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Relevant bibliographies by topics / Automated Threat Detection

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Academic literature on the topic 'Automated Threat Detection'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Automated Threat Detection"

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Shah, Anki, J. Kathyayani, D. Janani, E. Abhinav, and Rajashree Sutrawe. "CYBER THREAT DETECTION AND PROFILING USING AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–6. https://doi.org/10.55041/ijsrem.ncft025.

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The increasing reliance on the internet has escalated the frequency and sophistication of cyber threats, making timely identification and mitigation essential. This research presents an AI-powered framework for cyber threat detection and profiling using Natural Language Processing (NLP) and Machine Learning (ML) techniques. By utilizing Twitter as an Open Source Intelligence (OSINT) platform, the system collects real-time threat intelligence, classifies threats, and maps them to the MITRE ATT&CK framework to provide actionable insights. Key processes include data preprocessing, feature ext

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Datta, N. Bala Suresh. "Holistic Cyber Threat Intelligence System with Bert for Advanced Threat Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2662–64. https://doi.org/10.22214/ijraset.2025.68780.

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Cyber threats are evolving at an unprecedented rate, making traditional security measures insufficient in detecting and mitigating sophisticated attacks. This project introduces an AI-powered Cyber Threat Intelligence System that leverages machine learning, natural language processing (NLP), and automated threat analysis to enhance cybersecurity defenses. The system integrates data from multiple threat intelligence sources, such as OSINT feeds, security reports, and real-time network traffic, to identify, classify, and prioritize security threats. By employing a BERT-based NLP engine, the syst

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Desai, Aditya R. "A Review on Cybercrime Control through Behavioural Pattern Analysis Using a Comprehensive Database and Enhanced APIS." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3304–9. https://doi.org/10.22214/ijraset.2025.68894.

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Spam links have become a prevalent cybersecurity concern, leading to cyber threats such as phishing attacks, malware infections, ransomware propagation, identity theft, and financial fraud. Traditional detection methods, such as static blacklists and rule-based approaches, struggle to keep up with the rapid evolution of cyber threats[1]. This paper presents a comprehensive approach to spam link detection, integrating multiple threat intelligence sources such as Google Safe Browsing API, OpenPhish, PhishTank, and URLhaus, along with an intelligent behavioral pattern analysis module[3]. The prop

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Franciskumar, Gavit, and Mr Satish Kumar. "CipherScan: An Advanced Web Application Vulnerability Scanner for Enhanced Cybersecurity." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43804.

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The rapid development of cyber threats such as zero-day attacks and ransomware attacks has placed increasing pressure on proactive security practices. Traditional security products such as firewalls and antivirus software are not sufficient for efficient detection and neutralization of modern attacks. Intelligent, automated, and scalable security solutions need to be deployed by organizations to secure sensitive data and IT infrastructure. CipherSite has been engineered to integrate vulnerability scanning with real-time dynamic threat detection through artificial intelligence-powered analytics

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Ramasankar Molleti, Vinod Goje, Puneet Luthra, and Prathap Raghavan. "Automated threat detection and response using LLM agents." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 079–90. http://dx.doi.org/10.30574/wjarr.2024.24.2.3329.

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The increase of cyber threats from individual cases to a worldwide problem is the reason why people have shifted their cybersecurity perspectives. Basic defense processes, originally well understood and effective, fail to match modern attacks’ complexity and velocity. Taking into consideration LLMs as a recent addition to AI, this paper aims at discussing their application in integrating threat detection and response automation systems. As a result, LLMs, which have higher capabilities for natural language processing, deliver a revolutionary perspective regarding cybersecurity. Since LLM agent

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Ramasankar, Molleti, Goje Vinod, Luthra Puneet, and Raghavan Prathap. "Automated threat detection and response using LLM agents." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 079–90. https://doi.org/10.5281/zenodo.15067889.

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The increase of cyber threats from individual cases to a worldwide problem is the reason why people have shifted their cybersecurity perspectives. Basic defense processes, originally well understood and effective, fail to match modern attacks’ complexity and velocity. Taking into consideration LLMs as a recent addition to AI, this paper aims at discussing their application in integrating threat detection and response automation systems. As a result, LLMs, which have higher capabilities for natural language processing, deliver a revolutionary perspective regarding cybersecurity. Since LLM

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Mamayson, Emmanuel B., Mark Cherwin L. Alejandria, and Jose Gil K. Escalante. "Kalasag: An Integrated and Advanced Cybersecurity Tools for Cyber Threats Protection." International Journal of Research and Innovation in Applied Science X, no. VI (2025): 826–29. https://doi.org/10.51584/ijrias.2025.10060061.

