 

2. Signature-based: Detecting known malicious activities 

Cloud threat hunting involves proactively searching for hidden threats using TI and advanced analytics. This proactive approach helps organizations stay ahead of potential attackers by identifying and addressing vulnerabilities or suspicious activities before they can be exploited.   

Mix of behavioral/signature-based detections and context 

Balancing detection methods: 

• Behavioral: Adaptable to new threats, but potential for false positives 

• Signature-based: High accuracy for known threats, but may miss novel attacks 

Importance of context: 

• Enriches alerts with relevant cloud configuration data 

• Helps prioritize threats based on potential impact 

• Reduces false positives by considering normal operational patterns 

Example: An alert for data access from an unusual location is prioritized higher if the accessed resource contains PII. This context-aware approach ensures that security teams focus their efforts on the most critical threats first, improving overall incident response efficiency. 

Grouping 

Intelligent alert grouping: 

• Consolidates related alerts into a single incident 

• Provides a holistic view of attack progression 

• Reduces alert fatigue by presenting coherent narratives 

Benefits of grouping: 

• Improves investigation speed by connecting related events 

• Reduces Mean Time to Respond (MTTR) by streamlining analysis 

• Enables more efficient resource allocation for incident response 

By implementing these advanced detection strategies, organizations can significantly enhance their cloud security posture, reduce risk, and improve overall operational efficiency. Furthermore, intelligent grouping helps security analysts quickly understand the full scope of an incident, leading to more informed and effective response actions.