Article
Psychology, Biological
Gustav Kuhn, Jeniffer Ortega, Keir Simmons, Cyril Thomas, Christine Mohr
Summary: This study examines the cognitive mechanism behind misinformation and its impact on belief. Using fake psychic demonstrations, the researchers found that even when warned, participants still increased their psychic beliefs after witnessing the performance. However, providing alternative explanations about the deceptive methods mitigated this effect. The realization of deception significantly reduced participants' psychic beliefs immediately after the performance and remained reduced even one week later.
QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY
(2023)
Proceedings Paper
Computer Science, Information Systems
Filipo Sharevski, Donald Gover
Summary: The study found that warning covers converted into speech may not work effectively; vaccine-hesitant users may ignore warnings for Tweets that align with their beliefs; politically independent users trust Alexa less but are better at accurately perceiving truthful COVID-19 information.
ARES 2021: 16TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY
(2021)
Article
Geriatrics & Gerontology
Juanita-Dawne Bacsu, Sarah Fraser, Alison L. Chasteen, Allison Cammer, Karl S. Grewal, Lauren E. Bechard, Jennifer Bethell, Shoshana Green, Katherine S. McGilton, Debra Morgan, Hannah M. O'Rourke, Lisa Poole, Raymond J. Spiteri, Megan E. O'Connell
Summary: This study examined the stigma against people with dementia during the COVID-19 pandemic using Twitter data. The research found that social media has been used to spread negative beliefs, stereotypes, and misinformation about dementia, but it can also be used to challenge and counteract stigma. Therefore, dementia education and awareness campaigns on social media are urgently needed to address COVID-19-related stigma.
Article
Engineering, Electrical & Electronic
Filipo Sharevski, Paige Treebridge, Jessica Westbrook
IEEE COMMUNICATIONS MAGAZINE
(2019)
Article
Computer Science, Information Systems
Alan T. Sherman, Linda Oliva, Enis Golaszewski, Dhananjay Phatak, Travis Scheponik, Geoffrey L. Herman, Dong San Choi, Spencer E. Offenberger, Peter Peterson, Josiah Dykstra, Gregory V. Bard, Ankur Chattopadhyay, Filipo Sharevski, Rakesh Verma, Ryan Vrecenar
IEEE SECURITY & PRIVACY
(2019)
Article
Computer Science, Cybernetics
Filipo Sharevski, Peter Jachim, Paige Treebridge, Audrey Li, Adam Babin, Christopher Adadevoh
Summary: Malexa, a malicious twin of Alexa, covertly rewords news briefings to intentionally introduce misperception about events and manipulate users' perception of government attitudes favoring big businesses over working people. Regardless of users' political ideology, gender identity, age, or frequency of interaction with intelligent voice assistants, Malexa is capable of inducing misperceptions. A system-level solution is proposed to counter Malexa's influence as a covert influencer in people's living environments.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
(2021)
Article
Computer Science, Information Systems
Filipo Sharevski, Peter Jachim
Summary: This paper presents the findings of an empirical study on the effectiveness of using intelligent voice assistant Alexa for delivering phishing training. The results indicate that an interaction-based training using Alexa can significantly improve users' ability to detect phishing emails, especially those employing persuasion principles such as authority, commitment, liking, and scarcity.
EURASIP JOURNAL ON INFORMATION SECURITY
(2022)
Article
Computer Science, Information Systems
Filipo Sharevski, Anna Slowinski, Peter Jachim, Emma Pieroni
Summary: In this paper, the perceived accuracy of COVID-19 vaccine information spoken by Amazon Alexa is analyzed. It is found that malicious third-party skills can reduce the perceived accuracy among users. Solutions for soft moderation against such skills are discussed.
