Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models

Deep neural networks (DNNs) have achieved tremendous success in many remote sensing (RS) applications, in which DNNs are vulnerable to adversarial perturbations. Unfortunately, current adversarial defense approaches in RS studies usually suffer from performance fluctuation and unnecessary re-training costs due to the need for prior knowledge of the adversarial perturbations among RS data. To circumvent these challenges, we propose a universal adversarial defense approach in RS imagery (UAD-RS) using pre-trained diffusion models to defend the common DNNs against multiple unknown adversarial attacks. Specifically, the generative diffusion models are first pre-trained on different RS datasets to learn generalized representations in various data domains. After that, a universal adversarial purification framework is developed using the forward and reverse process of the pre-trained diffusion models to purify the perturbations from adversarial samples. Furthermore, an adaptive noise level selection (ANLS) mechanism is built to capture the optimal noise level of the diffusion model that can achieve the best purification results closest to the clean samples according to their Frechet Inception Distance (FID) in deep feature space. As a result, only a single pre-trained diffusion model is needed for the universal purification of adversarial samples on each dataset, which significantly alleviates the re-training efforts and maintains high performance without prior knowledge of the adversarial perturbations. Experiments on four heterogeneous RS datasets regarding scene classification and semantic segmentation verify that UAD-RS outperforms state-of-the-art adversarial purification approaches with a universal defense against seven commonly existing adversarial perturbations. Codes and the pre-trained models are available online (https://github.com/EricYu97/UAD-RS).Comment: Added the GitHub link to the abstrac

Toward robust deep neural networks

Dans cette thèse, notre objectif est de développer des modèles d’apprentissage robustes et fiables mais précis, en particulier les Convolutional Neural Network (CNN), en présence des exemples anomalies, comme des exemples adversaires et d’échantillons hors distribution –Out-of-Distribution (OOD). Comme la première contribution, nous proposons d’estimer la confiance calibrée pour les exemples adversaires en encourageant la diversité dans un ensemble des CNNs. À cette fin, nous concevons un ensemble de spécialistes diversifiés avec un mécanisme de vote simple et efficace en termes de calcul pour prédire les exemples adversaires avec une faible confiance tout en maintenant la confiance prédicative des échantillons propres élevée. En présence de désaccord dans notre ensemble, nous prouvons qu’une borne supérieure de 0:5 + _0 peut être établie pour la confiance, conduisant à un seuil de détection global fixe de tau = 0; 5. Nous justifions analytiquement le rôle de la diversité dans notre ensemble sur l’atténuation du risque des exemples adversaires à la fois en boîte noire et en boîte blanche. Enfin, nous évaluons empiriquement la robustesse de notre ensemble aux attaques de la boîte noire et de la boîte blanche sur plusieurs données standards. La deuxième contribution vise à aborder la détection d’échantillons OOD à travers un modèle de bout en bout entraîné sur un ensemble OOD approprié. À cette fin, nous abordons la question centrale suivante : comment différencier des différents ensembles de données OOD disponibles par rapport à une tâche de distribution donnée pour sélectionner la plus appropriée, ce qui induit à son tour un modèle calibré avec un taux de détection des ensembles inaperçus de données OOD? Pour répondre à cette question, nous proposons de différencier les ensembles OOD par leur niveau de "protection" des sub-manifolds. Pour mesurer le niveau de protection, nous concevons ensuite trois nouvelles mesures efficaces en termes de calcul à l’aide d’un CNN vanille préformé. Dans une vaste série d’expériences sur les tâches de classification d’image et d’audio, nous démontrons empiriquement la capacité d’un CNN augmenté (A-CNN) et d’un CNN explicitement calibré pour détecter une portion significativement plus grande des exemples OOD. Fait intéressant, nous observons également qu’un tel A-CNN (nommé A-CNN) peut également détecter les adversaires exemples FGS en boîte noire avec des perturbations significatives. En tant que troisième contribution, nous étudions de plus près de la capacité de l’A-CNN sur la détection de types plus larges d’adversaires boîte noire (pas seulement ceux de type FGS). Pour augmenter la capacité d’A-CNN à détecter un plus grand nombre d’adversaires,nous augmentons l’ensemble d’entraînement OOD avec des échantillons interpolés inter-classes. Ensuite, nous démontrons que l’A-CNN, entraîné sur tous ces données, a un taux de détection cohérent sur tous les types des adversaires exemples invisibles. Alors que la entraînement d’un A-CNN sur des adversaires PGD ne conduit pas à un taux de détection stable sur tous les types d’adversaires, en particulier les types inaperçus. Nous évaluons également visuellement l’espace des fonctionnalités et les limites de décision dans l’espace d’entrée d’un CNN vanille et de son homologue augmenté en présence d’adversaires et de ceux qui sont propres. Par un A-CNN correctement formé, nous visons à faire un pas vers un modèle d’apprentissage debout en bout unifié et fiable avec de faibles taux de risque sur les échantillons propres et les échantillons inhabituels, par exemple, les échantillons adversaires et OOD. La dernière contribution est de présenter une application de A-CNN pour l’entraînement d’un détecteur d’objet robuste sur un ensemble de données partiellement étiquetées, en particulier un ensemble de données fusionné. La fusion de divers ensembles de données provenant de contextes similaires mais avec différents ensembles d’objets d’intérêt (OoI) est un moyen peu coûteux de créer un ensemble de données à grande échelle qui couvre un plus large spectre d’OoI. De plus, la fusion d’ensembles de données permet de réaliser un détecteur d’objet unifié, au lieu d’en avoir plusieurs séparés, ce qui entraîne une réduction des coûts de calcul et de temps. Cependant, la fusion d’ensembles de données, en particulier à partir d’un contexte similaire, entraîne de nombreuses instances d’étiquetées manquantes. Dans le but d’entraîner un détecteur d’objet robuste intégré sur un ensemble de données partiellement étiquetées mais à grande échelle, nous proposons un cadre d’entraînement auto-supervisé pour surmonter le problème des instances d’étiquettes manquantes dans les ensembles des données fusionnés. Notre cadre est évalué sur un ensemble de données fusionné avec un taux élevé d’étiquettes manquantes. Les résultats empiriques confirment la viabilité de nos pseudo-étiquettes générées pour améliorer les performances de YOLO, en tant que détecteur d’objet à la pointe de la technologie.In this thesis, our goal is to develop robust and reliable yet accurate learning models, particularly Convolutional Neural Networks (CNNs), in the presence of adversarial examples and Out-of-Distribution (OOD) samples. As the first contribution, we propose to predict adversarial instances with high uncertainty through encouraging diversity in an ensemble of CNNs. To this end, we devise an ensemble of diverse specialists along with a simple and computationally efficient voting mechanism to predict the adversarial examples with low confidence while keeping the predictive confidence of the clean samples high. In the presence of high entropy in our ensemble, we prove that the predictive confidence can be upper-bounded, leading to have a globally fixed threshold over the predictive confidence for identifying adversaries. We analytically justify the role of diversity in our ensemble on mitigating the risk of both black-box and white-box adversarial examples. Finally, we empirically assess the robustness of our ensemble to the black-box and the white-box attacks on several benchmark datasets.The second contribution aims to address the detection of OOD samples through an end-to-end model trained on an appropriate OOD set. To this end, we address the following central question: how to differentiate many available OOD sets w.r.t. a given in distribution task to select the most appropriate one, which in turn induces a model with a high detection rate of unseen OOD sets? To answer this question, we hypothesize that the “protection” level of in-distribution sub-manifolds by each OOD set can be a good possible property to differentiate OOD sets. To measure the protection level, we then design three novel, simple, and cost-effective metrics using a pre-trained vanilla CNN. In an extensive series of experiments on image and audio classification tasks, we empirically demonstrate the abilityof an Augmented-CNN (A-CNN) and an explicitly-calibrated CNN for detecting a significantly larger portion of unseen OOD samples, if they are trained on the most protective OOD set. Interestingly, we also observe that the A-CNN trained on the most protective OOD set (calledA-CNN) can also detect the black-box Fast Gradient Sign (FGS) adversarial examples. As the third contribution, we investigate more closely the capacity of the A-CNN on the detection of wider types of black-box adversaries. To increase the capability of A-CNN to detect a larger number of adversaries, we augment its OOD training set with some inter-class interpolated samples. Then, we demonstrate that the A-CNN trained on the most protective OOD set along with the interpolated samples has a consistent detection rate on all types of unseen adversarial examples. Where as training an A-CNN on Projected Gradient Descent (PGD) adversaries does not lead to a stable detection rate on all types of adversaries, particularly the unseen types. We also visually assess the feature space and the decision boundaries in the input space of a vanilla CNN and its augmented counterpart in the presence of adversaries and the clean ones. By a properly trained A-CNN, we aim to take a step toward a unified and reliable end-to-end learning model with small risk rates on both clean samples and the unusual ones, e.g. adversarial and OOD samples.The last contribution is to show a use-case of A-CNN for training a robust object detector on a partially-labeled dataset, particularly a merged dataset. Merging various datasets from similar contexts but with different sets of Object of Interest (OoI) is an inexpensive way to craft a large-scale dataset which covers a larger spectrum of OoIs. Moreover, merging datasets allows achieving a unified object detector, instead of having several separate ones, resultingin the reduction of computational and time costs. However, merging datasets, especially from a similar context, causes many missing-label instances. With the goal of training an integrated robust object detector on a partially-labeled but large-scale dataset, we propose a self-supervised training framework to overcome the issue of missing-label instances in the merged datasets. Our framework is evaluated on a merged dataset with a high missing-label rate. The empirical results confirm the viability of our generated pseudo-labels to enhance the performance of YOLO, as the current (to date) state-of-the-art object detector