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This paper presents Kalasag, an integrated and advanced cybersecurity tools for cyber threats protection developed to enhance threat detection and accelerate incident response within enterprise environments. Traditional security infrastructures often consist of siloed tools, resulting in operational inefficiencies and delayed threat mitigation. Kalasag unifies multiple cybersecurity functions— including network-based intrusion detection, host monitoring, threat intelligence, and automated incident response—within a single, coherent system. By leveraging real-time data correlation, anomaly dete

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P. Nisha, Mrs M. "Dark web Guardian: Real time threat Detection and Analysis." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03502.

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Abstract - The dark web represents a significant security threat due to its anonymity and the prevalence of illegal activities, including cybercrime, data breaches, and the sale of illicit goods. In response, real-time threat detection and analysis have become critical components of cybersecurity strategies. This paper introduces "Dark Web Guardian," a system designed to monitor and identify threats in real-time by analyzing dark web activities. The study focuses on the integration of advanced threat detection techniques, such as machine learning algorithms, behavioural analysis, and automated

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Emmanuel Joshua, John Do, and Rushil Patel. "AI-Driven Threat Intelligence System (AIDTIS): Leveraging large language models for automated threat research and detection development." International Journal of Science and Research Archive 14, no. 3 (2025): 270–85. https://doi.org/10.30574/ijsra.2025.14.3.0339.

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Cyber threats are evolving at an unprecedented pace, challenging organizations to stay ahead of sophisticated adversaries. Traditional threat research methods often require extensive manual effort, leading to delays in identifying and mitigating threats. This paper proposes an AI-Driven Threat Intelligence System (AIDTIS), a theoretical approach that leverages large language models (LLMs) to automate and enhance threat research and detection development. Our simulations and theoretical models suggest that such a system could significantly reduce threat research time, improve detection accuracy

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Ramaiah, CH, D. Adithya Charan, and R. Syam Akhil. "Secure automated threat detection and prevention (SATDP)." International Journal of Engineering & Technology 7, no. 2.20 (2018): 86. http://dx.doi.org/10.14419/ijet.v7i2.20.11760.

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Secure automated threat detection and prevention is the more effective procedure to reduce the workload of analyst by scanning the network, server functions& then informs the analyst if any suspicious activity is detected in the network. It monitors the system continuously and responds according to the threat environment. This response action varies from phase to phase. Here suspicious activities are detected by the help of an artificial intelligence which acts as a virtual analyst concurrently with network intrusion detection system to defend from the threat environment and taking appropr

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Dissertations / Theses on the topic "Automated Threat Detection"

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Heiberg, Einar Brandt. "Automated feature detection in multidimensional images /." Linköping : Univ, 2004. http://www.bibl.liu.se/liupubl/disp/disp2005/tek917s.pdf.

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Wang, Lu, and 王璐. "Three-dimensional model based human detection and tracking in crowded scenes." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46587421.

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Järrendahl, Hannes. "Automatic Detection of Anatomical Landmarks in Three-Dimensional MRI." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-130944.

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Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur

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Pokric, Boris. "Laser based machine vision for three-dimensional surface analysis." Thesis, Leeds Beckett University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297358.

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El, Mallahi Ahmed. "Automated 3D object analysis by digital holographic microscopy." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209489.

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The main objective of this thesis is the development of new processing techniques for digital holograms. The present work is part of the HoloFlow project that intends to integrate the DHM technology for the monitoring of water quality. Different tools for an automated analysis of digital holograms have been developed to detect, refocus and classify particles in continuous fluid flows. A detailed study of the refocusing criterion permits to determine its dependencies and to quantify its robustness. An automated detection procedure has been developed to determine automatically the 3D positions o

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Seyrafi, Aylar. "Developing Real Time Automatic Step Detection in the three dimensional Accelerometer Signal implemented on a Microcontroller System." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1183.

