Relevant bibliographies by topics / Attack mitigation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Attack mitigation'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Attack mitigation"

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Bentley, Mark, Alec Stephenson, Peter Toscas, and Zili Zhu. "A Multivariate Model to Quantify and Mitigate Cybersecurity Risk." Risks 8, no. 2 (2020): 61. http://dx.doi.org/10.3390/risks8020061.

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The cost of cybersecurity incidents is large and growing. However, conventional methods for measuring loss and choosing mitigation strategies use simplifying assumptions and are often not supported by cyber attack data. In this paper, we present a multivariate model for different, dependent types of attack and the effect of mitigation strategies on those attacks. Utilising collected cyber attack data and assumptions on mitigation approaches, we look at an example of using the model to optimise the choice of mitigations. We find that the optimal choice of mitigations will depend on the goal—to prevent extreme damages or damage on average. Numerical experiments suggest the dependence aspect is important and can alter final risk estimates by as much as 30%. The methodology can be used to quantify the cost of cyber attacks and support decision making on the choice of optimal mitigation strategies.
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Samer, Moein1, Aaron Gulliver1 T., Gebali1 Fayez, and Alkandari2 Abdulrahman. "Hardware Attack Mitigation Techniques Analysis." International Journal on Cryptography and Information Security (IJCIS) 7, no. 1 (2017): 1–20. https://doi.org/10.5281/zenodo.1186619.

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ABSTRACT The goal of a hardware attack is to physically access a digital system to obtain secret information or modify the system behavior. These attacks can be classified as covert or overt based on the awareness of the attack. Each hardware attack has capabilities as well as objectives. Some employ hardware trojans, which are inserted during, manufacture, while others monitor system emissions. Once a hardware attack has been identified, mitigation techniques should be employed to protect the system. There are now a wide variety of techniques, which can be used against hardware attacks. In this paper, a comprehensive survey of hardware attack mitigation techniques is presented. These techniques are matched to the hardware attacks and attack criteria they can counter, which helps security personnel choose appropriate mitigation techniques to protect their systems against hardware attacks. An example is presented to illustrate the choice of appropriate countermeasures.
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Kadiri, Padmaja, and Seshadri Ravala. "Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks." International Journal of e-Collaboration 17, no. 4 (2021): 75–88. http://dx.doi.org/10.4018/ijec.2021100106.

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Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.
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Dhadhal, Hema, and Dr Paresh Kotak. "Analytical study of effects of DDoS mitigation methods." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 5 (2023): 728–31. http://dx.doi.org/10.54660/.ijmrge.2023.4.5.728-731.

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Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructure and services. Software Defined Networks (SDNs) have emerged as a promising technology to enhance network flexibility and adaptability. This paper explores various DDoS attack mitigation approaches within the context of SDNs, with a focus on their effectiveness, limitations, and key considerations. The study begins by providing an overview of DDoS attacks and their evolving complexities. It then delves into the fundamental principles of SDNs and their potential advantages in combating DDoS attacks. Various mitigation techniques are analyzed, including rate limiting, flow diversion, and traffic anomaly detection, and their applicability in an SDN environment is discussed Our research takes a multi-dimensional approach, evaluating the performance of mitigation methods in terms of attack detection, response time, false positives, and the impact on legitimate traffic. Additionally, the study considers the adaptability of these methods to emerging threats, such as IoT-based attacks and zero-day vulnerabilities. Furthermore, we discuss the trade-offs and challenges associated with the implementation of these methods, including their impact on user experience, resource utilization, and operational overhead. The results of this comparative analysis aim to provide valuable insights for organizations and network security professionals in selecting appropriate DDoS mitigation strategies. This paper provides insights into the evolving landscape of DDoS attack mitigation in Software Defined Networks, emphasizing the need for a proactive and dynamic defence strategy to protect network infrastructure and ensure uninterrupted service availability. This study serves as a valuable resource for mitigating the risks and consequences of DDoS attacks in today's interconnected and digitally-dependent world.
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Dhadhal, Hema, and Paresh Kotak. "Leveraging datasets for effective mitigation of DDoS attacks in software-defined networking: significance and challenges." Radioelectronic and Computer Systems 2024, no. 2 (2024): 136–46. http://dx.doi.org/10.32620/reks.2024.2.11.

