Dynamic Malware Detection via Sandbox Packet Analysis
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Solution Overview
Problem
Conventional methods for identifying malware in data packets are ineffective in detecting new malware threats, as they rely on pattern matching and signature recognition, which fail to identify hidden or newly developed malicious content.
Innovation Solution
A method that involves receiving data packets, executing instructions associated with them, and observing actions to identify unauthorized behavior, thereby detecting malware. This method also includes not sending the last data packet to the destination if malware is identified, preventing its delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pattern matching and signature recognition methods are used to scan data packets, then existing known malware can be detected, but new and hidden malware threats cannot be identified
Solution Approach 1:
The patent transitions from static signature-based detection to dynamic behavior analysis. The system executes malware samples in a controlled sandbox environment and observes their runtime behavior, such as file creation, registry modifications, network connections, and process spawning. This dynamic approach allows the system to detect new and unknown malware variants by analyzing their operational characteristics rather than relying on pre-defined signatures, thereby resolving the contradiction between reliable detection of known malware and adaptability to new threats.
Solution Approach 2:
The patent introduces a sandbox environment as an intermediary between the network scanner and the malware samples. This isolated execution environment allows safe capture and analysis of malware behavior without risking system compromise. The sandbox acts as a mediator that enables the collection of behavioral data, which is then processed to generate detection rules, thus enabling both reliable detection and adaptability to new threats simultaneously.
2Measurement precision
If deep packet inspection is performed to scan for malware, then existing malware signatures can be identified, but the system cannot detect hidden or obfuscated malware content
Solution Approach 1:
The patent replaces the mechanical approach of packet inspection with a virtualized execution approach. Instead of parsing network packets for signatures, the system downloads and executes the malware in a virtual sandbox, observing its actual behavior. This substitution enables the system to detect hidden and obfuscated malware that evades traditional inspection methods, as the sandbox can uncover malicious behavior regardless of how the malware is obfuscated in the network traffic.
3Adaptability or versatility
If dynamic behavior analysis is performed on all received data packets, then new malware threats can be detected, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary actions by capturing and analyzing only the first few packets from each data stream, which typically contain sufficient information to identify malware behavior patterns. By making this preliminary assessment early in the connection, the system can quickly determine whether to allow or block the traffic, avoiding the need to analyze every subsequent packet in detail. This preliminary action approach enables new malware detection while minimizing processing time.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary portion of data (first N packets) rather than processing the entire data stream. The system determines an appropriate sample size that provides sufficient behavioral information for malware detection without requiring complete packet analysis. This partial analysis approach balances detection accuracy with processing efficiency, reducing time loss while maintaining the ability to detect new malware threats.
4Productivity
If all data packets are sent to the destination computer without analysis, then network throughput is maintained, but malware can be delivered and executed successfully
Solution Approach 1:
The patent extracts and isolates the analysis function from the normal data transmission path. By using a separate sandbox environment that operates independently from the main network infrastructure, the system can analyze packets without significantly impacting overall network throughput. The extraction allows malicious content to be identified and blocked while maintaining normal productivity for legitimate traffic, as the analysis occurs in a parallel, isolated environment rather than blocking the main data path.
Data Source
AI summary
This disclosure is related to methods and apparatus used to for preventing malicious content from reaching a destination via a dynamic analysis engine may operate in real-time when packetized data is received. Data packets sent from a source computer may be received and be forwarded to an analysis computer that may monitor actions performed by executable program code included within the set of data packets when making determinations regarding whether the data packet set should be classified as malware. In certain instances all but a last data packet of the data packet set may also be sent to the destination computer while the analysis computer executes and monitors the program code included in the data packet set. In instances when the analysis computer identifies that the data packet set does include malware, the malware may be blocked from reaching the destination computer by not sending the last data packet to the destination computer.