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Parkinson’s disease is associated with reduced coordination between respiration and locomotion. For the neurological rehabilitation research, it requires a long-time monitoring system, which enables the online analysis of the patient’s vegetative locomotor coordination. In this work a real time step detector using three-dimensional accelerometer signal for the patients with Parkinson‘s disease is developed. This step detector is a complement for a recently developed system included of intelligent, wirelessly communicating sensors. The system helps to focus on the scientific questions whether t

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Gresson, Régis. "Segmentation et reconstruction tridimensionnelle du foyer de microcalcifications mammaires." Vandoeuvre-les-Nancy, INPL, 1998. http://www.theses.fr/1998INPL108N.

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Les travaux présentés dans ce mémoire traitent de la segmentation et de la reconstruction tridimensionnelle du foyer de microcalcifications mammaires. Ces travaux sont scindés en deux parties. La première partie a été consacrée à la détermination d'une méthode automatique de détection et de segmentation des microcalcifications. Nous avons évalué et testé les principales méthodes de segmentation répertoriées dans la littérature (le filtrage de chan, la morphologie mathématique en niveaux de gris, la méthode par hystérésis, les méthodes fractales (classique et par les ondelettes) et la technique

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Berrios-Ayala, Mark. "Brave New World Reloaded: Advocating for Basic Constitutional Search Protections to Apply to Cell Phones from Eavesdropping and Tracking by Government and Corporate Entities." Honors in the Major Thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1547.

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Imagine a world where someone’s personal information is constantly compromised, where federal government entities AKA Big Brother always knows what anyone is Googling, who an individual is texting, and their emoticons on Twitter. Government entities have been doing this for years; they never cared if they were breaking the law or their moral compass of human dignity. Every day the Federal government blatantly siphons data with programs from the original ECHELON to the new series like PRISM and Xkeyscore so they can keep their tabs on issues that are none of their business; namely, the personal

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Carroll, Matthew. "Automatic detection of revealing cutting planes in three-dimensional datasets." 2006. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=450328&T=F.

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Huang, Sheng-fang, and 黃聖方. "Automatic Spiculate Detection for Breast Tumor Using Three-dimensional Ultrasound." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/25361974990535961749.

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碩士<br>國立中正大學<br>資訊工程研究所<br>89<br>Spiculate is a kind of stellate distortion cased by the intrusion of the breast cancer. Its existence is an important clue to characterize malignant tumors. So far there are several methods proposed to detect tumors with spiculation on mammography. The objective of this paper is to identify such features on ultrasonic volume data. In the proposed method, each horizontal flat slice in the volume data is successively extracted and then operated as an 2D ultrasound image by the detecting method respectively. First, in each horizontal slice the modified ROSE operat

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Books on the topic "Automated Threat Detection"

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Hagan, John, Bill McCarthy, and Daniel Herda. Chicago's Reckoning. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197627860.001.0001.

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Chicago is confronting a racial reckoning that we explain with an exclusion-containment theory of legal cynicism. Mayors RJ and RM Daley used public and private funds to exclude and contain South and West Side predominantly Black neighborhoods where Police Detective Jon Burge supervised torture of over 100 Black men. A 1982 case involved Andrew Wilson’s tortured confession to two police killings. This case coincided with RM Daley’s pursuit of White votes in an early and unsuccessful primary campaign for mayor. Suspicions about Daley’s connection to Wilson’s confession lasted throughout his car

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Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.

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The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretic

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Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. AK Peters, 2004.

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Book chapters on the topic "Automated Threat Detection"

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Niranjana, J., J. Mary Dhivya, and A. Balamurali. "Artificial Intelligence Powered Weapon Detection with Automated Threat Alert System." In Advances in Computer Science Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-718-2_22.

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van Verre, Wouter, Toykan Özdeǧer, Ananya Gupta, Frank J. W. Podd, and Anthony J. Peyton. "Threat Identification in Humanitarian Demining Using Machine Learning and Spectroscopic Metal Detection." In Intelligent Data Engineering and Automated Learning – IDEAL 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33607-3_58.

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Petrozziello, Alessio, and Ivan Jordanov. "Automated Deep Learning for Threat Detection in Luggage from X-Ray Images." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34029-2_32.

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Ferrara, Matteo, and Annalisa Franco. "Morph Creation and Vulnerability of Face Recognition Systems to Morphing." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_6.

