PARIS: A Real-Time Malicious Behavior Detection System Using Adaptive Trace Fetching

Główne pojęcia

PARIS is a novel system that addresses the limitations of existing malware detection methods by using adaptive trace fetching to enable real-time, low-overhead detection of malicious behavior on Windows systems.

Streszczenie

PARIS: A Practical, Adaptive Trace-Fetching and Real-Time Malicious Behavior Detection System

This research paper introduces PARIS, a novel system designed for real-time detection of malicious behavior in Windows environments. The authors highlight the increasing sophistication of cyberattacks, particularly Advanced Persistent Threats (APTs), which often employ stealth tactics to evade traditional detection methods.

Existing static analysis methods are limited in their ability to detect obfuscated or polymorphic malware, while traditional dynamic monitoring approaches struggle with high overhead and evasion techniques. PARIS addresses these challenges by leveraging Event Tracing for Windows (ETW) to selectively collect and analyze maliciousness-related API call stacks, significantly reducing data overhead while maintaining high detection accuracy.

Key Innovations of PARIS:

• Adaptive Trace Fetching: PARIS dynamically identifies and collects only the most relevant API call stacks, minimizing resource consumption. This is achieved through graph-based API selection, API association analysis, call stack selection, and loop compression techniques.
• Real-time Behavior Detection: By reducing data overhead, PARIS enables real-time analysis of process behavior, allowing for timely detection and response to threats.
• Focus on Malicious Behaviors: PARIS prioritizes the identification of malicious behaviors (Potential Harmful Functions - PHFs) commonly observed in APT attacks, providing deeper insights into attacker tactics and intentions.

Methodology:

The researchers developed a prototype of PARIS and evaluated its performance in real-world settings using both benign and malicious datasets. They assessed system overhead, accuracy of behavior recognition, and the impact of different models and parameters.

Key Findings:

• Significant Data Reduction: PARIS achieved over 98.8% reduction in data size compared to raw ETW traces, leading to substantial savings in memory, bandwidth, and CPU usage.
• Low Overhead: PARIS demonstrated minimal impact on system performance, with an average memory usage of 32MB, bandwidth of 0.77kb/s, and CPU usage of 4.79%.
• High Detection Accuracy: Despite the significant data reduction, PARIS maintained a high detection accuracy of 93.6%, comparable to offline methods.

Significance:

PARIS represents a significant advancement in real-time malware detection by effectively balancing the trade-off between overhead and accuracy. Its ability to identify malicious behaviors in real-time with minimal system impact makes it a valuable tool for enhancing cybersecurity posture.

Limitations and Future Research:

The authors acknowledge the reliance on the assumption that ETW remains uncompromised. Future research could explore methods to enhance the resilience of PARIS against potential attacks targeting the ETW framework. Additionally, expanding the system's capabilities to encompass other operating systems beyond Windows would broaden its applicability.

Statystyki

PARIS can reduce over 98.8% of data compared to the raw ETW trace.
PARIS can run stably on the client for a long time with an average resource overhead of 32MB memory usage and 4.79% CPU usage.
PARIS transmits at an average network bandwidth of 0.77kb/s.
PARIS achieves a detection accuracy of 93.6%.

Cytaty

Kluczowe wnioski z

PARIS: A Practical, Adaptive Trace-Fetching and Real-Time Malicious Behavior Detection System

by Jian Wang, L... o arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.01273.pdf

Głębsze pytania

How could PARIS be adapted to detect emerging threats and zero-day attacks that may not exhibit known malicious behaviors?

While PARIS demonstrates effectiveness in detecting known malicious behaviors, addressing emerging threats and zero-day attacks requires adapting its core mechanisms:

Incorporating Anomaly Detection: Beyond classifying known behaviors, PARIS could integrate anomaly detection techniques. This would involve establishing a baseline of normal system and application behavior using machine learning models. Deviations from this baseline, such as unusual API call sequences, frequencies, or timings, could signal potentially malicious activity, even if the specific behavior is unknown.

Dynamically Updating Behavior Models: PARIS's effectiveness relies on its behavior models. To keep pace with emerging threats, these models need frequent updates. This could involve:

Automated Model Retraining: Periodically retraining the models on new malware samples and updated threat intelligence feeds.
Online Learning: Implementing online learning algorithms that allow the models to adapt and learn from new data in real-time, without requiring a complete retraining cycle.

Leveraging Threat Intelligence: Integrating external threat intelligence feeds can provide PARIS with information about emerging threats, including Indicators of Compromise (IOCs) like suspicious IP addresses, domain names, or file hashes. This information can be used to enhance PARIS's detection capabilities for zero-day attacks.

