الصفحة 9
الصفحة 9
Detection of intrusions and malware, and vulnerability assessment ; 3rd International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, Proceedings
This book constitutes the refereed proceedings of the Third International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2006, held in Berlin, Germany in July 2006.The 11 revised full papers presented were carefully reviewed and selected from 41 submissions.
Detection of intrusions and malware, and vulnerability assessment ; 17th International Conference, DIMVA 2020, Lisbon, Portugal, June 24–26, 2020, Proceedings
This book constitutes the proceedings of the 17th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2020, held in Lisbon, Portugal, in June 2020.* The 13 full papers presented in this volume were carefully reviewed and selected from 45 submissions. The contributions were organized in topical sections named: vulnerability discovery and analysis; attacks; web security; and detection and containment.
Detection and Signal Processing : Technical Realization
This comprehensive monograph deals with detectors, signal processors and related noise phenomena. Detailed quantitative analyses are developed in a consistent format for thermal detectors, vacuum detectors, semiconductor detectors and avalanche detectors, as well as their accompanying noise currents. For signal processing applocations, the monograph treats in detail the operational amplifier, signal averagers, waveform analyzers, correlation techniques and heterodyne detection. Several original extensions are reported, especially for correlation devices and heterodyne detection with noise rejection. In addition, results of analyses are illustrated with examples of operating systems and of applications in space communication and laser radar.
Detection and Disposal of Improvised Explosives
It including: Methods of detection of Improvised Explosives (IE). Methods of detection of Improvised explosives devices (IED). Disposal and safe handling of ID and IED. The treatment of detection methods may be divided in the following groups: Overview about the different methods; Trace- and vapor detection; Electromagnetic methods; Neutron methods; Laser techniques. Because of different definitions of Improvised Explosives the parti- pants of the workshop agreed after some discussions with the following definition: An Improvised Explosive (IE) can be any chemical compound or mixture capable of an explosive reaction. They are normally easily prepared by a knowledgeable layman under simple conditions. Components of IE are typically inorganic salts containing molecular bound oxygen like nitrates, chlorates or perchlorates etc. or organic compounds with nitro-, nitami- or nitrate-groups or peroxides. Admixtures of military or commercial explosive materials are also used. From the chemical point of view IE can be divided into the following types: Salts containing chemical groups with oxygen (like nitrates, chlorates or perchlorates etc.) in mixtures with combustible substances like carbon-hydrogen compounds.
Detection and Assessment of Dental Caries : A Clinical Guide
This book explains how to optimize clinical conditions for detection of the earliest visible signs of dental caries and how best to assess caries activity as a basis for effective management. The available evidence from the literature on detection criteria and methods is distilled and placed in a clinical context to facilitate implementation in clinical practice.
Detection and analysis of SARS Coronavirus : Advanced biosensors for pandemic viruses and related pathogens
The highly contagious SARS CoV-2 pathogen has challenged health systems around the world as they struggle to detect and monitor the spread of the pathogen. In Detection and Analysis of SARS Coronavirus: Advanced Biosensors for Pandemic Viruses and Related Pathogens expert chemists Chaudhery Mustansar Hussain and Sudheesh K. Shukla deliver a practical analysis of how contactless coronavirus detectors may be developed using existing biosensor technology.
Defense against Bioterror : Detection Technologies, Implementation Strategies and Commercial Opportunities ; Proceedings of the NATO Advanced Research Workshop on Defense against Bioterror: Detection Technologies, Implementation Strategies and Commercial Opportunities, held in Madrid, Spain from 8 to 11 April 2004
A critical assessment of state-of-the-art of emerging ("breakthrough") biosensor technologies that will allow for the rapid identification of biological threat agents in the environment and human population, Identification of directions for future research, and to promote close working relationships between scientists from different countries and with different professional experience. The volume is devoted to a comprehensive overview of the current state of biological weapons threat; challenges confronting biodetection technologies and systems; ongoing research and development; and, future requirements. Biosensor technologies including detection platforms, networked alarm-type biodetector systems, implementation strategies, electro-optical and electrochemical biosensors.
Deepfake detection = اكتشاف التزييف العميق
In the rapidly evolving era of artificial intelligence, addressing the escalating threats of deepfake technology becomes a necessity because of the increasing sophistication of AI algorithms in generating deceptive content, and since it threatens the integrity of information across diverse data. The main objective is to build a sophisticated AI-driven system to detect different types of deepfake in text, audio, and images. In English text deepfake detection, multiple pre-trained tokenizers have been used, but XLNET and BERT stand out with identifying objects outside the dataset with an accuracy of 0.9809 and both have been generalized & trained using LSTM. In Arabic text deepfake detection, Arabert has been trained using LSTM which led with an accuracy of 99.53% by generalizing the model. Both English and Arabic datasets have been generated to enhance the accuracy and effectiveness of the models. Audio deepfake detection has been generalized too, using Random Forest with an accuracy of 98.259%.
Deepfake detection
Recently, various techniques of manipulating the video content have become available to everyone – online, one can find free applications e.g., for face swapping in videos. Such universal accessibility carries a notable risk of flooding online content with false information, affecting not only the greats of this world, but also the whole societies, also the rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. It is therefore necessary to develop a verification tool that will help assess the authenticity of the videos posted on the internet. This project describes the approach of using artificial intelligence solutions to detect doctored videos.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deep Learning-Based Face Analytics
Provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
Deep Learning and its Applications
Presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction.
Deep fake detection
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is “deepfake”. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring.
Data science on the Google cloud platform : Implementing end-to-end real-time data pipelines : From ingest to machine learning
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. You'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines
Cytologic Detection of Urothelial Lesions
Cytologic Detection of Urothelial Lesions by Dorothy L. Rosenthal, MD and Stephen S. Raab, MD is the second volume in the Series. This volume will present a simple approach to dealing with cellular samples from the urinary tract.
Cyberspace security and defense : Research issues ; Proceedings of the NATO advanced research workshop on cyberspace security and defense : Research issues, Gdansk, Poland, from 6 to 9 September 2004.
The development of Internet, mobile communications, distributed computing, computer software and databases storing essential enterprise information has helped to conduct business and personal communication between individual people. and it has created many opportunities for abuse, fraud and expensive damage. This book is a selection of the best papers presented at the NATO Advanced Research Workshop dealing with the Subject of Cyberspace Security and Defense. The level of the individual contributions in the volume is advanced and suitable for senior and graduate students, researchers and technologists who wish to get some feeling of the state of the art in several sub-disciplines of Cyberspace security.
Cybersecurity of Digital Service Chains : Challenges, Methodologies, and Tools
This book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios.
Cybercrime in social media : Theory and solutions
Presents the important components for grasping the potential of social computing with an emphasis on concerns, challenges, and benefits of the social platform in depth. It discusses detection of social-cyber issues, including hate speech, cyberbullying, etc. using deep learning, natural language processing, etc.
Cyber Security ; 18th China Annual Conference, CNCERT 2021, Beijing, China, July 20–21, 2021, revised selected papers
This book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification.