INTERNET OF THINGS
(2022)
Proceedings Paper
Computer Science, Information Systems
Filipo Sharevski, Peter Jachim, Paige Treebridge, Audrey Li, Adam Babin
2020 IEEE EUROPEAN SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (EUROS&PW 2020)
(2020)
Proceedings Paper
Computer Science, Information Systems
Filipo Sharevski, Paige Treebridge, Jessica Westbrook
NSPW'19: PROCEEDINGS OF THE NEW SECURITY PARADIGMS WORKSHOP
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Adam Trowbridge, Filipo Sharevski, Jessica Westbrook
NSPW '18: PROCEEDINGS OF THE NEW SECURITY PARADIGMS WORKSHOP
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Filipo Sharevski
2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
(2018)
Review
Computer Science, Information Systems
Filipo Sharevski
EURASIP JOURNAL ON INFORMATION SECURITY
(2018)
Proceedings Paper
Education, Scientific Disciplines
Filipo Sharevski, Adam Trowbridge, Jessica Westbrook
PROCEEDINGS OF THE 8TH IEEE INTEGRATED STEM EDUCATION CONFERENCE (ISEC 2018)
(2018)
Article
Computer Science, Information Systems
Kashan Ahmed, Syed Khaldoon Khurshid, Sadaf Hina
Summary: This paper mainly introduces the construction of the cyber threat intelligence knowledge graph and the information extraction technique. By using joint extraction technique, it solves the problem of traditional techniques becoming ineffective due to the increasing size of CTI data. Experimental results show that this technique outperforms state-of-the-art models in knowledge triple extraction on CTI data and improves the F1 score.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Xinlong He, Yang Xu, Sicong Zhang, Weida Xu, Jiale Yan
Summary: This paper proposes a new membership inference attack method in federated learning, which utilizes data poisoning and sequence prediction confidence. The attack is effective and results in minimal overall model performance degradation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Tieming Chen, Huan Zeng, Mingqi Lv, Tiantian Zhu
Summary: In this paper, the authors propose a deep learning based dynamic malware detection method called CTIMD, which integrates threat knowledge from CTIs into the learning process of API call sequences with runtime parameters. Experimental results show that CTIMD outperforms existing methods in terms of performance.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wonwoo Choi, Minjae Seo, Seongman Lee, Brent Byunghoon Kang
Summary: This paper proposes SUM, a backward-edge control flow protection scheme for ARM Cortex-M processors. It combines MPU and the overlooked hardware feature FaultMask to achieve efficient and robust protection. The empirical evaluation shows minimal runtime overhead for the proposed solution.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Liliana Ribeiro, Ines Sousa Guedes, Carla Sofia Cardoso
Summary: Phishing susceptibility is influenced by individual and contextual factors. The study found that individuals who perceive themselves as capable of detecting phishing and those who use online services more frequently are more susceptible to phishing. However, technology competencies and other individual variables do not predict phishing susceptibility.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wenjie Wang, Yuanhai Shao, Yiju Wang
Summary: In this paper, we investigate the adversarial perturbations of twin support vector machines (TWSVMs) and propose an optimization framework, which provides explicit solutions to increase the interpretability of the conclusion and convenience for calculation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Snofy D. Dunston, V. Mary Anita Rajam
Summary: This paper proposes a novel adversarial attack technique that can synthesize adversarial images to mislead deep learning models, and also studies interpretability plots. The research findings show that the proposed attack technique influences the interpretability plots, regardless of the success of the attack.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Junchen Li, Guang Cheng, Zongyao Chen, Peng Zhao
Summary: Protocol Reverse Engineering (PRE) is a direct approach for analyzing unknown traffic. This paper proposes a method for clustering unknown traffic based on private protocol labels, and the experimental results demonstrate its advantages on real-world network traffic.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Rafal Kozik, Massimo Ficco, Aleksandra Pawlicka, Marek Pawlicki, Francesco Palmieri, Michal Choras
Summary: The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable, and ethical AI solutions. However, it has been shown that xAI can enable successful adversarial attacks in the domain of fake news detection, leading to a decrease in AI security. This paper presents an attack scheme that uses an explainable solution to reshape the structure of the original message, allowing the adversary to manipulate the model's prediction while keeping the message's meaning intact.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Benyuan Yang, Lili Luo, Zhimeng Wang
Summary: Interoperation is widely used in practical industrial applications, but merging local access control policies may lead to security violations. Dealing with these issues in a multidomain environment is critical, but finding the maximum secure interoperation among individual systems poses a challenge due to the large number of entities and access involved.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Binghui Zou, Chunjie Cao, Longjuan Wang, Sizheng Fu, Tonghua Qiao, Jingzhang Sun
Summary: The ongoing struggle between security researchers and malware has led to the exploration of using convolutional neural networks and capsule networks for classification and identification of malware. However, training these networks requires a significant amount of data and parameters, and the research on capsule networks is still in its early stages, posing challenges.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Hongsong Chen, Xingyu Li, Wenmao Liu
Summary: Multivariate time-series anomaly detection is crucial for maintaining normal operation of physical equipment. Recent advances have been made in this field, but two challenges have limited the model's ability to generalize. To address these challenges, a multivariate time-series anomaly detection model consisting of a characterization network and a forecasting network is proposed. Experimental results demonstrate that this method outperforms baseline methods in terms of detection performance and robustness.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Roberto Doriguzzi-Corin, Domenico Siracusa
Summary: This paper discusses the application of federated learning in the field of cybersecurity and proposes an adaptive mechanism-based federated learning solution for DDoS attack detection in dynamic cybersecurity scenarios. Through experiments, it is demonstrated that the proposed solution outperforms state-of-the-art federated learning algorithms in terms of convergence time and accuracy.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Antonio Giovanni Schiavone
Summary: The usage of HTTPS protocol is crucial for secure communication with websites, ensuring the confidentiality, integrity, and authenticity of online data transmissions. The Municipality2HTTPS research project analyzed the implementation of HTTPS in Italian municipalities' websites and identified areas for improvement.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Domna Bilika, Nikoletta Michopoulou, Efthimios Alepis, Constantinos Patsakis
Summary: Voice Assistants (VAs) are widely used in smart devices, but are vulnerable to attacks, as shown by experiments with popular VAs revealing successful attack rates exceeding 30% and statistical variations among vendors, calling for additional countermeasures to protect user information.
COMPUTERS & SECURITY
(2024)
