Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

1st Symposium of Applied Science for Young Researchers: proceedings

SASYR, the rst Symposium of Applied Science for Young Researchers, welcomes works from young researchers (master students) covering any aspect of all the scienti c areas of the three research centres ADiT-lab (IPVC, Instituto Polit ecnico de Viana do Castelo), 2Ai (IPCA, Instituto Polit ecnico do C avado e do Ave) and CeDRI (IPB, Instituto Polit ecnico de Bragan ca). The main objective of SASYR is to provide a friendly and relaxed environment for young researchers to present their work, to discuss recent results and to develop new ideas. In this way, it will provide an opportunity to the ADiT-lab, 2Ai and CeDRI research communities to gather synergies and indicate possible paths for future joint work. We invite you to join SASYR on 7 July and to share your research!info:eu-repo/semantics/publishedVersio

Detecting cyberstalking from social media platform(s) using data mining analytics

Cybercrime is an increasing activity that leads to cyberstalking whilst making the use of data mining algorithms to detect or prevent cyberstalking from social media platforms imperative for this study. The aim of this study was to determine the prevalence of cyberstalking on the social media platforms using Twitter. To achieve the objective, machine learning models that perform data mining alongside the security metrics were used to detect cyberstalking from social media platforms. The derived security metrics were used to flag up any suspicious cyberstalking content. Two datasets of detailed tweets were analysed using NVivo and R Programming. The dominant occurrence of cyberstalking was assessed with the induction of fifteen unigrams identified from the preliminary dataset such as “abuse”, “annoying”, “creep or creepy”, “fear”, “follow or followers”, “gender”, “harassment”, “messaging”, “relationships p/p”, “scared”, “stalker”, “technology”, “unwanted”, “victim”, and “violent”. Ordinal regression was used to analyse the use of the fifteen unigrams which were categorised according to degree or relationship/link towards cyberstalking on the platform Twitter. Moreover, two lightweight machine learning algorithms were used for the model performance showcasing cyberstalking indicative content. K Nearest Neighbour and K Means Clustering were both coded in R computer language for the extraction, refined, analysation and visualisation process for this research. Results showed the emotional terms like “bad”, “sad” and “hate” were attached to the unigrams being linked to cyberstalking. Each emotional term was flagged up in correspondence with one of the fifteen unigrams in tweets that correlate cyberstalking indicative content, proving one must accompany the other. K Means Clustering results showed the two terms “bad” and “sad” were shown within 100 percent of the clustering results and the term “hate” was only seen within 60 percent of the results. Results also revealed that the accuracy of the KNN algorithm was up to 40% in predicting key terms-based cyberstalking content in a real Twitter dataset consisting of 1m data points. This study emphasises the continuous relationship between the fifteen unigrams, emotional terms, and tweets within numerous datasets portrayed in this research, and reveals a general picture that cyberstalking indicative content in fact happens on Twitter at a vast rate with the corresponding links or relationships within the detection of cyberstalking

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity