553 research outputs found
GNN4IFA: Interest Flooding Attack Detection With Graph Neural Networks
In the context of Information-Centric Networking, Interest Flooding Attacks (IFAs) represent a new and dangerous sort of distributed denial of service. Since existing proposals targeting IFAs mainly focus on local information, in this paper we propose GNN4IFA as the first mechanism exploiting complex non-local knowledge for IFA detection by leveraging Graph Neural Networks (GNNs) handling the overall network topology. In order to test GNN4IFA, we collect SPOTIFAI, a novel dataset filling the current lack of available IFA datasets by covering a variety of IFA setups, including ?40 heterogeneous scenarios over three network topologies. We show that GNN4IFA performs well on all tested topologies and setups, reaching over 99% detection rate along with a negligible false positive rate and small computational costs. Overall, GNN4IFA overcomes state-of-the-art detection mechanisms both in terms of raw detection and flexibility, and – unlike all previous solutions in the literature – also enables the transfer of its detection on network topologies different from the one used in its design phase
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Availability, Integrity, and Confidentiality for Content Centric Network internet architectures
The Internet as we know it today, despite being ``the result of a series of accidents of choices'' in Prof. Jon Crowcroft's words, has undoubtedly been an amazing success story. However, it has been constantly challenged by the demands of the overwhelming evolution of data traffic types, non-functional needs of applications and users, and device diversity. The phrase ``future internet architecture'' can be interpreted as referring to a revised set of design principles. As Dr David Clark rightfully suggested, we need to ``allow for the future in the face of the present''. Content Centric Networking (CCN) is one of the candidates for a future internet architecture. Security is one of the most significant considerations while designing a future internet architecture. Availability, Integrity, and Confidentiality (AIC) are considered the three most crucial components of security: 1) availability is the assurance of continuous, reliable, and uninterrupted access to the information by authorized people, 2) integrity is the preservation of information and prevention of any change in it caused via accident or malicious intent, and 3) confidentiality is the ability to keep the information secret from unintended audience, intruders, and adversaries. This thesis discusses AIC related security threats and corresponding remedies for Named Data Networking (NDN) which is a promising example of CCN. It also presents a system dynamics modelling approach to bridge the gap between the technical solutions and business strategy by quantifying some of the qualitative variables salient to technology architects, policymakers, lawmakers, regulators, and internet service providers for the design of a future-proof internet architecture
Deep Insights of Deepfake Technology : A Review
Under the aegis of computer vision and deep learning technology, a new
emerging techniques has introduced that anyone can make highly realistic but
fake videos, images even can manipulates the voices. This technology is widely
known as Deepfake Technology. Although it seems interesting techniques to make
fake videos or image of something or some individuals but it could spread as
misinformation via internet. Deepfake contents could be dangerous for
individuals as well as for our communities, organizations, countries religions
etc. As Deepfake content creation involve a high level expertise with
combination of several algorithms of deep learning, it seems almost real and
genuine and difficult to differentiate. In this paper, a wide range of articles
have been examined to understand Deepfake technology more extensively. We have
examined several articles to find some insights such as what is Deepfake, who
are responsible for this, is there any benefits of Deepfake and what are the
challenges of this technology. We have also examined several creation and
detection techniques. Our study revealed that although Deepfake is a threat to
our societies, proper measures and strict regulations could prevent this
Falling for fake news: investigating the consumption of news via social media
In the so called ‘post-truth’ era, characterized by a loss of public trust in various institutions, and the rise of ‘fake news’ disseminated via the internet and social media, individuals may face uncertainty about the veracity of information available, whether it be satire or malicious hoax. We investigate attitudes to news delivered by social media, and subsequent verification strategies applied, or not applied, by individuals. A survey reveals that two thirds of respondents regularly consumed news via Facebook, and that one third had at some point come across fake news that they initially believed to be true. An analysis task involving news presented via Facebook reveals a diverse range of judgement forming strategies, with participants relying on personal judgements as to plausibility and scepticism around sources and journalistic style. This reflects a shift away from traditional methods of accessing the news, and highlights the difficulties in combating the spread of fake news
GUIDE FOR THE COLLECTION OF INSTRUSION DATA FOR MALWARE ANALYSIS AND DETECTION IN THE BUILD AND DEPLOYMENT PHASE
During the COVID-19 pandemic, when most businesses were not equipped for remote work and cloud computing, we saw a significant surge in ransomware attacks. This study aims to utilize machine learning and artificial intelligence to prevent known and unknown malware threats from being exploited by threat actors when developers build and deploy applications to the cloud. This study demonstrated an experimental quantitative research design using Aqua. The experiment\u27s sample is a Docker image. Aqua checked the Docker image for malware, sensitive data, Critical/High vulnerabilities, misconfiguration, and OSS license. The data collection approach is experimental. Our analysis of the experiment demonstrated how unapproved images were prevented from running anywhere in our environment based on known vulnerabilities, embedded secrets, OSS licensing, dynamic threat analysis, and secure image configuration. In addition to the experiment, the forensic data collected in the build and deployment phase are exploitable vulnerability, Critical/High Vulnerability Score, Misconfiguration, Sensitive Data, and Root User (Super User). Since Aqua generates a detailed audit record for every event during risk assessment and runtime, we viewed two events on the Audit page for our experiment. One of the events caused an alert due to two failed controls (Vulnerability Score, Super User), and the other was a successful event meaning that the image is secure to deploy in the production environment. The primary finding for our study is the forensic data associated with the two events on the Audit page in Aqua. In addition, Aqua validated our security controls and runtime policies based on the forensic data with both events on the Audit page. Finally, the study’s conclusions will mitigate the likelihood that organizations will fall victim to ransomware by mitigating and preventing the total damage caused by a malware attack
Secure and robust machine learning for healthcare: A survey
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
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