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Software-Defined Networking (SDN) has emerged as a transformative paradigm for network management, offering centralized control and programmability. However, with the proliferation of Distributed Denial of Service (DDoS) attacks that pose significant threats to network infrastructures, effective mitigation strategies are needed. The subject matter of this study is to explore the importance of datasets in the mitigation of DDoS attacks in SDN environments. The paper discusses the significance of datasets for training machine learning models, evaluating detection mechanisms, and enhancing the resilience of SDN-based defense systems. Goal of the paper is to assist researchers in effectively selecting and usage of datasets for DDoS mitigation in SDN, thereby maximizing benefits and overcoming challenges involved in dataset selection. This paper outlines the challenges associated with dataset collection, labeling, and management, along with potential solutions to address these challenges. Effective detection and mitigation of DDoS attacks in SDN require robust datasets that capture the diverse and evolving nature of attack scenarios. Characterization of tasks for each section is as follows: Importance of datasets in DDoS attack mitigation in SDN, challenges in dataset utilization in DDoS mitigation in SDN, Guidelines for dataset selection, comparison of datasets used and their results and different dataset usage according to the need. Methodology involves collecting results in tabular form based on prior research to analyze the characteristics of existing datasets, techniques for dataset augmentation and enhancement, and evaluating the effectiveness of different datasets in detecting and mitigating DDoS attacks through comprehensive experimentation. Results of our findings indicate that effective detection and mitigation of DDoS attacks in SDN require robust datasets that capture the diverse and evolving nature of attack scenarios. Our findings provide valuable insights into the importance of datasets in enhancing the resilience of SDN infrastructures against DDoS attacks. In conclusion, our findings provide valuable insights into the importance of datasets in enhancing the resilience of SDN infrastructures against DDoS attacks and highlight the need for further research in this critical area. Thorough guidelines for dataset selection and impacts of different datasets used in recent studies, provide research challenges and future directions in this area.
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Venkatachalam, Nallarasan, and Kottilingam Kottursamy. "Jamming Attack Mitigation in CR-IoT Using Game Theory." Revue d'Intelligence Artificielle 36, no. 4 (2022): 615–20. http://dx.doi.org/10.18280/ria.360414.

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Jamming attack is Cognitive radio Internet of thing network disables the spectrum sharing and reduces the spectrum utilization .detection and mitigation of such attacks is the main component in realizing cognitive radio-based spectrum sharing in cognitive radio-based internet of thing network this work proposes a game theory-based jamming attack mitigation strategy. The problem of jamming attack mitigation is modeled as a zero-sum game and solved by finding Nash equilibrium. The cognitive node which tries to share the spectrum pays the zero-sum game with the jamming attacker and selects the best strategy of selecting the best frequency without getting into the jamming attack. The result of the proposed mechanism proves that the gaming mechanism can tackle the jamming attack.
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Gupta, Deepak, Ankita Sharma, Rashmi Pandey, Deshdeepak Shrivastava, Archana Acharya, and Madhukar Dubey. "ENHANCING COLLABORATIVE MITIGATION OF VOLUMETRIC DDOS ATTACKS AND FAILURE IN MULTI-SDN NETWORKS." ICTACT Journal on Communication Technology 15, no. 2 (2024): 3165–72. http://dx.doi.org/10.21917/ijct.2024.0471.

