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High-Assurance Software

At HASLab, we anchor our research on a rigorous approach to three areas of Computer Science: Software Engineering, Distributed Systems, and Cryptography and Information Security.

Our contributions to these areas range from fundamental research on formal methods and algorithms, to applied research on the development of tools and middleware that address real-world demands stemming from long-term collaborations with industry.

Latest News
Computer Science

INESC TEC receives students of the Summer on the Campus 2019

The Computation Without Borders, an activity integrated in the Summer on the Campus 2019 programme and organised in collaboration with INESC TEC's High-Assurance Software Laboratory, had the participation of 28 secondary school students and took place between 22 and 26 July in the Department of Informatics of the University of Minho, in Braga.

16th August 2019

Computer Science

INESC TEC researchers elected for the School and Scientific Boards of EEUM

Luís Soares Barbosa, researcher of INESC TEC’s High-Assurance Software Laboratory (HASLab) and Rui Carlos Oliveira, member of INESC TEC's board and also researcher of HASLab, were elected for the School and Scientific Boards of the School of Engineering of the University of Minho (EEUM), respectively

30th July 2019

INESC TEC participates in IoT Week 2019

Within the framework of the recently adopted European project InterConnect, INESC TEC participated in the 9th edition of IoT Week, which was held between 17 and 21 June in Aarhus, Denmark. INESC TEC is the leading partner of this project, which will officially start in October of 2019.

26th July 2019

Computer Science

Issuing and using the driving licence is possible through a smartphone

The issue and use of the driving licence in smartphones and other mobile devices, with integrity and authenticity assurances of information, user privacy and interoperability between different countries, will give rise to an international standard ISO 18013-5, which was debated in the meeting of the working group of the International Organization for Standardisation (ISO) that took place between 1 and 4 July at INESC TEC’s headquarters, in Porto.

26th July 2019

Computer Science

INESC TEC's researchers receive Special Mention Award

The paper entitled “GreenHub Farmer: Real-world data for Android Energy Mining” was distinguished with the Special Mention Award in the Data Showcase Track of the MSR 2019 conference.

10th July 2019

Interest Topics
032

Featured Projects

InterConnect

Interoperable Solutions Connecting Smart Homes, Buildings and Grids

2019-2023

OLM2

OutSystems Learnability Model - 2nd phase

2019-2020

CLOUD4CANDY

Cloud for CANDY

2019-2019

HADES

HArdware-backed trusted and scalable DEcentralized Systems

2018-2021

MaLPIS

Aprendizagem Automática para Deteção de Ataques e Identificação de Perfis Segurança na Internet

2018-2021

OLM

OutSystems Learnability Model

2018-2019

DaVinci

Distributed architectures: variability and interaction for cyber-physical systems

2018-2021

SAFER

Safery verification for robotic software

2018-2021

KLEE

Coalgebraic modeling and analysis for computational synthetic biology

2018-2021

KMSEC

Machine-checked proofs of AWS Key Management Service

2018-2019

RCS

Reinsurance collaborate system

2018-2020

CRDB

Consulting on CRDTs for Redis Lab

2018-2020

EMRPrototype

Development of training videos illustrating the use of Medicaltech's SDIM system

2018-2019

InteGrid

Demonstration of INTElligent grid technologies for renewables INTEgration and INTEractive consumer participation enabling INTEroperable market solutions and INTErconnected stakeholders

2017-2020

Lightkone

Lightweight Computation for Networks at the Edge

2017-2019

CloudDBAppliance

European Cloud In-Memory Database Appliance with Predictable Performance for Critical Applications

2016-2019

GSL

GreenSoftwareLab: Towards an Engineering Discipline for Green Software

2016-2019

Cloud-Setup

PLATAFORMA DE PREPARAÇÃO DE CONTEÚDOS AUDIOVISUAIS PARA INGEST NA CLOUD

2016-2019

DSGrid

Digital Systems Technology for Next Generation Grid Automation

2016-2018

CORAL-TOOLS

CORAL – Sustainable Ocean Exploitation: Tools and Sensors

2016-2018

SafeCloud

Secure and Resilient Cloud Architecture

2015-2018

NanoStima-RL1

NanoSTIMA - Macro-to-Nano Human Sensing Technologies

2015-2019

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2019

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

UPGRID

Real proven solutions to enable active demand and distributed generation flexible integration, through a fully controllable LOW Voltage and medium voltage distribution grid

