lanter官网破解版

Dynamic Analysis, API Call Graph based techniques (Call Graph Clustering, Call Graph Similarities, etc..)

• Extracting call graphs from executable files using API Call sequences generated from Dynamic Analysis Sandboxing or using Static Analysis Disassembling techniques
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• Clustering known malware samples using approximation algorithms like k-means clustering, DBSCAN clustering, etc..
• Classifying unknown samples using this clusters and implemented similarity comparison techniques

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• Extracting call graphs from executable files using API Call sequences generated from Dynamic Analysis Sandboxing
• Categorizing API calls in to high level operation groups
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• Implementation of techniques to recognize common patterns on generated sequences (longest Common Subsequence, Edit Distance, etc.)
• Revealing common call sequence patterns of different malware families
• Removing Sequence patterns seen on benign samples to minimize false positive rate.

Static Analysis, Opcode Sequence based techniques

• Op-code extraction from clean and malware samples
• To use Op-code Sequence information: Implementation of statistical classification techniques and training (using n-grams, Levenshtein / Euclidean distance, or other sequence similarity search techniques)
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New methods to combine Dynamic and Static Analysis results

• Combination of individual analysis results
• Performing hybrid analysis (using combined features gathered from both Static and Dynamic analysis)

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• Implementation of featur extraction techniques specific to each document type (PDF, MS document, etc.)
• Signature extraction using known malicious samples and signature based detection
• Heuristicaly classifying benign and malware samples

Active-learning Generic Signatures for specific malware families

• Implementation of signature extraction techinques(Static / Dynamic)
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Researching file reputation by statistic

Different statistic behaviours can be researched by criterions for file classification (malware, safe, system update etc.,). In study, differenet types of statistics: geo-distribution, frequency/time graphs, populating through subnets by time etc. can be used.

Fully automated signature generation based on machine learning models

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• Dynamic Analysis, API Call Sequence based techniques
• Static Analysis, Opcode Sequence based techniques
• New methods to combine Dynamic and Static Analysis results
• Active-learning Generic Signatures for specific malware families

Unpacking Techniques

• Dynamic Analysis, API Call Graph based techniques (Call Graph Clustering, Call Graph Similarities, etc.)
• Dynamic Analysis, API Sequence based techniques
• Static Analysis, Opcode Sequence based techniques
• Malicious Documents detections techniques (malicious JavaScript blocks on PDF samples, etc.)
• Active-learning Generic Signature for specific malware families

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There are fuzzy hash algorithms like nilsimsa and spamsum for detecting similarity between 2 text pieces. In order to detect similar bulk mails properly, we need to be able group (cluster) them based in a efficient way Since ASLab has continuous data flow, this module need to perform a stream clustering algorithm.

Anomaly detection in bulk mails

Spam/Phishing mail are usually sent in numbers. Howerver there are also legit mails, like newsletters, which are also sent in bulk. Creating patterns for legit bulk mails (by observing for a duration) and detecting out of the ordinary bulk mails will help creating alerts for suspecious behavior.