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Multi-SDN (Software-Defined Networking) architectures offer enhanced flexibility and control over network traffic, which is essential in mitigating Distributed Denial of Service (DDoS) attacks. Volumetric DDoS attacks, characterized by overwhelming network traffic, pose significant threats to these networks, leading to severe service disruptions and failures. Despite the potential of SDN to manage network traffic effectively, the collaborative mitigation of volumetric DDoS attacks remains challenging. Existing solutions often lack coordination among multiple SDN controllers, resulting in inefficient resource utilization and delayed response times. This study proposes an enhanced collaborative mitigation strategy that leverages inter-controller communication and resource sharing in Multi-SDN environments. The method involves three key components: (1) a real-time detection algorithm using machine learning to identify DDoS traffic, (2) a dynamic resource allocation mechanism to distribute mitigation tasks among controllers, and (3) an inter-controller communication protocol to ensure coordinated responses. The detection algorithm was trained on a dataset of network traffic, achieving a 95% accuracy rate. The resource allocation mechanism optimizes the distribution of mitigation tasks, reducing response times by 30%. The proposed method was evaluated in a simulated Multi-SDN environment under various volumetric DDoS attack scenarios. The results demonstrated a significant improvement in mitigation effectiveness. The attack detection accuracy reached 94.5%, and the coordinated mitigation reduced average response times by 28%. Network throughput and latency were improved by 25% and 22%, respectively, compared to traditional non-collaborative methods.
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Shankari, M. Uma, and Dr S. Rethinavalli. "Dynamic Duplication Attack Detection and Mitigation System (DDADMS)." Indian Journal Of Science And Technology 18, no. 18 (2025): 1452–64. https://doi.org/10.17485/ijst/v18i18.535.

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Objectives: To propose a suitable approach to detect, analyse and prevent duplication attacks in cloud computing for effective and secure data management. DDADMS strengthens cloud security and dynamically manages resource usage with real-time de-duplication, machine learning profiling and LSTM prediction. Methods: This study has proposed Dynamic Duplication Attack Detection and Mitigation System (DDADMS) which uses machine learning, hashing, and pattern matching to detect duplicate data when file names and also to change metadata. It develops normal duplication profiles from end-to-end software analysis on client hosts and cloud hosts. Real-time de-duplication is combined with LSTM networks for duplication attack prediction using past patterns for proactive defence mechanisms against advanced threats. Findings: DDADMS was compared against three others Signature-Based Detection and Behavioural Analysis System, Traditional Hashing Technique, and Behavioural Analysis System and outshined them far better. Traditional Hashing Technique can detect duplication attacks 80% of the time but at a comparatively higher false positive rate of 10.5%, water down its differentiation between malicious and benign duplication. Signature-Based Detection increases detection to 85% but at a loss of 8.0% false positive. Behavioural Analysis System identifies 88.5% at a false positive rate of 6.5%, higher than the first two but lower than DDADMS. A mean de-duplication time winner for the Behavioural Analysis System, DDADMS has a higher performance in all three methods in overall detection accuracy, optimality of resources used, and threat blocking. By projecting LSTM-trained models onto huge history datasets, DDADMS forcefully avoids and counteracts duplicate attacks, curbing wastage of storage and redundancy of data by a huge extent. Thus, DDADMS strengthens cloud security and dynamically manages resource usage with real-time de-duplication, machine learning profiling and LSTM prediction. Novelty: DDADMS brings forth a revolutionary innovation in cloud security with real-time duplicate elimination and predictive analysis integration. With other available mechanisms reacting to duplication attacks only after their occurrence, DDADMS identifies duplicate data the instant it is present and deletes it, achieving maximum storage optimization with superior detection rates. The innovative approach provides adaptive and dynamic defence strategy against dynamic duplication attacks, making DDADMS a very powerful cloud security management solution. Keywords: Machine Learning, Attack Detection and Mitigation, Cloud Security, Real-Time De-Duplication, Machine Learning Profiling, LSTM Prediction, Resource Optimization
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Akanji, Oluwatobi Shadrach, Opeyemi Aderiike Abisoye, and Mohammed Awwal Iliyasu. "MITIGATING SLOW HYPERTEXT TRANSFER PROTOCOL DISTRIBUTED DENIAL OF SERVICE ATTACKS IN SOFTWARE DEFINED NETWORKS." Journal of Information and Communication Technology 20, Number 3 (2021): 277–304. http://dx.doi.org/10.32890/jict2021.20.3.1.

