Malware static analysis and DDoS capabilities detection

by   Mounir Baammi, et al.

The present thesis addresses the topic of denial of service capabilities detection at malware binary level, with the aim of designing a framework that integrate results from different binary analysis methods and decide on the DDoS capabilities of the analysed malware. We have implemented a process to extract meaningful data from malware samples, the extracted data was used to find characteristics and features that can lead to the detection of DDoS capabilities in binaries. Based on the discoveries, a set of rules was elaborated to detect those features in binaries. The method is tested on a dataset of 815 samples. Another dataset of 525 benign binaries is also used to test false positives rate of the implemented method. The results of our method are compared with Virus Total analysis results to assess our detection approach.



There are no comments yet.


page 19

page 27

page 28

page 29

page 30

page 31

page 32


A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images

Our computer systems for decades have been threatened by various types o...

Antiforensic techniques deployed by custom developed malware in evading anti-virus detection

Both malware and antivirus detection tools advance in their capabilities...

SCGDet: Malware Detection using Semantic Features Based on Reachability Relation

Recently, with the booming development of software industry, more and mo...

Early Detection of In-Memory Malicious Activity based on Run-time Environmental Features

In recent years malware has become increasingly sophisticated and diffic...

Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS

Cloud Infrastructure as a Service (IaaS) is vulnerable to malware due to...

RoboMal: Malware Detection for Robot Network Systems

Robot systems are increasingly integrating into numerous avenues of mode...

Identifying meaningful clusters in malware data

Finding meaningful clusters in drive-by-download malware data is a parti...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.