601 research outputs found
Impacts of invasive Opuntia cacti on wild mammals in Kenya
In this thesis, I explored the impacts of invasive plants on animal behaviour, using the invasion of Opuntia cacti in Laikipia County, Kenya, as a specific case study. In the opening chapter, I introduced the topic of biological invasions, addressing essential background material and identifying key knowledge gaps.
In the second chapter, I focused on the impacts of invasive plants on animal behaviour, an important – yet neglected – topic. I synthesised the disparate literature on invasive plants’ behavioural impacts within a novel mechanistic framework, revealing that invasive plants can cause profound behavioural changes in native animals, with ecological consequences at multiple scales. I also found that environmental context played an important role in moderating how an invader’s modes of impact translate into behavioural changes in native species, and how these behavioural changes then generate ecological impacts. Finally, I identified priority research questions relating to the behavioural impacts of invasive plants.
Invasive plants’ behavioural impacts can manifest as changes to the occurrence patterns of native animals. In Chapter 3, I used simulations to explore model selection in occupancy models, which are a powerful tool for studying the patterns and drivers of occurrence. Specifically, I investigated the consequences of collider bias – a type of confounding that can arise when adding explanatory variables to a model – for model selection using the Akaike Information Criterion (AIC) and Schwarz Criterion (or Bayesian Information Criterion, BIC). I found that the effect of collider bias, and consequently the inferential and predictive accuracy of the AIC/BIC-best model, depended on whether the collider bias was present in the occupancy or detection data-generating process. My findings illustrate the importance of distinguishing between inference and prediction in ecological modelling and have more general implications for the use of information criteria in all linear modelling approaches.
In Chapter 4, I applied the mechanistic framework from Chapter 2 and the modelling conclusions from Chapter 3 to the problem of understanding Opuntia’s behavioural impacts in Laikipia County. Specifically, I used camera traps to explore the effects of Opuntia on occupancy and activity for eight key mammal species. I found that the effects of Opuntia varied among mammal species and depended on the spatial scale of the Opuntia cover covariate. These findings have important implications for the conservation of endangered mammal species in the region, the future spread of Opuntia through seed dispersal, and interactions between wildlife and local communities.
In Chapter 5, I addressed key knowledge gaps pertaining to Opuntia’s biotic interactions with native animals. First, I quantified the relationship between height and fruiting in O. engelmannii and O. stricta, finding that height was positively related to fruiting for both species, and that the relationship was stronger for O. engelmannii than for O. stricta. I also found that local habitat variables were related to height and/or fruiting in both Opuntia species. Second, I documented the interactions between animals and Opuntia using camera traps. In so doing, I confirmed the importance of interactions that were previously thought to be important, while also highlighting interactions which have previously received little attention in the published scientific literature
Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation
The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics
TIGER: Tor Traffic Generator for Realistic Experiments
Tor is the most widely adopted anonymity network, helping safeguard the privacy of Internet users, including journalists and human rights activists. However, effective attacks aimed at deanonymizing Tor users' remains a significant threat. Unfortunately, evaluating the impact such attacks by collecting realistic Tor traffic without gathering real users’ data poses a significant challenge.