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Distributed Denial of Service (DDoS) attacks has been one of the persistent forms of attacks on information technology infrastructure connected to public networks due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS which overwhelms the target with a large number of request packets. However, compared to slow DDoS, limited number of research has been executed on mitigating slow DDoS. Attackers have resorted to slow DDoS because it mimics the behaviour of a slow legitimate client thereby causing service unavailability. This paper provides the scholarly community with an approach to boosting service availability in web servers under slow Hypertext Transfer Protocol (HTTP) DDoS attacks through attack detection using Genetic Algorithm and Support Vector Machine which facilitates attack mitigation in a Software-Defined Networking (SDN) environment simulated in GNS3. Genetic algorithm was used to select the Netflow features which indicates the presence of an attack and also determine the appropriate regularization parameter, C, and gamma parameter for the Support Vector Machine classifier. Results obtained showed that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and a false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. Also, the algorithm for subsequent implementation of the selective adaptive bubble burst mitigation mechanism was presented. This study contributes to the ongoing research in detecting and mitigating slow HTTP DDoS attacks with emphasis on the use of machine learning classification and meta-heuristic algorithms.
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Zalte, S. S., K. Kamat R., and R. Ghorpade V. "Mitigation of DDoS Attack in MANET." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 410–13. https://doi.org/10.35940/ijeat.E95400.089620.

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This paper discusses an improved mechanism to mitigate the DDoS attack in Mobile Ad-hoc Network (MANET) without compromising the packet delivery ratio. MANET inherently has geographically sparse mobile nodes more vulnerable to various attackers more radically the DDoS type. Moreover it emanates open medium, absence of centralized authority, lack of clear line of defense that leads to more vulnerability towards secure environment. Since there are no hindrances for non-legitimate nodes, which allows them to freely enter and exit from the network at anytime, various active attacks like black hole, gray hole, DDoS found to jeopardize the network. The DDoS attack discussed predominantly in to present paper, penetrates the network thereby constraining the resources like memory, battery and bandwidth by frequently transmitting fake packets after regular time span thereby starving the computing resources. This paper demonstrates the feasibility of defense mechanism to the DDOS attack in MANET.
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Dissertations / Theses on the topic "Attack mitigation"

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Giannini, Beatrice. "Attack and Mitigation Modelling for Software-Defined Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19817/.

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Le Software-Defined Networks nascono per superare i limiti delle reti tradizionali, quindi la configurazione verticale e la poca propensione alle novità. Ciò che rende straordinariamente innovative le Software-Defined Networks è la separazione tra piano di controllo e piano dei dati, centralizzando il comando nei controllori e riducendo il compito degli switch al solo inoltro di messaggi. Tuttavia, a causa della giovane età, le SDN non hanno ancora maturato una buona resistenza gli attacchi, soprattutto sono particolarmente propense e soggette ad attacchi di Denial of Service. Partendo da questo assunto si è voluto dapprima comprendere come lavorano queste reti, sia in situazione normale e sia, in particolare, sotto attacco, concentrandosi su quali sono i problemi e le vulnerabilità. Il primo step è stata dunque l'analisi dei vari scenari e delle varie casistiche possibili, con l'obiettivo di modellare una topologia multi-controller che facesse da base a tutti gli esperimenti. Grazie ad essa è stato possibile simulare e analizzare il comportamente in situazioni normali, con solo traffico di base, per poi studiare la risposta della rete a un attacco DoS, individuando il minimo sforzo necessario all'attaccante per riuscire a manomettere la rete. Infine, sulla base dello studio dei dati raccolti e dei meccanismi di difesa esistenti, si è pensato che sfruttando una topologia dinamica si potessero ottenere buoni risultati nella prevenzione degli attacchi e si è individuato un modello basato sulla Teoria dei Giochi di mitigazione di un attacco DoS.
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Mohammadnia, Hamzeh. "IoT-NETZ: Spoong Attack Mitigation in IoT Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260250.