Performance Evaluation of Network Anomaly Detection Systems

Nowadays, there is a huge and growing concern about security in information and communication technology (ICT) among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. Attacks, problems, and internal failures when not detected early may badly harm an entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection system based on the statistical method Principal Component Analysis (PCADS-AD). This approach creates a network profile called Digital Signature of Network Segment using Flow Analysis (DSNSF) that denotes the predicted normal behavior of a network traffic activity through historical data analysis. That digital signature is used as a threshold for volume anomaly detection to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP addresses and Ports, to provides the network administrator necessary information to solve them. Via evaluation techniques, addition of a different anomaly detection approach, and comparisons to other methods performed in this thesis using real network traffic data, results showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection accuracy on the detection schema. The observed results seek to contribute to the advance of the state of the art in methods and strategies for anomaly detection that aim to surpass some challenges that emerge from the constant growth in complexity, speed and size of today’s large scale networks, also providing high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia da informação e comunicação (TIC) entre a comunidade científica. Isto porque qualquer ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade em muitos domínios, como segurança nacional, armazenamento de dados privados, bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito surgiram ao longo dos anos. Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo de deteção de anomalias baseado em perfil utilizando o método estatístico Análise de Componentes Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e portas de origem e destino para fornecer ao administrador de rede as informações necessárias para resolvê-los. Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem de deteção distinta da proposta principal e comparações com outros métodos realizados nesta tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção. Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir para o avanço do estado da arte em métodos e estratégias de deteção de anomalias, visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade e tamanho das redes de grande porte da atualidade, proporcionando também alta performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para que possa ser aplicado a deteção em tempo real

