kalinin, maxim pavlenko, evgeny gavva, georgij pakhomov, maxim
The paper proposes a technique for protecting reconfigurable networks that implements topology rebuilding, which combines immunization and network gaming methods, as a solution for maintaining cyber resilience. Immunization presumes an adaptive set of protective reconfigurations destined to ensure the functioning of a network. It is a protective re...
xinzhong, su youyun, xu
Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in mor...
Saeed, Mozamel M.
Published in
Open Computer Science
This article investigates the effect of cybersecurity knowledge on the ability to detect malicious events in a network. We developed a simplified intrusion detection system (IDS) to simulate real-world scenarios and assess detection capabilities. The IDS features typical network intrusion characteristics, such as signature-based detection and anoma...
erel-özçevik, müge özçift, akın özçevik, yusuf yücalar, fatih
In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud servers, each with their own trade-...
marcillo, pablo suntaxi, gabriela hernández-álvarez, myriam
Due to the expansion of Artificial Intelligence (AI), especially Machine Learning (ML), it is more common to face confidentiality regulations about using sensitive data in learning models generally hosted in cloud environments. Confidentiality regulations such as HIPAA and GDPR seek to guarantee the confidentiality and privacy of personal informati...
elgarhy, islam badr, mahmoud m. mahmoud, mohamed alsabaan, maazen alshawi, tariq alsaqhan, muteb
In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are trained on benig...
bashir, syed raza raza, shaina misic, vojislav
As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of cont...
najar, karim nylander, ola woxnerud, william
An assessment method for sustainability was developed by the authors in a previous article. Many social sustainability assessment methods rely on assessors’ subjective judgments, which can be problematic. This study aims to examine the level of consensus different assessors can achieve using various assessment methods and to compare their results w...
Soliman, Francesca Dinesson, Kajsa
Over the past two decades, UK counterterrorism efforts have involved contentious legal and social measures aimed at preventing harm and increasing security. Critical scholars argue that these measures narrowly define security, risk, and harm, failing to recognise intervention itself as a potential threat to individuals and communities. Despite sign...
tayyeh, huda kadhim ahmed al-jumaili, ahmed sabah
Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem. Understanding how different hyperparameters affect this balance is crucial for optimizing FL systems. ...