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The phenomenal growth of the Internet of Things (IoT) and popularity of the mobile stations have rapidly increased the demand of WLAN network (known as IEEE 802.11 and WiFi). WLAN is a low-cost alternative of the cellular network and being an unlicensed spectrum to build the master plan of embedding the Internet in everything -&amp;-anywhere. At the same time, monitoring the number of IoT and WiFi-enabled devices across residential and enterprises is not trivial. Therefore, future WiFi network architecture requires an agile management paradigm to provide internal support and security for WiFi networks.The operation of IoT and mobile device applications relies on scalability and high-performance computing of clouds. Cloud computing has completely centralized the current data center networking architecture and it provides computation-intensive, high-speed network, and realtime responses to the requests of IoT. The IoT-to-cloud communication is the essence of network security concerns and it is in grievous need of constant security improvement along the inter-networking. Based on the number of researches and analysis on generated traffic by IoT, it has been observed there are the significant number of massive spoofing-oriented attacks targeting cloud services are launched from compromised IoT.On the basis of reviewing prior researches on mostly-conducted network attacks by IoT, there is a challenging and common characteristic which has been frequently utilized in the numerous massive Internet attacks, known as spoofing. This work will survey the existing proposed solutions which have been deployed to protect both traditional and softwarized network paradigms. Then, it proposes the approach of this work that enables IoT-hosting networks protected by employing Software-defined Wireless Networking (SDWN) within the proposed model to mitigate spoofing -oriented network attacks. In addition, the proposed solution provides the environmental sustainability feature by saving power consumption in networking devices during network operation. The practical improvement in the proposed model is measured and evaluated within the emulated environment of Mininet-WiFi.<br>Den fenomenala tillväxten av IoT och populariteten hos mobilstationerna har snabbt ökat efterfrågan på WLAN-nätverk (känd som IEEE 802.11 och WiFi). WLAN är ett billigt alternativ för mobilnätet och är ett olicensierat spektrum för att bygga huvudplanen för att bädda in Internet i allt-och-var som helst. Samtidigt är det inte trivialt att övervaka antalet IoT och WiFi-aktiverade enheter över bostäder och företag. Därför kräver framtida WiFi nätverksarkitektur ett smidigt hantering paradigm för att tillhandahålla internt stöd och säkerhet för WiFi-nätverk.Användningen av IoT och mobilanvändningsapplikationer är beroende av skalbarhet och högpresterande beräkningar av moln. Cloud computing har helt centraliserat den nuvarande datacenters nätverksarkitektur och det ger beräkningsintensiva, höghastighetsnätverk och realtidssvar påbegäran från IoT. IoT-till-moln kommunikationen är kärnan i nätverkssäkerhetshänsyn och de har ett allvarligt behov av ständig förbättring och säkerhetshärdning inom deras internätverk. Baserat på antalet undersökningar och analyser av genererad trafik av IoT har det observerats. Det finns det betydande antalet massiva spoofing-orienterade attacker som riktar sig mot molntjänster, lanseras från komprometterad IoT.På grundval av att granska tidigare undersökningar om IoTs mest genomförda nätverksattacker finns det en utmanande och gemensam egenskap som ofta utnyttjats i de många massiva internetattackerna. Detta arbete kommer att undersöka de befintliga lösningarna som har implementerats för att skydda både traditionella och mjukvariga nätverksparadigmer. Därefter föreslår det tillvägagångssättet för detta arbete som möjliggör IoT-värdnät skyddade genom att använda SDWN inom den föreslagna modellen för att mildra poofing-orienterade nätverksattacker. Dessutom erbjuder den föreslagna lösningen miljöhållbarhet genom att spara strömförbrukning i nätverksenheter under nätverksdrift. Den praktiska förbättringen av den föreslagna modellen mäts och utvärderas inom den omgivande miljön av Mininet-WiFi.
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Saied, Alan. "Distributed Denial of Service (DDoS) attack detection and mitigation." Thesis, King's College London (University of London), 2015. http://kclpure.kcl.ac.uk/portal/en/theses/distributed-denial-of-service-ddos-attack-detection-and-mitigation(eaa45e51-f602-46da-a37a-75c3ae71d2db).html.