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AbstractFace recognition in controlled environments is nowadays considered rather reliable, and very good accuracy levels can be achieved by state-of-the-art systems in controlled scenarios. However, even under these desirable conditions, digital image alterations can severely affect the recognition performance. In particular, several studies show that automatic face recognition systems are very sensitive to the so-called face morphing attack, where face images of two individuals are mixed to produce a new face image containing facial features of both subjects. Face morphing represents nowadays a big security threat particularly in the context of electronic identity documentsbecause it can be successfully exploited for criminal intents, for instance to fool Automated Border Control (ABC) systems thus overcoming security controls at the borders. This chapter will describe the face morphing process, in an overview ranging from the traditional techniques based on geometry warping and texture blending to the most recent and innovative approaches based on deep neural networks. Moreover, the sensitivity of state-of-the-art face recognition algorithms to the face morphing attack will be assessed using morphed images of different quality generated using various morphing methods to identify possible factors influencing the probability of success of the attack.

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Orazi, Gilles, Marianne Marot, Iheb Khelifi, Léa Robert, and Franck Le Gall. "Early Warning of Harmful Algal Blooms (HAB): A Low-Cost Integrated IoT Device with Spectrofluorometry and Automated Plankton Imaging." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78572-6_11.

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Abstract This paper presents the development of a prototype automated device designed to monitor sea water quality and provide early warnings of potential harmful algal bloom (HAB) outbreaks. HABs pose a significant threat to aquaculture operations and marine ecosystems due to their ability to cause mass mortalities of fish and shellfish. The device integrates a low-cost custom spectrofluorometer capable of measuring absorption and fluorescence spectra of liquid samples, and an automated plankton imager adapted from an open-source design. Key aimed sensing parameters include nutrients, chlorophyll, water temperature, and phytoplankton presence. The affordability of the device is targeted by using low-cost components and integrating the spectrofluorometer and plankton imager into a single unit driven by an embedded computer. Machine learning algorithms are employed for real-time anomaly detection from the multivariate sensor data streams to provide early alerts of potential HAB events. Initial results demonstrate the device’s ability to detect low concentrations of fluorescent dyes and phytoplankton, and the effectiveness of an adaptive anomaly detection approach on real aquarium data.

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Flizikowski, Adam, Mirosław Maszewski, Marcin Wachowiak, and Grzegorz Taberski. "Background Subtraction and Movement Detection Algorithms in Dynamic Environments – An Evaluation for Automated Threat Recognition Systems." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01622-1_26.

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Li, Xiaodong, Song Chai, Liwei Wang, and Hua Wang. "A Configurable and Automated Testing Framework for Hardware Trojan Detection in FPGAs." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_31.

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Abstract With the widespread adoption of FPGA, the inevitable rise of security threats, including Hardware Trojan, poses significant risks. Detecting such trojan is crucial as they can lead to severe consequences. Consequently, various detection methods have been proposed. However, conventional approaches typically require extensive datasets for model training. To tackle this issue, this paper presents a customizable Hardware Trojan detection framework. This framework is designed to generate ample samples for FPGA hardware security testing, ensuring robust detection capabilities.

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van Ditmarsch, Hans, Krisztina Fruzsa, Roman Kuznets, and Ulrich Schmid. "A Logic for Repair and State Recovery in Byzantine Fault-Tolerant Multi-agent Systems." In Automated Reasoning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63501-4_7.

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AbstractWe provide novel epistemic logical language and semantics for modeling and analysis of byzantine fault-tolerant multi-agent systems, with the intent of not only facilitating reasoning about the agents’ fault status but also supporting model updates for repair and state recovery. Besides the standard knowledge modalities, our logic provides additional agent-specific hope modalities capable of expressing that an agent is not faulty, and also dynamic modalities enabling change to the agents’ correctness status. These dynamic modalities are interpreted as model updates that come in three flavors: fully public, more private, and/or involving factual change. Tailored examples demonstrate the utility and flexibility of our logic for modeling a wide range of fault-detection, isolation, and recovery (FDIR) approaches in mission-critical distributed systems. By providing complete axiomatizations for all variants of our logic, we also create a foundation for building future verification tools for this important class of fault-tolerant applications.

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Rampershad, Yashay, Serestina Viriri, and Mandlenkosi Gwetu. "Automatic Baggage Threat Detection Using Deep Attention Networks." In Artificial Intelligence Research. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95070-5_11.

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Sales, Emerson, Omar Inverso, and Emilio Tuosto. "Accurate Static Data Race Detection for C." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71162-6_23.

