CloudAV: N-Version Antivirus in the Network Cloud

By Jon Oberheide, Evan Cooke, Farnam Jahanian. USENIX Security Symposium, 2008

353 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1ca0b25bd4fe4994f7f5e334987588776a1a123c

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Citations in the corpus

AVclass: A Tool for Massive Malware Labeling (2016, background): “Some works evaluate different thresholds [20,22] showing that small threshold increases, quickly reduce the number of samples considered malicious.”
A survey of mobile cloud computing: architecture, applications, and approaches (2013, background): “The platform consists of host agent and network service components [82,83].”
Abusing File Processing in Malware Detectors for Fun and Profit (2012, background): “Many attacks are detector-specific, thus applying multiple detectors to the same file—as advocated by CloudAV [21]—may provide better protection, at a significant performance cost.”
Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel (2013, unknown): “, CloudAV [11]).”
CAMP: Content-Agnostic Malware Protection (2013, methodology): “Our evaluation shows that CAMP exhibits accuracy close to 99% relative to proprietary VM-based dynamic analysis, is able to process requests in less than 130 ms on average, and was able to detect approximately five million intentional malware downloads per month that were not detected by existing…”
CryptoLock (and Drop It): Stopping Ransomware Attacks on User Data (2016, methodology): “The ease with which ransomware can be written and obfuscated limits the effectiveness of traditional signaturebased detection schemes [36].”
An Android Application Sandbox system for suspicious software detection (2010, background): “[23] showed that the average time for receiving a signature for a new malware is about 48 days.”
Scalable fine-grained behavioral clustering of HTTP-based malware (2013, background): “As a consequence, AV companies have a hard time keeping their signature databases up to date, and their AV scanners often suffer from a high rate of false negatives [28].”
Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces (2010, methodology): “Results from our evaluation, which includes real-world deployment, confirm the effectiveness of the proposed clustering system and show that our approach can aid the process of automatically extracting network signatures for detecting HTTP traffic generated by malware-compromised machines.”
Paranoid Android: versatile protection for smartphones (2010, background): “have explored AV file scanning in the cloud with [29] and [30], but file scans are not able to detect zerodays, remote exploits, or memory-resident attacks (all of which have targeted mobile phones in the past [20, 14, 31, 25]).”