This paper introduces TIGER (Tor traffIc GEnerator for Realistic experiments), a novel framework that automates the generation of realistic Tor traffic datasets towards improving our understanding of the robustness of Tor's privacy mechanisms. To this end, TIGER allows researchers to design large-scale testbeds and collect data on the live Tor network while responsibly avoiding the need to collect real users' traffic. We motivate the usefulness of TIGER by collecting a preliminary dataset with applicability to the evaluation of traffic confirmation attacks and defenses.Fundação para a Ciência e Tecnologia (FCT), grants PRT/BD/154197/2022 and UIDB/50021/2020 || NSERC, grant RGPIN-2023-03304 || IAPME, grant C6632206063-00466847 (SmartRetail)
Distributed Subweb Specifications for Traversing the Web
Link Traversal-based Query Processing (ltqp), in which a sparql query is
evaluated over a web of documents rather than a single dataset, is often seen
as a theoretically interesting yet impractical technique. However, in a time
where the hypercentralization of data has increasingly come under scrutiny, a
decentralized Web of Data with a simple document-based interface is appealing,
as it enables data publishers to control their data and access rights. While
ltqp allows evaluating complex queries over such webs, it suffers from
performance issues (due to the high number of documents containing data) as
well as information quality concerns (due to the many sources providing such
documents). In existing ltqp approaches, the burden of finding sources to query
is entirely in the hands of the data consumer. In this paper, we argue that to
solve these issues, data publishers should also be able to suggest sources of
interest and guide the data consumer towards relevant and trustworthy data. We
introduce a theoretical framework that enables such guided link traversal and
study its properties. We illustrate with a theoretic example that this can
improve query results and reduce the number of network requests. We evaluate
our proposal experimentally on a virtual linked web with specifications and
indeed observe that not just the data quality but also the efficiency of
querying improves.
Under consideration in Theory and Practice of Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest
achievements in AI development. The content generated by related applications,
such as text, images and audio, has sparked a heated discussion. Various
derived AIGC applications are also gradually entering all walks of life,
bringing unimaginable impact to people's daily lives. However, the rapid
development of such generative tools has also raised concerns about privacy and
security issues, and even copyright issues in AIGC. We note that advanced
technologies such as blockchain and privacy computing can be combined with AIGC
tools, but no work has yet been done to investigate their relevance and
prospect in a systematic and detailed way. Therefore it is necessary to
investigate how they can be used to protect the privacy and security of data in
AIGC by fully exploring the aforementioned technologies. In this paper, we
first systematically review the concept, classification and underlying
technologies of AIGC. Then, we discuss the privacy and security challenges
faced by AIGC from multiple perspectives and purposefully list the
countermeasures that currently exist. We hope our survey will help researchers
and industry to build a more secure and robust AIGC system.Comment: 43 pages, 10 figure
The Service Worker Hiding in Your Browser: Novel Attacks and Defenses in Appified Websites
The service worker (SW) is an emerging web technology that was introduced to enhance the
browsing experience of web users. At the core, it is essentially a JavaScript file that runs in an
isolated and privileged context separated from the main web page or web workers. Websites can register a service worker to enable native mobile application features including but not limited to supporting offline usage and sending push notifications. With the help of this technology, traditional websites can now act like native mobile apps or become appified. Recently, the use of service workers has gained much attention from web developers, security researchers, and even cyber-criminals due to the service worker’s unique capabilities, especially the ability to intercept and modify web requests and responses at runtime. Such capabilities inevitably introduce new factors to web security considerations.
The goal of this research is to systematically study both the vulnerabilities and the security enhancement to websites that can come with the introduction of service workers. The contributions of this dissertation are three folds. First, we investigate the service worker lifecycle and uncover a vulnerability allowing cross-site scripts to be executed inside the service worker. We term this novel attack as Service Worker Cross-Site Scripting (SW-XSS) and develop a dynamic taint tracking tool to measure the impact of SW-XSS in the wild. Second, we analyze the communication channels between the service worker and other web contexts. We identify two vulnerable channels, IndexedDB and Push notifications. These channels can be utilized to launch SW-XSS and push hijacking attacks, which can lead to the privacy leakage of users. Third, we propose and develop a framework, SWAPP (Service Worker APplication Platform), for implementing security appliances by leveraging the unique capabilities of a service worker. Not only can SWAPP prevent the aforementioned attacks against service workers but also be used to implement defense mechanisms for traditional web attacks such as Cross-Site Scripting (XSS), data leakage, or side-channel attacks. We develop several defenses for traditional attacks using SWAPP and show that they are easier to develop, have lesser installation requirements, and are effective compared to existing solutions
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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