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

Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures

Floods are the most recurrent, widespread and damaging natural disasters, and are ex-pected to become further devastating because of global warming. Blockage of cross-drainage hydraulic structures (e.g., culverts, bridges) by flood-borne debris is an influen-tial factor which usually results in reducing hydraulic capacity, diverting the flows, dam-aging structures and downstream scouring. Australia is among the countries adversely impacted by blockage issues (e.g., 1998 floods in Wollongong, 2007 floods in Newcas-tle). In this context, Wollongong City Council (WCC), under the Australian Rainfall and Runoff (ARR), investigated the impact of blockage on floods and proposed guidelines to consider blockage in the design process for the first time. However, existing WCC guide-lines are based on various assumptions (i.e., visual inspections as representative of hy-draulic behaviour, post-flood blockage as representative of peak floods, blockage remains constant during the whole flooding event), that are not supported by scientific research while also being criticised by hydraulic design engineers. This suggests the need to per-form detailed investigations of blockage from both visual and hydraulic perspectives, in order to develop quantifiable relationships and incorporate blockage into design guide-lines of hydraulic structures. However, because of the complex nature of blockage as a process and the lack of blockage-related data from actual floods, conventional numerical modelling-based approaches have not achieved much success. The research in this thesis applies artificial intelligence (AI) approaches to assess the blockage at cross-drainage hydraulic structures, motivated by recent success achieved by AI in addressing complex real-world problems (e.g., scour depth estimation and flood inundation monitoring). The research has been carried out in three phases: (a) litera-ture review, (b) hydraulic blockage assessment, and (c) visual blockage assessment. The first phase investigates the use of computer vision in the flood management domain and provides context for blockage. The second phase investigates hydraulic blockage using lab scale experiments and the implementation of multiple machine learning approaches on datasets collected from lab experiments (i.e., Hydraulics-Lab Dataset (HD), Visual Hydraulics-Lab Dataset (VHD)). The artificial neural network (ANN) and end-to-end deep learning approaches reported top performers among the implemented approaches and demonstrated the potential of learning-based approaches in addressing blockage is-sues. The third phase assesses visual blockage at culverts using deep learning classifi-cation, detection and segmentation approaches for two types of visual assessments (i.e., blockage status classification, percentage visual blockage estimation). Firstly, a range of existing convolutional neural network (CNN) image classification models are imple-mented and compared using visual datasets (i.e., Images of Culvert Openings and Block-age (ICOB), VHD, Synthetic Images of Culverts (SIC)), with the aim to automate the process of manual visual blockage classification of culverts. The Neural Architecture Search Network (NASNet) model achieved best classification results among those im-plemented. Furthermore, the study identified background noise and simplified labelling criteria as two contributing factors in degraded performance of existing CNN models for blockage classification. To address the background clutter issue, a detection-classification pipeline is proposed and achieved improved visual blockage classification performance. The proposed pipeline has been deployed using edge computing hardware for blockage monitoring of actual culverts. The role of synthetic data (i.e., SIC) on the performance of culvert opening detection is also investigated. Secondly, an automated segmentation-classification deep learning pipeline is proposed to estimate the percentage of visual blockage at circular culverts to better prioritise culvert maintenance. The AI solutions proposed in this thesis are integrated into a blockage assessment framework, designed to be deployed through edge computing to monitor, record and assess blockage at cross-drainage hydraulic structures

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

Enhancing Computer Network Security through Improved Outlier Detection for Data Streams

