Biblio
We have been investigating methods for establishing an effective, immediate defense mechanism against the DDoS attacks on Web applications via hacker botnets, in which this defense mechanism can be immediately active without preparation time, e.g. for training data, usually asked for in existing proposals. In this study, we propose a new mechanism, including new data structures and algorithms, that allow the detection and filtering of large amounts of attack packets (Web request) based on monitoring and capturing the suspect groups of source IPs that can be sending packets at similar patterns, i.e. with very high and similar frequencies. The proposed algorithm places great emphasis on reducing storage space and processing time so it is promising to be effective in real-time attack response.
Due to the unavailability of signatures for previously unknown malware, non-signature malware detection schemes typically rely on analyzing program behavior. Prior behavior based non-signature malware detection schemes are either easily evadable by obfuscation or are very inefficient in terms of storage space and detection time. In this paper, we propose GZero, a graph theoretic approach fast and accurate non-signature malware detection at end hosts. GZero it is effective while being efficient in terms of both storage space and detection time. We conducted experiments on a large set of both benign software and malware. Our results show that GZero achieves more than 99% detection rate and a false positive rate of less than 1%, with less than 1 second of average scan time per program and is relatively robust to obfuscation attacks. Due to its low overheads, GZero can complement existing malware detection solutions at end hosts.