58,823 research outputs found
Computational intelligence-enabled cybersecurity for the Internet of Things
The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies
Intelligence and Security Informatics
The book constitutes the proceedings of the First European Conference on Intelligence and Security Informatics, EuroISI 2008
Intelligence and security informatics (ISI) is a multidisciplinary field encompassing methodologies, models, algorithms, and advanced tools for intelligence analysis, homeland security, terrorism research as well as security-related public policies. These proceedings contain 25 original papers, out of 48 submissions received, related to the topics of intelligence and security informatics. These papers cover a broad range of fields such as: social network analysis, knowledge discovery, web-based intelligence and analysis, privacy protection, access control, digital rights management, malware and intrusion detection, surveillance, crisis management, and computational intelligence, among others.JRC.G.2-Support to external securit
Taking Computation to Data: Integrating Privacy-preserving AI techniques and Blockchain Allowing Secure Analysis of Sensitive Data on Premise
PhD thesis in Information technologyWith the advancement of artificial intelligence (AI), digital pathology has seen significant progress in recent years. However, the use of medical AI raises concerns about patient data privacy. The CLARIFY project is a research project funded under the European Union’s Marie Sklodowska-Curie Actions (MSCA) program. The primary objective of CLARIFY is to create a reliable, automated digital diagnostic platform that utilizes cloud-based data algorithms and artificial intelligence to enable interpretation and diagnosis of wholeslide-images (WSI) from any location, maximizing the advantages of AI-based digital pathology.
My research as an early stage researcher for the CLARIFY project centers on securing information systems using machine learning and access control techniques. To achieve this goal, I extensively researched privacy protection technologies such as federated learning, differential privacy, dataset distillation, and blockchain. These technologies have different priorities in terms of privacy, computational efficiency, and usability. Therefore, we designed a computing system that supports different levels of privacy security, based on the concept: taking computation to data. Our approach is based on two design principles. First, when external users need to access internal data, a robust access control mechanism must be established to limit unauthorized access. Second, it implies that raw data should be processed to ensure privacy and security. Specifically, we use smart contractbased access control and decentralized identity technology at the system security boundary to ensure the flexibility and immutability of verification. If the user’s raw data still cannot be directly accessed, we propose to use dataset distillation technology to filter out privacy, or use locally trained model as data agent. Our research focuses on improving the usability of these methods, and this thesis serves as a demonstration of current privacy-preserving and secure computing technologies
Security, Privacy, and Technology Development: The Impact on National Security
The evolution of modern communications and information technology sparked a revolution of unprecedented proportions, bringing about an explosion in terms of users and capabilities, as well as increasing demands for both security and privacy. To meet these security demands, new technologies are evolving that can in fact provide a secure and protected environment. At the same time, however, the technology-development path is being increasingly impacted by two other major dynamics: the legal environment and user expectations with respect to privacy. Within the past four years in particular, several major court decisions as well as the official release of documents and illicit “leaks” have drawn enormous attention to what privacy protections must be afforded to various types of data and communications. Users, increasingly aware of intrusions into their data and communications—ranging from intelligence services to hackers and criminals—are demanding greater levels of protection. While technological approaches to greater privacy are possible, they are not costfree— particularly in terms of the computational overhead and other constraints imposed on new systems
Security, Privacy, and Technology Development: The Impact on National Security
The evolution of modern communications and information technology sparked a revolution of unprecedented proportions, bringing about an explosion in terms of users and capabilities, as well as increasing demands for both security and privacy. To meet these security demands, new technologies are evolving that can in fact provide a secure and protected environment. At the same time, however, the technology-development path is being increasingly impacted by two other major dynamics: the legal environment and user expectations with respect to privacy. Within the past four years in particular, several major court decisions as well as the official release of documents and illicit “leaks” have drawn enormous attention to what privacy protections must be afforded to various types of data and communications. Users, increasingly aware of intrusions into their data and communications—ranging from intelligence services to hackers and criminals—are demanding greater levels of protection. While technological approaches to greater privacy are possible, they are not costfree— particularly in terms of the computational overhead and other constraints imposed on new systems
Intelligent Financial Fraud Detection Practices: An Investigation
Financial fraud is an issue with far reaching consequences in the finance
industry, government, corporate sectors, and for ordinary consumers. Increasing
dependence on new technologies such as cloud and mobile computing in recent
years has compounded the problem. Traditional methods of detection involve
extensive use of auditing, where a trained individual manually observes reports
or transactions in an attempt to discover fraudulent behaviour. This method is
not only time consuming, expensive and inaccurate, but in the age of big data
it is also impractical. Not surprisingly, financial institutions have turned to
automated processes using statistical and computational methods. This paper
presents a comprehensive investigation on financial fraud detection practices
using such data mining methods, with a particular focus on computational
intelligence-based techniques. Classification of the practices based on key
aspects such as detection algorithm used, fraud type investigated, and success
rate have been covered. Issues and challenges associated with the current
practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and
Privacy in Communication Networks (SecureComm 2014
-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI
In the wake of the burgeoning expansion of generative artificial intelligence
(AI) services, the computational demands inherent to these technologies
frequently necessitate cloud-powered computational offloading, particularly for
resource-constrained mobile devices. These services commonly employ prompts to
steer the generative process, and both the prompts and the resultant content,
such as text and images, may harbor privacy-sensitive or confidential
information, thereby elevating security and privacy risks. To mitigate these
concerns, we introduce -Split, a split computing framework to
facilitate computational offloading while simultaneously fortifying data
privacy against risks such as eavesdropping and unauthorized access. In
-Split, a generative model, usually a deep neural network (DNN), is
partitioned into three sub-models and distributed across the user's local
device and a cloud server: the input-side and output-side sub-models are
allocated to the local, while the intermediate, computationally-intensive
sub-model resides on the cloud server. This architecture ensures that only the
hidden layer outputs are transmitted, thereby preventing the external
transmission of privacy-sensitive raw input and output data. Given the
black-box nature of DNNs, estimating the original input or output from
intercepted hidden layer outputs poses a significant challenge for malicious
eavesdroppers. Moreover, -Split is orthogonal to traditional
encryption-based security mechanisms, offering enhanced security when deployed
in conjunction. We empirically validate the efficacy of the -Split
framework using Llama 2 and Stable Diffusion XL, representative large language
and diffusion models developed by Meta and Stability AI, respectively. Our
-Split implementation is publicly accessible at
https://github.com/nishio-laboratory/lambda_split.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
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Security techniques for intelligent spam sensing and anomaly detection in online social platforms
Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen
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