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AbstractData races are a particular kind of subtle, unintended program behaviour arising from thread interference in shared-memory concurrency. In this paper, we propose an automated technique for static detection of data races in multi-threaded C programs with POSIX threads. The key element of our technique is a reduction to reachability. Our prototype implementation combines such reduction with context-bounded analysis. The approach proves competitive against state-of-the-art tools, finding new issues in the implementation of well-known lock-free data structures, and shows a considerably superior accuracy of analysis in the presence of complex shared-memory access patterns.

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Conference papers on the topic "Automated Threat Detection"

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Yulianto, Semi, and Gerard Nathaniel Cac Ngo. "Automated Threat Hunting, Detection, and Threat Actor Profiling Using TIRA." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10750987.

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P, Krishnaja Venkata Naga Sri Lasya, Munisaiteja Sharan Tupakula, and Naresh Sammeta. "Automated Anomaly Detection and Threat Classification in Network Traffic." In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2025. https://doi.org/10.1109/rmkmate64874.2025.11042463.

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Kamath, Abhay, Aditya Joshi, Aditya Sharma, Nikhil R. Shetty, and Lenish Pramiee. "Automated Threat Detection in the Dark Web: A Multi-Model NLP Approach." In 2025 13th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2025. https://doi.org/10.1109/isdfs65363.2025.11011956.

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Manikandan, K. P., Navaneeswar Reddy Onteddu, and Abdul Kalam Chilimi. "Next-Gen Malware Detection using AI AI-Powered Malware Threat Detection Automated Malware Classification Through Machine Learning." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985389.

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Venuturumilli, Munni, V. L. Manaswini Nune, M. Praveen, Helina Rajini Suresh, G. Elumalai, and P. Chitra. "Enhancing Cybersecurity: A Scalable System for Real-Time Threat Detection Using ML and Automated Response." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910406.

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Alo, Salam Omar, Abeer Salim Jamil, Mohammed Jabbar Hussein, Mohammed K. H. Al-Dulaimi, Sarmad Waleed Taha, and Anastasiia Khlaponina. "Automated Detection of Cybersecurity Threats Using Generative Adversarial Networks (GANs)." In 2024 36th Conference of Open Innovations Association (FRUCT). IEEE, 2024. http://dx.doi.org/10.23919/fruct64283.2024.10749874.

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Refaat, Seif Eldin, and Haitham Omran. "Machine Learning Assisted Automated Laser Profile Tracking." In Advanced Solid State Lasers. Optica Publishing Group, 2024. https://doi.org/10.1364/assl.2024.jtu2a.45.

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We investigate three convolutional neural networks machine-learning models —YOLO, Faster R-CNN, and DETR— in precision detection and tracking of laser profiles. Automated tracking is achieved by coupling trained models to nano-positioning system. We achieved detection mean-average-precision (mAP) of 99% with 20um spatial tracking resolution.

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Zanpure, Vibhas Mohan. "Improving Zero Trust Architecture with Machine Learning: Automatic Threat Detection using Network Traffic Analysis." In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2025. https://doi.org/10.1109/rmkmate64874.2025.11042837.

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Wang, Yunjia, Ziling Li, Tiejun Wu, Ishbel Duncan, and Qixin Lyu. "An Empirical Study: Automated Subdomain Takeover Threat Detection." In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). IEEE, 2021. http://dx.doi.org/10.1109/cybersa52016.2021.9478220.

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Stachowiak, Jeanne C., Erin E. Shugard, Pamela Caton, et al. "Automated Sample Preparation System for Rapid Biological Threat Detection." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80945.

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Rapid, automated sample preparation of bacterial cells and spores is required for threat analysis by remotely deployed chemical and biological warning systems. Sandia is designing, building, and testing an automated front-end sample preparation system based on miniature and microfluidic components, with the goal of concentrating bacterial species collected from the air, harvesting and solubilizing proteins from them, and delivering them to Sandia’s MicroChemLab capillary gel electrophoresis system1,2 for analysis (Fig. 1). Miniature, motorized valves and pumps control flow between system compo

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Reports on the topic "Automated Threat Detection"

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l

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Price, Donald. PR-403-123706-R02 2012 Right-of-Way Automated Monitoring Program- Machinery Threat Flight Test Results. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010865.

