Page 12
Page 12
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.
Designing machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Designing & Implementing an IDS in SDN
Solving the problem of the various type of unknown attacks that are hitting not only companies but also high level business individuals, of course we know that there is no way to stop the attacks permanently but this project is attempting to reduce these attacks to the possible minimum where it can detect the attack and declare its type so that the hostile can at least know what is the type of attacks on him and what to do in response and build a higher security. This system is implemented using the SDN environment and IDS technology for monitoring the traffic on the network and for detecting the attack and its type. Also the SDN technology has a built-in OpenFlow protocol. To work in an OF environment, any device that wants to communicate to an SDN controller must support the OpenFlow protocol.
Dental Image Analysis for Disease Diagnosis
This book provides an overview of computational approaches to medical image examination and analysis in oral radiology utilizing dental radiograph to detect and diagnose dental caries in cases of decayed teeth. The book also presents a novel multiphase level set method for automatic segmentation of dental radiographs.
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.
Deepfake
The technology used to create such digital content has quickly become accessible to the masses, such as “DEEPFAKE.” Deep fakes refer to manipulated videos, or other digital representations produced by sophisticated artificial intelligence, that yields to synthesize a sequence of face images and voices of characters corresponding to their identities, such as voice tone, facial expression, with a good lip synchronization. Therefore, this study is about developing real-time video generation software, which generates a target video from a single input image. Several methods and algorithms have been applied to detect, analyze personalize facial expression, voice and natural head poses to present a life-like image instead of a low quality one.
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
Dangerous of Pharmaceutical Waste From Hospitals and Homes on Human and Environment
The occurrence of pharmaceuticals in environment originating from hospitals and household waste has received increased scientific attention during the last decades because more than 100 different drugs have been detected in the aquatic environment at concentrations from the nanogram (ng) to the μg/l range. This research talk about improper disposal of pharmaceutical waste, impacts of some drugs included in like metals, endocrine disruptors, and various compounds that are dangerous for aquatic and human lives. The safe disposal and management of pharmaceutical waste. The origin of this problem begin due to lack of awareness about this issues beside there is no training or courses for pharmacists and people work in medical departments on pharmaceutical waste management during their academic studies.in addition, this research also talk about how to reduce the amount of pharmaceuticals waste and environmentally friendly and cost-effective ways for handling this waste, beside increase the awareness to overcome this problem.
