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The Remedium Cooperative Virus Defense System enhances military network security by addressing vulnerabilities exposed by threats like Conficker. Unlike traditional antivirus solutions, it empowers workstations to share knowledge about files, enabling the identification of trusted, suspicious, and harmful files. By utilizing a triad of workstations as command centers, the system ensures resilience and anomaly detection. Networks can scale with sub-triumvirates, while local databases safeguard knowledge. Performance and security measures are in place to mitigate risks, ensuring robust defense against evolving threats.
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remedium Cooperative virus defense system
Viruses such as conficker reveal weakness in military security level networks • Once a workstation inside an organization is compromised, other machines typically have no better defenses • Network administrators rely on third party security vendors to detect viruses The problem
Focus on trusted files • Unlike typical antivirus, we focus on gathering knowledge about the files on each workstation • Recognize trusted, suspicious, cautious and prejudicial files • Workstations share knowledge • Anomalies are detected if there is a mismatch between the files on a workstation compared to the other workstation in the same network • Keep administrators informed • if anomalies are found, suspicious workstations can be isolated The solution
Triumvirate • Uses 3 workstations as command and control centers to coordinate the sharing of knowledge • Using 3 machines allows byzantine decisions in case one of the triumvirs is marked as compromised • Can scale to sub-triumvirates according to number of clients connected • Periodically rotates the role of triumvir amongst members Network control
SQLite • Requires no additional setup on the workstation, good for restricted environments • Uses flat files to store data locally • Eases the transfer of databases between workstations • Allows performing checksum operations to prevent database corruption / tampering Database system
Cliens • Locally managed, each workstation adds knowledge about his own machine • Network • Inside a group, there is the knowledge from each workstation that is added and compared with each other • Official • Derives from us (the service provider) and contain processed knowledge of trusted files DB’s located in workstation
DB size • The potential pool of knowledge that can be stored is formidable • We are self-limiting the size of each DB to 4Gb • Currently indexes ~4 million records per DB • The initial idea was creating an Index DB to keep a pointer (checksum and filename) to the DB where the details of the target file are located: • Allows indexing ~8 million records on index DB • Requires to run two queries DB scalability
Increase limit of index DB to 8 Gb? • Searching records across 8Gb takes a longer time • No assurance that this limit is supported by sqlite implementation or by the file system • Still limited to 16 million records on index DB Alternatives?
Disk size • We limit the size of each DB to 4Gb • Currently indexes ~4 millions records per DB • Performance • Indexing 30 000 files on disk takes ~40 minutes • Comparing a single file on disk against three types of DB’s might become slow when multiplied by thousands of files • Security • Checksums prevent DB corruption • But if attacker corrupts index DB, the system is compromised • Attacker might also target binary itself Limitations
How can we improve the index system? Ideas? Comments?
