Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Cyber Security

This open access book constitutes the refereed proceedings of the 18th China Annual Conference on Cyber Security, CNCERT 2022, held in Beijing, China, in August 2022. The 17 papers presented were carefully reviewed and selected from 64 submissions. The papers are organized according to the following topical sections: ​​data security; anomaly detection; cryptocurrency; information security; vulnerabilities; mobile internet; threat intelligence; text recognition

Social media analytics with applications in disaster management and COVID-19 events

Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled methods that can provide appropriate tags or labels for further analysis for timely situation-awareness. In that direction, this work proposes machine learning models to identify the people who are seeking assistance using social media during a disaster and further demonstrates a prototype application that can collect and process Twitter data in real-time, identify the stranded people, and create rescue scheduling. In addition, to understand the people’s reactions to different trending topics, this work proposes a unique auxiliary feature-based deep learning model with adversarial sample generation for emotion detection using tweets related to COVID-19. This work also presents a custom Q&A-based RoBERTa model for extracting related phrases for emotions. Finally, with the aim of polarization detection, this research work proposes a deep learning pipeline for political ideology detection leveraging the tweet texts and the expressed emotions in the text. This work also studies and conducts the historical emotion and polarization analysis of the COVID-19 pandemic in the USA and several individual states using tweeter data --Abstract, page iv

Utility-Preserving Anonymization of Textual Documents

Cada dia els éssers humans afegim una gran quantitat de dades a Internet, tals com piulades, opinions, fotos i vídeos. Les organitzacions que recullen aquestes dades tan diverses n'extreuen informació per tal de millorar llurs serveis o bé per a propòsits comercials. Tanmateix, si les dades recollides contenen informació personal sensible, hom no les pot compartir amb tercers ni les pot publicar sense el consentiment o una protecció adequada dels subjectes de les dades. Els mecanismes de preservació de la privadesa forneixen maneres de sanejar les dades per tal que no revelin identitats o atributs confidencials. S'ha proposat una gran varietat de mecanismes per anonimitzar bases de dades estructurades amb atributs numèrics i categòrics; en canvi, la protecció automàtica de dades textuals no estructurades ha rebut molta menys atenció. En general, l'anonimització de dades textuals exigeix, primer, detectar trossos del text que poden revelar informació sensible i, després, emmascarar aquests trossos mitjançant supressió o generalització. En aquesta tesi fem servir diverses tecnologies per anonimitzar documents textuals. De primer, millorem les tècniques existents basades en etiquetatge de seqüències. Després, estenem aquestes tècniques per alinear-les millor amb el risc de revelació i amb les exigències de privadesa. Finalment, proposem un marc complet basat en models d'immersió de paraules que captura un concepte més ampli de protecció de dades i que forneix una protecció flexible guiada per les exigències de privadesa. També recorrem a les ontologies per preservar la utilitat del text emmascarat, és a dir, la seva semàntica i la seva llegibilitat. La nostra experimentació extensa i detallada mostra que els nostres mètodes superen els mètodes existents a l'hora de proporcionar anonimització robusta tot preservant raonablement la utilitat del text protegit.Cada día las personas añadimos una gran cantidad de datos a Internet, tales como tweets, opiniones, fotos y vídeos. Las organizaciones que recogen dichos datos los usan para extraer información para mejorar sus servicios o para propósitos comerciales. Sin embargo, si los datos recogidos contienen información personal sensible, no pueden compartirse ni publicarse sin el consentimiento o una protección adecuada de los sujetos de los datos. Los mecanismos de protección de la privacidad proporcionan maneras de sanear los datos de forma que no revelen identidades ni atributos confidenciales. Se ha propuesto una gran variedad de mecanismos para anonimizar bases de datos estructuradas con atributos numéricos y categóricos; en cambio, la protección automática de datos textuales no estructurados ha recibido mucha menos atención. En general, la anonimización de datos textuales requiere, primero, detectar trozos de texto que puedan revelar información sensible, para luego enmascarar dichos trozos mediante supresión o generalización. En este trabajo empleamos varias tecnologías para anonimizar documentos textuales. Primero mejoramos las técnicas existentes basadas en etiquetaje de secuencias. Posteriormente las extendmos para alinearlas mejor con la noción de riesgo de revelación y con los requisitos de privacidad. Finalmente, proponemos un marco completo basado en modelos de inmersión de palabras que captura una noción más amplia de protección de datos y ofrece protección flexible guiada por los requisitos de privacidad. También recurrimos a las ontologías para preservar la utilidad del texto enmascarado, es decir, su semantica y legibilidad. Nuestra experimentación extensa y detallada muestra que nuestros métodos superan a los existentes a la hora de proporcionar una anonimización más robusta al tiempo que se preserva razonablemente la utilidad del texto protegido.Every day, people post a significant amount of data on the Internet, such as tweets, reviews, photos, and videos. Organizations collecting these types of data use them to extract information in order to improve their services or for commercial purposes. Yet, if the collected data contain sensitive personal information, they cannot be shared with third parties or released publicly without consent or adequate protection of the data subjects. Privacy-preserving mechanisms provide ways to sanitize data so that identities and/or confidential attributes are not disclosed. A great variety of mechanisms have been proposed to anonymize structured databases with numerical and categorical attributes; however, automatically protecting unstructured textual data has received much less attention. In general, textual data anonymization requires, first, to detect pieces of text that may disclose sensitive information and, then, to mask those pieces via suppression or generalization. In this work, we leverage several technologies to anonymize textual documents. We first improve state-of-the-art techniques based on sequence labeling. After that, we extend them to make them more aligned with the notion of privacy risk and the privacy requirements. Finally, we propose a complete framework based on word embedding models that captures a broader notion of data protection and provides flexible protection driven by privacy requirements. We also leverage ontologies to preserve the utility of the masked text, that is, its semantics and readability. Extensive experimental results show that our methods outperform the state of the art by providing more robust anonymization while reasonably preserving the utility of the protected outcome

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI