Matteo Dell'Amico

Matteo Dell'Amico

Matteo Dell'Amico

Matteo received his Ph.D. in Computer Science in 2008 at the University of Genoa (Italy).

He worked at EURECOM (France) from 2008 to 2014. His current research is focused on the design of scalable algorithms to make sense of massive security data, and on ways to reason on trust and reputation on the Internet.

Matteo's research interests touch distributed systems and security; and since joining the research group in 2014, he has investigated topics such as peer-to-peer systems, machine learning, reputation systems, distributed backup and storage, recommender systems, scheduling, and password security.

Selected Academic Papers

Journey to the Center of the Cookie Ecosystem: Unraveling Actors' Roles and Relationships

In Proceedings of the 42nd IEEE Symposium on Security and Privacy (S&P 2021) Our analysis lets us paint a highly detailed picture of the cookie ecosystem, discovering an intricate network of connections between players that reciprocally exchange information and include each other's content in web pages whose owners may not even be aware.

Scalable k-nn based text clustering

In Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015)
We use distributed and scalable clustering techniques to cluster text data based on the edit distance metric.

HFSP: Bringing Size-Based Scheduling To Hadoop

IEEE Transactions on Cloud Computing, 2015
HFSP is a scheduler for Hadoop inpired by the FSP algorithm. Like FSP, HFSP improves the scheduling both in terms of service time and fairness.

Can I Opt Out Yet? GDPR and the Global Illusion of Cookie Control

In Proceedings of the 14th ACM Asia Conference on Computer and Communications Security (ACM ASIACCS 2019)
We evaluate both the information presented to users and the actual tracking implemented through cookies; we find that the GDPR has impacted website behavior in a truly global way, both directly and indirectly. On the other hand, we find that tracking remains ubiquitous.

Smoke Detector: Cross-Product Intrusion Detection With Weak Indicators

In Proceedings of the Annual Computer Security Applications Conference (ACSAC 2017)
Smoke Detector significantly expands upon limited collections of hand-labeled security incidents by framing event data as relationships between events and machines, and performing random walks to rank candidate security incidents. Smoke Detector significantly increases incident detection coverage for mature Managed Security Service Providers.

PSBS: Practical Size-Based Scheduling

IEEE Transactions on Computers, 2016
Size-based scheduling algorithms can perform disastrously with skewed workloads and incorrect size information. PSBS is a scheduling discipline that performs very well even when job sizes are incorrect.

Improving population estimation from mobile calls: a clustering approach

In Proceedings of the 21st IEEE Symposium on Computers and Communication (ISCC 2016)
We use distributed and scalable clustering techniques to perform estimation of population estimation, including mobility, based on mobile phone calls data.

Monte Carlo Strength Evaluation: Fast and Reliable Password Checking

In Proceedings of the 22nd ACM Conference on Computer and Communications Security (ACM CCS 2015)
A method for scalable password strength checking reflecting the effort that state-of-the-art attackers would need to guess them.

Efficient and Self-Balanced ROLLUP Aggregates for Large-Scale Data Summarization

In Proceedings of the IEEE 4th International Congress on Big Data (BigData Congress 2015)
The ROLLUP primitive allows summarizing complex and large datasets. We develop an efficient implementation for Apache Pig.

RiskTeller: Predicting the Risk of Cyber Incidents

In Proceedings of the 24th ACM Conference on Computer and Communications Security (ACM SIGSAC 2017)

Webs of Trust: Choosing Who to Trust on the Internet

To appear in the proceedings of the ENISA Annual Privacy Forum (APF 2020)
We discuss the problem of creating an open, decentralized, secure and privacy-aware reputation system for the Internet.

Beyond Precision and Recall: Understanding Uses (and Misuses) of Similarity Hashes in Binary Analysis

In Proceedings of the 8th ACM Conference on Data and Application Security and Privacy (CODASPY 2018)
Fuzzy hashing algorithms are a cheap and convenient way to find similarity in files. We evaluate how various of these algorithms perform for various tasks in binary analysis.

NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data

In Proceedings of the VLDB Endowment, Vol. 10, No. 3, 2016
A scalable and distributed implementation of the DBSCAN clustering algorithm. The particularity of NG-DBSCAN is that it works scalably based on arbitrary data and distance functions.

Hierarchical Incident Clustering for Security Operation Centers

In Proceedings of the Interactive Data Exploration and Analytics Workshop (IDEA 2018)
We enable security incident responders to dispatch multiple similar security incidents at once through an intuitive user interface. The heart of our algorithm is a visualized hierarchical clustering technique that enables responders to identify the appropriate level of cluster granularity at which to dispatch multiple incidents.

Lean On Me: Mining Internet Service Dependencies From Large-Scale DNS Data

In Proceedings of the 33th Annual computer Security Applications Conference (ACSAC 2017)
To assess the security risk for a given entity, and motivated by the effects of recent service disruptions, we perform a large-scale analysis of passive and active DNS datasets including more than 2.5 trillion queries in order to discover the dependencies between websites and Internet services.

A Field Study of Computer-Security Perceptions Using Anti-Virus Customer-Support Chats

In Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI 2019)
To identify needs for improvement in security products, we study security concerns raised in Norton Security customer support chats. We found that many consumers face technical support scams and are susceptible to them. Findings also show the value of customer support centers in that 96% of customers that reach out for support in relation to scams have not paid the scammers

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