V několika posledních letech se metody strojového učení (zvláště ty zabývající se detekcí odlehlých hodnot - OD) v oblasti kyberbezpečnosti opíraly o zjišťování anomálií síťového provozu spočívajících v nových schématech útoků. Detekce anomálií v počítačových sítích reálného světa se ale stala stále obtížnější kvůli trvalému nárůstu vysoce objemných, rychlých a dimenzionálních průběžně přicházejících dat (SD), pro která nejsou k dispozici obecně uznané a pravdivé informace o anomalitě. Účinná detekční schémata pro vestavěná síťová zařízení musejí být rychlá a paměťově nenáročná a musejí být schopna se potýkat se změnami konceptu, když se vyskytnou. Cílem této disertace je zlepšit bezpečnost počítačových sítí zesílenou detekcí odlehlých hodnot v datových proudech, obzvláště SD, a dosáhnout kyberodolnosti, která zahrnuje jak detekci a analýzu, tak reakci na bezpečnostní incidenty jako jsou např. nové zlovolné aktivity. Za tímto účelem jsou v práci navrženy čtyři hlavní příspěvky, jež byly publikovány nebo se nacházejí v recenzním řízení časopisů. Zaprvé, mezera ve volbě vlastností (FS) bez učitele pro zlepšování již hotových metod OD v datových tocích byla zaplněna navržením volby vlastností bez učitele pro detekci odlehlých průběžně přicházejících dat označované jako UFSSOD. Následně odvozujeme generický koncept, který ukazuje dva aplikační scénáře UFSSOD ve spojení s online algoritmy OD. Rozsáhlé experimenty ukázaly, že UFSSOD coby algoritmus schopný online zpracování vykazuje srovnatelné výsledky jako konkurenční metoda upravená pro OD. Zadruhé představujeme nový aplikační rámec nazvaný izolovaný les založený na počítání výkonu (PCB-iForest), jenž je obecně schopen využít jakoukoliv online OD metodu založenou na množinách dat tak, aby fungovala na SD. Do tohoto algoritmu integrujeme dvě varianty založené na klasickém izolovaném lese. Rozsáhlé experimenty provedené na 23 multidisciplinárních datových sadách týkajících se bezpečnostní problematiky reálného světa ukázaly, že PCB-iForest jasně překonává už zavedené konkurenční metody v 61 % případů a dokonce dosahuje ještě slibnějších výsledků co do vyváženosti mezi výpočetními náklady na klasifikaci a její úspěšností. Zatřetí zavádíme nový pracovní rámec nazvaný detekce odlehlých hodnot a rozpoznávání schémat útoku proudovým způsobem (SOAAPR), jenž je na rozdíl od současných metod schopen zpracovat výstup z různých online OD metod bez učitele proudovým způsobem, aby získal informace o nových schématech útoku. Ze seshlukované množiny korelovaných poplachů jsou metodou SOAAPR vypočítány tři různé soukromí zachovávající podpisy podobné otiskům prstů, které charakterizují a reprezentují potenciální scénáře útoku s ohledem na jejich komunikační vztahy, projevy ve vlastnostech dat a chování v čase. Evaluace na dvou oblíbených datových sadách odhalila, že SOAAPR může soupeřit s konkurenční offline metodou ve schopnosti korelace poplachů a významně ji překonává z hlediska výpočetního času . Navíc se všechny tři typy podpisů ve většině případů zdají spolehlivě charakterizovat scénáře útoků tím, že podobné seskupují k sobě. Začtvrté představujeme algoritmus nepárového kódu autentizace zpráv (Uncoupled MAC), který propojuje oblasti kryptografického zabezpečení a detekce vniknutí (IDS) pro síťovou bezpečnost. Zabezpečuje síťovou komunikaci (autenticitu a integritu) kryptografickým schématem s podporou druhé vrstvy kódy autentizace zpráv, ale také jako vedlejší efekt poskytuje funkcionalitu IDS tak, že vyvolává poplach na základě porušení hodnot nepárového MACu. Díky novému samoregulačnímu rozšíření algoritmus adaptuje svoje vzorkovací parametry na základě zjištění škodlivých aktivit. Evaluace ve virtuálním prostředí jasně ukazuje, že schopnost detekce se za běhu zvyšuje pro různé scénáře útoku. Ty zahrnují dokonce i situace, kdy se inteligentní útočníci snaží využít slabá místa vzorkování.ObhájenoOver the past couple of years, machine learning methods - especially the Outlier Detection (OD) ones - have become anchored to the cyber security field to detect network-based anomalies rooted in novel attack patterns. Due to the steady increase of high-volume, high-speed and high-dimensional Streaming Data (SD), for which ground truth information is not available, detecting anomalies in real-world computer networks has become a more and more challenging task. Efficient detection schemes applied to networked, embedded devices need to be fast and memory-constrained, and must be capable of dealing with concept drifts when they occur. The aim of this thesis is to enhance computer network security through improved OD for data streams, in particular SD, to achieve cyber resilience, which ranges from the detection, over the analysis of security-relevant incidents, e.g., novel malicious activity, to the reaction to them. Therefore, four major contributions are proposed, which have been published or are submitted journal articles. First, a research gap in unsupervised Feature Selection (FS) for the improvement of off-the-shell OD methods in data streams is filled by proposing Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD. A generic concept is retrieved that shows two application scenarios of UFSSOD in conjunction with online OD algorithms. Extensive experiments have shown that UFSSOD, as an online-capable algorithm, achieves comparable results with a competitor trimmed for OD. Second, a novel unsupervised online OD framework called Performance Counter-Based iForest (PCB-iForest) is being introduced, which generalized, is able to incorporate any ensemble-based online OD method to function on SD. Two variants based on classic iForest are integrated. Extensive experiments, performed on 23 different multi-disciplinary and security-related real-world data sets, revealed that PCB-iForest clearly outperformed state-of-the-art competitors in 61 % of cases and even achieved more promising results in terms of the tradeoff between classification and computational costs. Third, a framework called Streaming Outlier Analysis and Attack Pattern Recognition, denoted as SOAAPR is being introduced that, in contrast to the state-of-the-art, is able to process the output of various online unsupervised OD methods in a streaming fashion to extract information about novel attack patterns. Three different privacy-preserving, fingerprint-like signatures are computed from the clustered set of correlated alerts by SOAAPR, which characterize and represent the potential attack scenarios with respect to their communication relations, their manifestation in the data's features and their temporal behavior. The evaluation on two popular data sets shows that SOAAPR can compete with an offline competitor in terms of alert correlation and outperforms it significantly in terms of processing time. Moreover, in most cases all three types of signatures seem to reliably characterize attack scenarios to the effect that similar ones are grouped together. Fourth, an Uncoupled Message Authentication Code algorithm - Uncoupled MAC - is presented which builds a bridge between cryptographic protection and Intrusion Detection Systems (IDSs) for network security. It secures network communication (authenticity and integrity) through a cryptographic scheme with layer-2 support via uncoupled message authentication codes but, as a side effect, also provides IDS-functionality producing alarms based on the violation of Uncoupled MAC values. Through a novel self-regulation extension, the algorithm adapts its sampling parameters based on the detection of malicious actions on SD. The evaluation in a virtualized environment clearly shows that the detection rate increases over runtime for different attack scenarios. Those even cover scenarios in which intelligent attackers try to exploit the downsides of sampling