2015-2017

PaaS2

Integrated Management of PaaS services

2014-2015

LeanBigData

Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics

2014-2017

Practice

Privacy-Preserving Computation in the Cloud

2013-2016

CoherentPaaS

A Coherent and Rich PaaS with a Common Programming Model

2013-2016

PaaS

Integrated Management of PaaS services

2013-2015

Cloud

Integrated Management of Cloud Services with Operations Support Systems

2012-2013

WEB2Economy

Web 2.0 Platform - WEB2Economy

2011-2012

Team
001

Laboratories

CLOUDinha Laboratory

Publications

HASLAB Publications

View all Publications

2019

Anomaly Detection and Modeling in 802.11 Wireless Networks

Authors
Allahdadi, A; Morla, R;

Publication
CoRR

Abstract

2019

Predicting throughput in IEEE 802.11 based wireless networks using directional antenna

Authors
Kandasamy, S; Morla, R; Ramos, P; Ricardo, M;

Publication
Wireless Networks

Abstract
In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In this paper we study the relationship between the (Formula presented.) metric, which captures interference, and throughput for IEEE 802.11 based network using directional antenna. The (Formula presented.) model was found to best represent the relationship between the interference metric and the network throughput. We use this model to predict the performance of similar networks and decide the best configuration a network operator could use for planning his network. © 2017 Springer Science+Business Media, LLC

2019

Rapid detection of spammers through collaborative information sharing across multiple service providers

Authors
Azad, MA; Morla, R;

Publication
Future Generation Computer Systems

Abstract
Spammers and telemarketers target a very large number of recipients usually dispersed across many Service Providers (SPs). Collaboration and Information sharing between SPs would increase the detection accuracy but detection effectiveness depends on the amount of information shared between SPs. Having service provider's exchange call detail records would arguably attain the best detection accuracy but would require significant network resources. Moreover, SPs are likely to feel uncomfortable in sharing their call records because call records contain user's private information as well as operational details of their networks. The challenge towards the design of collaborative Spam over Internet Telephony (SPIT) detection system is two-fold: it should attain high detection accuracy with a small false positive, and should fully protect the privacy of users and their service providers. In this paper, we propose a COllaborative Spit Detection System (COSDS)-a collaborative SPIT detection system for the Voice over IP (VoIP) network where service providers collaborate for the effective and early detection of SPIT callers without raising privacy concerns. To this extent, COSDS relies on a trusted Centralized Repository (CR) and exchange of non-sensitive reputation scores. The CR computes global reputation of users by aggregating the reputation scores provided by the respective collaborating SPs. The data exchanged to the CR is not sensitive regarding users privacy, and cannot be used to infer the relationship network of users. We evaluate the performance of our system using synthetic data that we have generated by simulating the realistic social behavior of spammers and non-spammers in a network. The results show that the COSDS approach has better detection accuracy as compared to the traditional stand-alone detection systems. For instances, in a setup where spammers are making calls to recipients of many SPs, COSDS successfully identifies spammers with the True Positive (TP) rate of around 80% and false positive (FP) rate of around 2% on a first day, which further increases to 100% TP rate and zero FP rate in three days. COSDS approach is fast, requires a small communication overhead, ensures privacy of users and collaborating SP, and requires only few iterations for the reputation convergence within the SP. © 2018 Elsevier B.V.

2019

Anomaly Detection and Modeling in 802.11 Wireless Networks

Authors
Allahdadi, A; Morla, R;

Publication
J. Network Syst. Manage.

Abstract

2019

Scalable eventually consistent counters over unreliable networks

Authors
Almeida, PS; Baquero, C;

Publication
Distributed Computing

Abstract

Supervised Theses

2018

Trade-offs between privacy and efficiency on databases

Author
Rogério António da Costa Pontes

Institution
UP-FCUP

2018

Avaliação Realista de Sistemas de Armazenamento

Author
Alexandre Silva

Institution
UM

2018

Distributed Black-Box Data Tracing

Author
Catarina Cardoso

Institution
UM

2018

Security Analysis of a Closed-Source Signal Protocol Implementation

Author
João Diogo Gaspar Alves

Institution
UP-FCUP

2018

Computação colaborativa segura num cenário de gestão de risco financeiro

Author
Tiago André Ferreira Fernandes

Institution
UP-FCUP

Facts & Figures

3Concluded PhD theses

2016

340EU Programmes (k€)

2016

0R&D Employees

2016