SeMaMatch: Semantic Malware Matching
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Rodríguez-Fernández V
(2017)
Analysing temporal performance profiles of UAV operators using time series clustering
in Expert Systems with Applications
Menéndez HD
(2021)
Getting Ahead of the Arms Race: Hothousing the Coevolution of VirusTotal with a Packer.
in Entropy (Basel, Switzerland)
Menéndez HD
(2019)
Mimicking Anti-Viruses with Machine Learning and Entropy Profiles.
in Entropy (Basel, Switzerland)
Menéndez H
(2016)
Medoid-based clustering using ant colony optimization
in Swarm Intelligence
Menéndez H
(2017)
Extending the SACOC algorithm through the Nyström method for dense manifold data analysis
in International Journal of Bio-Inspired Computation
Menéndez H
(2019)
The arms race: Adversarial search defeats entropy used to detect malware
in Expert Systems with Applications
Menéndez H
(2021)
Diversifying Focused Testing for Unit Testing
in ACM Transactions on Software Engineering and Methodology
Menendez, H.
(2016)
Extending the SACOC algorithm through the Nystrom method for Dense Manifold Data Analysis
in International Journal of Bio-Inspired Computation
Martín A
(2016)
MOCDroid: multi-objective evolutionary classifier for Android malware detection
in Soft Computing
Martín A
(2016)
Advances in Artificial Intelligence
Description | We have shown that compression programs and an initial zoo of malware can be used to detect malware with 98% accuracy. We have performed a series of experiments that establish the robustness of this claim. We have developed a tool (EnTS) that is more lightweight than using compression and has equivalent accuracy. Again, we have robust experimental evidence for our accuracy claim. We have engineered a new tool (EEE) that has successfully attacked the classification ability of EnTS as well as other compression and information theory based detection methods. EEE is based on search algorithms which attack weaknesses in the detection methods. We have applied EEE to modify malware submitted to the VirusTotal web site and analysed the co-evolution of detection and disguise of malware in a cutting edge context. We have used a similar approach to Android malware classification into families through the use of search algorithms to force misclassification during malware triage. |
Exploitation Route | These findings are sufficiently strong as to warrant both further research and commercialisation. |
Sectors | Aerospace Defence and Marine Agriculture Food and Drink Chemicals Construction Creative Economy Digital/Communication/Information Technologies (including Software) Energy Manufacturing including Industrial Biotechology Retail Security and Diplomacy Transport Other |
URL | https://github.com/hdg7/IagoDroid |
Description | Previous Research Associate for SeMaMatch has moved to FaceBook and has been using Kolmogorov complexity based ideas, especially the Normalised Compression Distance, as a similarity measure to solve code similarity problems at Facebook. Other previous Research Associate is now an academic at Middlesex University. |
First Year Of Impact | 2019 |
Sector | Digital/Communication/Information Technologies (including Software) |
Impact Types | Economic |
Title | EnTS ML database |
Description | This dataset contains the Entropy Profiles for a subset of Kaggle malware Competition files, VirusShare Win32 malware using different packing systems and Windows benign-ware. The data is a training and test set for new machine learning techniques on entropy time series for malware detection and classification. |
Type Of Material | Database/Collection of data |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | None as yet. The data is not openly available until |
URL | https://data.mendeley.com/datasets/rxnx8rzwph/draft |
Description | Collaboration with Charles III University, Madrid |
Organisation | Charles III University of Madrid |
Country | Spain |
Sector | Academic/University |
PI Contribution | We have written a paper together (DOI) below and planned to collaborate further. The collaboration is now dormant. |
Collaborator Contribution | As above (the contributions are symmetric). |
Impact | DOI: 10.1016/j.eswa.2017.11.032 |
Start Year | 2016 |
Title | EEE: the evolutionary entropy based packer |
Description | A modified packer (modifications to UPX) that uses search algorithms to produce programs that can evade virus detection based on entropy |
Type Of Technology | Software |
Year Produced | 2017 |
Impact | None so far |
URL | https://github.com/hdg7/EEE |
Title | EnTS: Entropy Time Series Analysis tool |
Description | This tool generates a simplified entropy profile of a file as a fixed length time series. |
Type Of Technology | Webtool/Application |
Year Produced | 2016 |
Impact | None yet. Note that, since the associated paper is under anonymous submission, we cannot openly publish the URL of the tool except via application to the PI. This will change once the paper is accepted. |
URL | https://www.mendeley.com/sign-in/?routeTo=https%3A%2F%2Fapi.mendeley.com%2Foauth%2Fauthorize%3Fredir... |
Title | IagoDroid |
Description | This is an academic tool that forces misclassification of Android Malware |
Type Of Technology | Software |
Year Produced | 2017 |
Impact | None as yet. |
URL | https://github.com/hdg7/IagoDroid |
Title | MimicAV |
Description | Allows mimicking of the behaviour of anti-virus programs |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | None so far |
URL | https://github.com/hdg7/MimickAV |
Description | COW 27: workshop on Malware |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Workshop for academics involved in research on malware. Attended by some industrial people and government representatives. Main outcomes were knowledge and information sharing as well as some new collaborations and reinforcement of existing collaborations. |
Year(s) Of Engagement Activity | 2013 |
URL | http://crest.cs.ucl.ac.uk/cow/27/ |
Description | COW 41: workshop on Software Engineering and Computer Science using information theory |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Workshop for software engineering researchers to learn about different applications of information theory to software engineering and computer science problems. The previous RA gave a talk on the Journal of Computer Security submission, "Detecting Malware with Information Complexity". |
Year(s) Of Engagement Activity | 2015 |
URL | http://crest.cs.ucl.ac.uk/cow/41/ |
Description | Malware MSc course 2014-2015 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Presented overview of Journal of Computer Security submission entitled "Detecting Malware with Information Complexity" in the UCL MSc malware module. Later asked students to carry out a coursework based on available tools. |
Year(s) Of Engagement Activity | 2014 |
Description | Malware MSc course 2015-16 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Presented an overview of the ideas in the Journal of Computer Security submission entitled "Detecting Malware with Information Complexity" to a UCL MSc module. |
Year(s) Of Engagement Activity | 2015 |
Description | Malware MSc module 2016-2017 |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Knowledge and Technology Transfer |
Year(s) Of Engagement Activity | 2016 |