Assessing the Role of Critical Value Factors (CVFs) on Users’ Resistance of Urban Search and Rescue Robotics

Natural and manmade disasters have brought urban search and rescue (USAR) robots to the technology forefront as a means of providing additional support for search and rescue workers. The loss of life among victims and rescue workers necessitates the need for a wider acceptance of this assistive technology. Disasters, such as hurricane Harvey in 2017, hurricane Sandy in 2012, the 2012 United States tornadoes that devastated 17 states, the 2011 Australian floods, the 2011 Japan and 2010 Haiti earthquakes, the 2010 West Virginia coal mine explosions, the 2009 Typhoon caused mudslides in Taiwan, the 2001 Collapse of the World Trade Center, the 2005 Hurricane Katrina, the 1995 Oklahoma City bombing, and the 1995 Kobe Japan earthquake all benefited from the use of USAR. While there has been a push for use of USAR for disaster, user resistance to such technology is still significantly understudied. This study applied a mixed quantitative and qualitative approach to identify important system characteristics and critical value factors (CVFs) that contribute to team members’ resistance to use such technology. The populations for this study included 2,500 USAR team members from the Houston Professional Fire Fighters Association (HPFFA), and the expected sample size of approximately 250 respondents. The main goal of this quantitative study was to examine system characteristics and CVFs that contribute to USAR team members’ resistance to use such technology. System characteristics and CVFs are associated with USAR. Furthermore, the study utilized multivariate linear regression (MLR) and multivariate analysis of covariance (ANCOVA) to determine if, and to what extent, CVFs and computer self-efficacy (CSE) interact to influence USAR team members’ resistance to use such technology. This quantitative study will test for significant differences on CVF’s, CSE, and resistance to use such technology based on age, gender, prior experience with USAR events, years of USAR experience, and organizational role. The contribution of this study was to reduce USAR team members’ resistance to use such technology in an effort minimize risk to USAR team members while maintaining their lifesaving capability