Biblio

Secure logging as an indispensable part of any secure system in practice is well-understood by both academia and industry. However, providing security for audit logs on an untrusted machine in a large distributed system is still a challenging task. The emergence and wide availability of log management tools prompted plenty of work in the security community that allows clients or auditors to verify integrity of the log data. Most recent solutions to this problem focus on the space-efficiency or public verifiability of forward security. Unfortunately, existing secure audit logging schemes have significant performance limitations that make them impractical for realtime large-scale distributed applications: Existing cryptographic hashing is computationally expensive for logging in task intensive or resource-constrained systems especially to prove individual log events, while Merkle-tree approach has fundamental limitations when face with highly concurrent, large-scale log streams due to its serially appending feature. The verification step of Merkle-tree based approach requiring a logarithmic number of hash computations is becoming a bottleneck to improve the overall performance. There is a huge gap between the flux of log streams collected and the computational efficiency of integrity verification in the large-scale distributed systems. In this work, we develop a novel scheme, performance of which favorably compares with the existing solutions. The performance guarantees that we achieve stem from a novel data structure called concurrent authenticated tree, which allows log events concurrently appending and removes the need to wait for append operations to complete sequentially. We implement a prototype using chameleon hashing based on discrete log and Merkle history tree. A comprehensive experimental evaluation of the proposed and existing approaches is used to validate the analytical models and verify our claims. The results demonstrate that our proposed scheme verifying in a concurrent way is significantly more efficient than the previous tree-based approach.

During an advanced persistent threat (APT), an attacker group usually establish more than one C&C server and these C&C servers will change their domain names and corresponding IP addresses over time to be unseen by anti-virus software or intrusion prevention systems. For this reason, discovering and catching C&C sites becomes a big challenge in information security. Based on our observations and deductions, a malware tends to contain a fixed user agent string, and the connection behaviors generated by a malware is different from that by a benign service or a normal user. This paper proposed a new method comprising filtering and clustering methods to detect C&C servers with a relatively higher coverage rate. The experiments revealed that the proposed method can successfully detect C&C Servers, and the can provide an important clue for detecting APT.