Answer: Signature-based detection is a malware detection approach that identifies a malware instance by the presence at least one byte code pattern present in a database of signatures from known malicious programs. If a program contains a pattern that exists within the database, it is deemed

This dissertation develops techniques, based on monitoring network traffic, that automate signature generation for wide-spreading malicious payloads such as Internet worms. Fast signature detection is required to achieve effective content-based filtering. The main thesis is that content prevalence

Abstract. This paper introduces a novel approach to user event reconstruction by showing the practicality of generating and implementing signature-based analysis methods to reconstruct high-level user actions from a collection of low-level traces found during a post-mortem forensic analysis of a

—semanticanalysis based signature generation. We show that by backtracing the chain of tainted data structure rooted at the detection point, TaintCheck can automatically identify which original flow and which part of the original flow have caused the attack and identify important invariants of the payload that can

extensive binary rewriting and other changes to the system software. In this paper, we examine a signature-based detection of CRAs, where the attack is detected by observing the behavior of programs and detecting the gadget execution patterns. We first demonstrate that naive signature-based defenses can

or require extensive binary rewriting and other changes to the system software. In this paper, we examine a signature-based detection of CRAs, where the attack is detected by observing the behavior of programs and detecting the gadget execution patterns. We first demonstrate that naive signature-based

Signature-based detection relies on patterns present in viruses and provides a relatively simple and efficient method for detecting known viruses. At present, most anti-virus systems rely primarily on signature detection. Metamorphic viruses are one of the most difficult types of viruses to detect

fully automatic signature-based document image retrieval system that handles: 1) Automatic detection and segmentation of signatures from document images and 2) Translation, scale, and rotation invariant signature matching for document image retrieval. We treat signature retrieval in the unconstrained

to detect attacks as they occur or after the attacks took place. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. In this context, signature-based network intrusion detection techniques are a valuable technology

is presenting a method to detect this software based on the digital signature of the malware. Our aim is to obtain a dedicated detection scheme for different state-of-the-art digital signatures. Such detection scheme should be optimal from performance point of view.