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A Distributed Denial of Service (DDoS) attack is an organised distributed packet-storming technique that aims to overload network devices and the communication channels between them. Its major objective is to prevent legitimate users from accessing networks, servers, services, or other computer resources. In this thesis, we propose, implement and evaluate a DDoS Detector approach consisting of detection, defence and knowledge sharing components. The detection component is designed to detect known and unknown DDoS attacks using an Artificial Neural Network (ANN) while the defence component prevents forged DDoS packets from reaching the victim. DDoS Detectors are distributed across one or more networks in order to mitigate the strength of a DDoS attack. The knowledge sharing component uses encrypted messages to inform other DDoS Detectors when it detects a DDoS attack. This mechanism increases the efficacy of the detection mechanism between the DDoS Detectors. This approach has been evaluated and tested against other related approaches in terms of Sensitivity, Specificity, False Positive Rate (FPR), Precision, and Detection Accuracy. A major contribution of the research is that this approach achieves a 98% DDoS detection and mitigation accuracy, which is 5% higher than the best result of previous related approaches.
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Zaleski, Elisa Marie. "Mechanisms and Mitigation of CMAS Attack on Thermal Barrier Coatings." Thesis, University of California, Santa Barbara, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3559850.

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<p> As gas turbine engines are driven to operate at higher temperatures to maximize efficiency, components become susceptible to attack by deposits of calcium magnesium alumino-silicate (CMAS) ingested with the intake air. Of particular interest to this work is the degradation of thermal barrier coatings (TBCs) by CMAS. Molten CMAS is known to interact with TBCs both thermochemically, by dissolving the ceramic and reprecipitating it as a new or modified phase, and thermomechanically, by infiltrating the porosity and degrading the strain tolerance. </p><p> The thermochemical degradation of TBCs was investigated using primarily differential scanning calorimetry (DSC) by comparing the endotherms and exotherms recorded for pure, model silicates to those observed for silicates mixed with various TBC materials including YSZ and GZO. The five ternary silicates studied (CaO-AlO<sub>1.5</sub>-SiO<sub>2</sub>) began melting over a relatively narrow range (&sim;1125-1145&deg;C). Introducing magnesium to the ternary results in higher melting temperatures and only minor changes to the crystallization behavior. Iron decreases the melting temperature, and markedly improves the crystallization kinetics of pure silicate systems, especially absent magnesium. </p><p> Modification of the crystallization behavior of pure silicates has been proposed in the literature as a mitigation strategy for CMAS. This work utilizes DSC to look for characteristic changes as described above to probe potentially effective TBCs. The addition of YSZ to a quaternary CMAS results in little change to the melting or crystallization in the DSC, despite the dissolution of YSZ into the silicate. In stark contrast, GZO with CMAS generates a significant crystallization exotherm that appears in the DSC immediately after the silicate melts. As the fraction of GZO is increased, the melting endotherm begins to shrink due to the thermal overlap of the melting and crystallization processes. This signifies a rapid reaction, and a potentially useful TBC material for CMAS mitigation. Several additional TBC materials are tested and discussed including Y<sub>2</sub>Zr<sub>2</sub>O<sub>7</sub>, La<sub>2</sub>Zr<sub> 2</sub>O<sub>7</sub>, La<sub>2</sub>Ce<sub>2</sub>O<sub>7</sub>, and GdAlO<sub> 3</sub>. Stemming from these results, isothermal exposures of silicates on GZO TBCs demonstrated the influence of CMAS loading and exposure time on both penetration and the recession front depth, as well as the importance of silicate viscosity on the competition between infiltration time and reaction kinetics. </p><p> Furthermore, the present study illuminates several factors relevant to the mechanical degradation of both yttria stablizaed zirconia (YSZ) and gadolinium zirconate (GZO) by molten CMAS through the use of a laser gradient test (LGT) designed following the mechanical model of Evans and Hutchinson. Number of cycles, CMAS loading, CTE mismatch, and most of all, for the LGT, coating toughness influenced the location and extent of cracking within the TBC exposed to CMAS. Both the chemical and mechanical considerations and experimental protocol developed in this investigation lay the groundwork for assessing the CMAS resistance of next generation TBCs.</p>
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Sriskandarajah, Shriparen. "Detection and mitigation of denial-of-service attacks against software-defined networking." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226951/1/Shriparen_Sriskandarajah_Thesis.pdf.