Focusing on Behavioral Patterns: Instead of relying solely on specific API calls, PARIS could focus on identifying broader behavioral patterns associated with malicious activity. For example, detecting attempts to escalate privileges, access sensitive data, or establish persistence, regardless of the specific APIs used, can be indicative of an attack.
By incorporating these adaptations, PARIS can become more proactive in identifying emerging threats and zero-day attacks, even those that do not exhibit previously observed malicious behaviors.

Could the adaptive trace fetching techniques employed by PARIS be leveraged for other security applications beyond malware detection, such as intrusion prevention or anomaly detection?

Yes, the adaptive trace fetching techniques employed by PARIS hold significant potential for various security applications beyond malware detection:

Intrusion Prevention Systems (IPS): PARIS's ability to identify malicious behaviors in real-time can be directly applied to IPS. By monitoring system calls and API usage, an IPS enhanced with PARIS's techniques could detect and block malicious activity as it happens, preventing intrusions before they cause significant damage. For example, detecting a sequence of system calls indicative of a buffer overflow attack could trigger an immediate response, blocking the attack and protecting the system.

Anomaly Detection: PARIS's adaptive trace fetching, combined with its ability to establish baselines of normal behavior, makes it well-suited for anomaly detection. By selectively monitoring and analyzing system events, PARIS can identify deviations from established norms, signaling potential security threats or system misconfigurations. This can be particularly valuable in complex environments where defining specific attack signatures is challenging.

Security Information and Event Management (SIEM): SIEM systems aggregate and analyze security data from various sources. PARIS's adaptive trace fetching can enhance SIEM systems by providing a more focused and efficient way to collect and analyze system-level events. This can reduce the volume of data processed by SIEM systems, improving performance and enabling faster threat detection and response.

Data Loss Prevention (DLP): DLP solutions aim to prevent sensitive data from leaving the organization's control. PARIS's ability to monitor file system activity and API calls related to data access and transmission can be leveraged to identify and prevent unauthorized data exfiltration attempts. For example, detecting attempts to read sensitive files and then send them over the network could trigger a DLP alert, preventing data leakage.
By adapting its core principles of selective data collection, behavior analysis, and real-time monitoring, PARIS's adaptive trace fetching techniques can significantly benefit a wide range of security applications, enhancing their effectiveness and efficiency.

What are the ethical implications of real-time behavior monitoring systems like PARIS, and how can privacy concerns be addressed while ensuring effective security measures?

Real-time behavior monitoring systems like PARIS, while offering significant security benefits, raise important ethical considerations, particularly regarding privacy:

Data Collection and Use: The very nature of PARIS involves collecting and analyzing potentially sensitive data about user actions and system activities. This raises concerns about:

Scope of Collection: Clearly defining what data is collected, for what purpose, and for how long it is retained is crucial. Limiting collection to security-relevant data and minimizing the retention period are essential.
Data Security: Robust security measures must be in place to protect collected data from unauthorized access, use, or disclosure. Encryption, access controls, and secure storage are paramount.
Purpose Limitation: Using collected data solely for its intended security purpose and preventing repurposing for other uses, such as employee monitoring or targeted advertising, is crucial.

Transparency and Control: Users have the right to know:

If and how their activities are being monitored. Clear and concise disclosures about the system's functionality, data collection practices, and potential implications for privacy are essential.
Mechanisms for control: Providing users with options to control the level of monitoring, such as opting out of certain types of data collection or setting thresholds for alerts, can empower them and address privacy concerns.

Bias and Discrimination: Behavior monitoring systems trained on historical data can inherit and perpetuate existing biases. This can lead to discriminatory outcomes, such as falsely flagging certain users or activities as suspicious based on biased data. Mitigating bias requires:

Diverse Training Data: Ensuring the training data used to build behavior models is diverse and representative to minimize bias.
Regular Auditing: Regularly auditing the system's performance to identify and correct for any discriminatory outcomes or unintended consequences.

Accountability and Oversight: Clear lines of accountability for the system's operation, data handling practices, and any privacy implications are essential. Independent oversight mechanisms, such as audits or reviews by ethics boards, can provide additional assurance and accountability.
Balancing effective security with privacy requires a thoughtful and multifaceted approach. By incorporating privacy-enhancing technologies, adhering to ethical data handling practices, and prioritizing transparency and user control, real-time behavior monitoring systems like PARIS can offer valuable security benefits while respecting individual rights and freedoms.