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In May 2012, the RAM Program completed a comprehensive flight test to evaluate the performance of airborne sensor technologies for detecting machinery threats on pipeline ROWs. The field trials were conducted on an operating pipeline Right Of Way (ROW) to provide realistic conditions for assessing the capabilities and limitations of automated systems that are currently available or are under development for identifying and reporting machinery encroachment. Four (4) separate vendors (American Aerospace Advisors, ATE Aero Surveillance, GeoEye, and New Era Technology) participated in the field tr

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Yoel, David, Tina Sicilia, Matthew Bogaart, and Jeremy Fernandes. PR-417-203902-R01 Remote Sensing and Leak Detection Platform That Can Deploy Multiple Sensor Types. Pipeline Research Council International, Inc. (PRCI), 2024. http://dx.doi.org/10.55274/r0000052.

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The PRCI Project #417-203902 - ROW 3-1-A Final Report is attached for Member review and comment. The report includes a summary of all work completed in all Tasks which include: - Catalog, taxonomy, and sample data set for the threats detected. - Benchmarks of the sensitivity, accuracy, reliability, and robustness of an automated multi sensor, multi-threat detection and near real-time reporting comparing performance on conventional aircraft and UAS. - Technical synopsis for operators on integrating automated near real-time aerial threat reports into pipeline performance and safety improvement p

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Yoel, David, Tina Sicilia, Matthew Bogaart, and Jeremy Fernandes. PR-417-203902-R02 Remote Sensing and Leak Detection Platform that Can Deploy Multiple Sensor Types. Pipeline Research Council International, Inc. (PRCI), 2024. http://dx.doi.org/10.55274/r0000055.

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American Aerospace Technologies, Inc. has conducted a pilot project for the Pipeline Research Council, International to evaluate the performance of an automated threat detection system onboard a medium alti-tude, long endurance unmanned aircraft capable of beyond visual line of sight flight that can be used in the pipeline industry for routine patrol and surveillance as a risk reduction solution. The unmanned aircraft system test program evaluation was conducted at Test Site(s) in Pendleton, Oregon, Woodbine, New Jersey, and San Joaquin Valley, California using American Aerospace sensors, unma

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Unknown, Author. L51630 In-Line Detection and Sizing of Stress Corrosion Cracks Using EMAT Ultrasonics. Pipeline Research Council International, Inc. (PRCI), 1990. http://dx.doi.org/10.55274/r0010616.

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The development of stress corrosion cracks (SCC) in buried gas pipelineshas posed a serious threat to pipeline integrity for many years. It can be reliably detected by magnetic particle techniques in the field or by laboratory studies using low frequency eddy currents. It is also possible to find and measure the depths of the cracks from the ID by careful scanning with an ultra-sonic angle beam probe but the transducer must be manipulated by a skilled operator. All of these approaches are not very satisfactory for in-line inspections because they are not suitable for covering the total area of

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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and

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Price, Donald. SM-403-148100-R01 Mineral Wells 2012 RAM Gas and Oil Leak Detection Field Study Results. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010851.

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In June 2012, the RAM Program conducted a comprehensive field study to evaluate the performance of current off the shelf sensor technologies for detecting gas and oil leaks on pipelines. This study had three key objectives: 1. Evaluate the capabilities of current technologies that are offered commercially for methane leak detection monitoring using standard pipeline patrol aircraft 2. Provide a test location for development of emerging technologies that are not yet commercially available for pipeline leak detection 3. Assess the feasibility of using airborne sensors to detect staged liquid oil

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Berney, Ernest, Andrew Ward, and Naveen Ganesh. First generation automated assessment of airfield damage using LiDAR point clouds. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40042.

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This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and pro

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c

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Kurimo-Beechuk, Elizabeth, Michael Byrne, and Wendy Wright. Landbird community monitoring at Fort Sumter National Monument: 2012 data summary. National Park Service, Natural Resource Stewardship and Science, 2016. https://doi.org/10.36967/2228028.

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Birds are an important component of park ecosystems. Due to their high body temperature, rapid metabolism, and high ecological position in most food webs, birds are also good indicators of the effects of local and regional ecosystem changes. Patterns in the community composition, distribution, and occurrence of breeding birds provide a metric for assessing ecological integrity and sustainability in southeastern U.S. ecosystems. Further, trends in these attributes in relation to activities occurring at Fort Sumter National Monument (e.g., management actions, natural disturbance, invasive-specie

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