. This approach is not useful for newly evolved worms due to the unavailability of their signatures. Present worm signature generation work needs manual analysis which is time consuming process. To circumvent these problems substantial efforts have been made which automate the process of worm signature generation

Several syntactic-based automatic worm signature generators, e.g., Polygraph, have recently been proposed. These systems typically assume that a set of suspicious flows are provided by a flow classifier, e.g., a honeynet or an intrusion detection system, that often introduces “noise ” due

Fast and accurate generation of worm signatures is essential to contain zero-day worms at the Internet scale. Recent work has shown that signature generation can be automated by analyzing the repetition of worm substrings (i.e., fingerprints) and their address dispersion. However, at the early

Abstract--Network worms are a growing threat to today’s Internet-connected networks and hosts. Modern worms can spread very quickly and widely so automatic response is required to contain worm outbreaks. In this paper, we propose a system that can automatically generate useful signatures that can

be used as signatures. Semantic-analysis based signature generation can be more accurate, resilient against polymorphic worms, and robust to attacks exploiting polymorphism than the pattern-extraction based signature generation methods.

of novel Internet worms. Generation of the worm signatures required by an IDS—the byte patterns sought in monitored traffic to identify worms—today entails non-trivial human labor, and thus significant delay: as network operators detect anomalous behavior, they communicate with one another and manually

It is widely believed that content-signature-based intrusion detection systems (IDSes) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match

with a range of unique sources generating infections and destinations being targeted. More importantly, our approach – called “content sifting ” – automatically generates precise signatures that can then be used to filter or moderate the spread of the worm elsewhere in the network. Using a combination

Abstract. DNA micro-arrays now permit scientists to screen thousands of genes simultaneously and determine whether those genes are active, hyperactive or silent in normal or cancerous tissue. Because these new micro-array devices generate bewildering amounts of raw data, new analytical methods must

RSA assumption, hence the name. Strong RSA was previously used to construct signature schemes without random oracles. However, signatures generated by our scheme are much shorter and simpler than signatures from schemes based on Strong RSA.