arXiv subjects

Isabel Praça

Publications and source records attributed to Isabel Praça.

44 records · Page 3Linked to original sources

arXiv2022

A Multi-Policy Framework for Deep Learning-Based Fake News Detection

Connectivity plays an ever-increasing role in modern society, with people all around the world having easy access to rapidly disseminated information. However, a more interconnected society enables the spread of intentionally false information. To mitigate the negative impacts of fake news, it is essential to improve detection methodologies. This work introduces Multi-Policy Statement Checker (MPSC), a framework that automates fake news detection by using deep learning techniques to analyze a statement itself and its related news articles, predicting whether it is seemingly credible or suspicious. The proposed framework was evaluated using four merged datasets containing real and fake news. Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) models were trained to utilize both lexical and syntactic features, and their performance was evaluated. The obtained results demonstrate that a multi-policy analysis reliably identifies suspicious statements, which can be advantageous for fake news detection.

cs.CL↗

arXiv2022

Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially concerning. Nonetheless, an example generated for a domain with tabular data must be realistic within that domain. This work establishes the fundamental constraint levels required to achieve realism and introduces the Adaptative Perturbation Pattern Method (A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on pattern sequences that are independently adapted to the characteristics of each class to create valid and coherent data perturbations. The proposed method was evaluated in a cybersecurity case study with two scenarios: Enterprise and Internet of Things (IoT) networks. Multilayer Perceptron (MLP) and Random Forest (RF) classifiers were created with regular and adversarial training, using the CIC-IDS2017 and IoT-23 datasets. In each scenario, targeted and untargeted attacks were performed against the classifiers, and the generated examples were compared with the original network traffic flows to assess their realism. The obtained results demonstrate that A2PM provides a scalable generation of realistic adversarial examples, which can be advantageous for both adversarial training and attacks.

cs.CR↗

arXiv2021

Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space

Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an F1-score of 87.89% in the SWaT dataset.

cs.CR↗

arXiv2021

A tool to support the investigation and visualization of cyber and/or physical incidents

Investigating efficiently the data collected from a system's activity can help to detect malicious attempts and better understand the context behind past incident occurrences. Nowadays, several solutions can be used to monitor system activities to detect probable abnormalities and malfunctions. However, most of these systems overwhelm their users with vast amounts of information, making it harder for them to perceive incident occurrences and their context. Our approach combines a dynamic and intuitive user interface with Machine Learning forecasts to provide an intelligent investigation tool that facilitates the security operator's work. Our system can also act as an enhanced and fully automated decision support mechanism that provides suggestions about possible incident occurrences.

cs.CR↗

arXiv2021

A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System

Cybersecurity has been a concern for quite a while now. In the latest years, cyberattacks have been increasing in size and complexity, fueled by significant advances in technology. Nowadays, there is an unavoidable necessity of protecting systems and data crucial for business continuity. Hence, many intrusion detection systems have been created in an attempt to mitigate these threats and contribute to a timelier detection. This work proposes an interpretable and explainable hybrid intrusion detection system, which makes use of artificial intelligence methods to achieve better and more long-lasting security. The system combines experts' written rules and dynamic knowledge continuously generated by a decision tree algorithm as new shreds of evidence emerge from network activity.

cs.CR↗

arXiv2021

A Multi-Agent System for Autonomous Mobile Robot Coordination

The automation of internal logistics and inventory-related tasks is one of the main challenges of modern-day manufacturing corporations since it allows a more effective application of their human resources. Nowadays, Autonomous Mobile Robots (AMR) are state of the art technologies for such applications due to their great adaptability in dynamic environments, replacing more traditional solutions such as Automated Guided Vehicles (AGV), which are quite limited in terms of flexibility and require expensive facility updates for their installation. The application of Artificial Intelligence (AI) to increase AMRs capabilities has been contributing for the development of more sophisticated and efficient robots. Nevertheless, multi-robot coordination and cooperation for solving complex tasks is still a hot research line with increasing interest. This work proposes a Multi-Agent System for coordinating multiple TIAGo robots in tasks related to the manufacturing ecosystem such as the transportation and dispatching of raw materials, finished products and tools. Furthermore, the system is showcased in a realistic simulation using both Gazebo and Robot Operating System (ROS).

cs.RO↗

arXiv2021

Machine Learning for Network-based Intrusion Detection Systems: an Analysis of the CIDDS-001 Dataset

With the increasing amount of reliance on digital data and computer networks by corporations and the public in general, the occurrence of cyber attacks has become a great threat to the normal functioning of our society. Intrusion detection systems seek to address this threat by preemptively detecting attacks in real time while attempting to block them or minimizing their damage. These systems can function in many ways being some of them based on artificial intelligence methods. Datasets containing both normal network traffic and cyber attacks are used for training these algorithms so that they can learn the underlying patterns of network-based data. The CIDDS-001 is one of the most used datasets for network-based intrusion detection research. Regarding this dataset, in the majority of works published so far, the Class label was used for training machine learning algorithms. However, there is another label in the CIDDS-001, AttackType, that seems very promising for this purpose and remains considerably unexplored. This work seeks to make a comparison between two machine learning models, K-Nearest Neighbours and Random Forest, which were trained with both these labels in order to ascertain whether AttackType can produce reliable results in comparison with the Class label.

cs.CR↗

arXiv2021

A Search Engine for Scientific Publications: a Cybersecurity Case Study

Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large disciplinary spectrum and many significant research topics generate a considerable amount of information, making it hard for us to find what we are looking for when researching a particular subject. This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms to extract answers from a collection of domain-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.

cs.AI↗