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In the rapidly evolving digital world, network security is a critical concern. Traditional security measures often fail to detect unknown attacks, making anomaly-based Network Intrusion Detection Systems (NIDS) using Machine Learning (ML) vital. However, ...

Context: In contemporary Information Systems development, effective management of data provenance has become pivotal for ensuring transparency, accountability, and reproducibility, particularly in the context of data-intensive applications.

Problem: ...

In Brazil, there are over 7 million individuals with visual disabilities (PDVs), including at least 500,000 blind individuals, according to IBGE 2019 data. Orientation and Mobility (OM) training is crucial for PDVs to navigate, identify objects, ...

Good scientific work requires comprehensible, transparent and reproducible research. One way to ensure this is to include all data relevant to a study or evaluation when publishing an article. This data should be at least aggregated or anonymized, at ...

Social media is an ever-evolving web based platform for sharing thoughts, opinions, ideas and other contents. Among all social media networks, Twitter has become one of the most popular social networking/micro-blogging sites, allowing users to share ...

The success of Bitcoin and other cryptocurrencies is drawing significant interest to blockchains. A blockchain system implements a tamper-evident ledger for recording transactions that modify some global states. The system captures the entire ...

Auditing is an increasingly essential tool for the defense of computing systems, but the unwieldy nature of log data imposes significant burdens on administrators and analysts. To address this issue, a variety of techniques have been proposed for ...

Recent advances in the causal analysis can accelerate incident response time, but only after a causal graph of the attack has been constructed. Unfortunately, existing causal graph generation techniques are mainly offline and may take hours or days to ...

Scientific research has always struggled with problems related to reproducibility caused in part by low data sharing rates and lack of provenance. Credibility of the research hypothesis comes into question when results cannot be replicated. While the ...

Data provenance and lineage are critical for ensuring integrity and reproducibility of information in research and application. This is particularly challenging for distributed scenarios, where data may be originating from decentralized sources without ...

Digital forensics are becoming increasingly important for the investigation of computer-related crimes, white-collar crimes and massive hacker attacks. After an incident has been detected an appropriate incident response is usually initiated with the ...

Data sharing is an integral component of research and academic publications, allowing for independent verification of results. Researchers have the ability to extend and build upon prior research when they are able to efficiently access, validate, and ...

The development of Cloud processing and Big Data have raised many concerns over the use to which data is being put. These concerns have created new demands for methodologies, and capabilities which can provide transparency and trust in data provenance ...

With the increasing advancement of Internet of Things (IoT) enabled systems, smart medical devices open numerous opportunities for the healthcare sector. The success of using such devices in the healthcare industry depends strongly on secured and ...

The redesign of cloud storage with the amalgamation of cooperative cloud and an immutable and unhackable distributed database blockchain thrives towards a strong CIA triad and secured data provenance. The conspiracy ideology associated with the ...

Smart grid empowers home owners to efficiently manage their smart home appliances within a Home Area Network (HAN), by real time monitoring and fine-grained control. However, it offers the possibility for a malicious user to intrude into the HAN and ...

Regression tests are executed after every change in software. In a software development environment that adopts Continuous Software Engineering practices such as Continuous Integration, Continuous Delivery and Continuous Deployment, software is changed, ...

Data provenance tracking determines the set of inputs related to a given output. It enables quality control and problem diagnosis in data engineering. Most existing techniques work by tracking program dependencies. They cannot quantitatively assess the ...

A scientific software ecosystem aims to integrate all stages of an experiment and its related workflows, in order to solve complex problems. In this vein, in order to assure the experiment proper execution, any modification that occurs must be ...

Provenance is becoming increasingly important as more and more people are using data that they themselves did not generate. In the last decade, significant efforts have been directed toward developing generic, shared data provenance ontologies that ...