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Software-defined networking (SDN) is an emerging architecture in computer networking that was introduced to fulfill the demand of current Internet-based services and applications. New features introduced in the SDN architecture open the space for attackers to disrupt the SDN-based networks using new types of Denial-of-Service (DoS) attacks. In this study, first, we present a new DoS attack, namely the control channel DoS attack. Second, we present another new DoS attack to overwhelm the flow table of the SDN switches, namely the flow rule overwhelming attack. Finally, we propose novel strategies to detect and mitigate DoS attacks against the SDN architecture.
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Zhang, Yueqian. "Resource Clogging Attacks in Mobile Crowd-Sensing: AI-based Modeling, Detection and Mitigation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40082.

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Mobile Crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of the smart devices to improve the sensing capacity and reduce the sensing costs in large regions. Due to the ubiquitous nature of MCS, smart devices require cyber protection against adversaries that are becoming smarter with the objective of clogging the resources and spreading misinformation in such a non-dedicated sensing environment. In an MCS setting, one of the various adversary types has the primary goal of keeping participant devices occupied by submitting fake/illegitimate sensing tasks so as to clog the participant resources such as the battery, sensing, storage, and computing. With this in mind, this thesis proposes a systematical study of fake task injection in MCS, including modeling, detection, and mitigation of such resource clogging attacks. We introduce modeling of fake task attacks in MCS intending to clog the server and drain battery energy from mobile devices. We creatively grant mobility to the tasks for more extensive coverage of potential participants and propose two take movement patterns, namely Zone-free Movement (ZFM) model and Zone-limited Movement (ZLM) model. Based on the attack model and task movement patterns, we design task features and create structured simulation settings that can be modified to adapt different research scenarios and research purposes. Since the development of a secure sensing campaign highly depends on the existence of a realistic adversarial model. With this in mind, we apply the self-organizing feature map (SOFM) to maximize the number of impacted participants and recruits according to the user movement pattern of these cities. Our simulation results verify the magnified effect of SOFM-based fake task injection comparing with randomly selected attack regions in terms of more affected recruits and participants, and increased energy consumption in the recruited devices due to the illegitimate task submission. For the sake of a secure MCS platform, we introduce Machine Learning (ML) methods into the MCS server to detect and eliminate the fake tasks, making sure the tasks arrived at the user side are legitimate tasks. In our work, two machine learning algorithms, Random Forest and Gradient Boosting are adopted to train the system to predict the legitimacy of a task, and Gradient Boosting is proven to be a more promising algorithm. We have validated the feasibility of ML in differentiating the legitimacy of tasks in terms of precision, recall, and F1 score. By comparing the energy-consuming, effected recruits, and impacted candidates with and without ML, we convince the efficiency of applying ML to mitigate the effect of fake task injection.
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Chen, Cong. "Side Channel Leakage Exploitation, Mitigation and Detection of Emerging Cryptosystems." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-dissertations/472.

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With the emerging computing technologies and applications in the past decades, cryptography is facing tremendous challenges in its position of guarding our digital world. The advent of quantum computers is potentially going to cease the dominance of RSA and other public key algorithms based on hard problems of factorization and discrete logarithm. In order to protect the Internet at post-quantum era, great efforts have been dedicated to the design of RSA substitutions. One of them is code- based McEliece public key schemes which are immune to quantum attacks. Meanwhile, new infrastructures like Internet of Things are bringing the world enormous benefits but, due to the resource-constrained nature, require compact and still reliable cryptographic solutions. Motivated by this, many lightweight cryptographic algorithms are introduced. Nevertheless, side channel attack is still a practical threat for implementations of these new algorithms if no countermeasures are employed. In the past decades two major categories of side channel countermeasures, namely masking and hiding, have been studied to mitigate the threat of such attacks. As a masking countermeasure, Threshold Implementation becomes popular in recent years. It is sound in providing provable side channel resistance for hardware-based cryptosystems but meanwhile it also incurs significant overheads which need further optimization for constrained applications. Masking, especially for higher order masking schemes, requires low signal-to-noise ratio to be effective which can be achieved by applying hiding countermeasures. In order to evaluate side channel resistance of countermeasures, several tools have been introduced. Due to its simplicity, TVLA is being accepted by academy and industry as a one-size-fit-all leakage detection methodolgy that can be used by non-experts. However, its effectiveness can be negatively impacted by environmental factors such as temperature variations. Thus, a robust and simple evaluation method is desired. In this dissertation, we first show how differential power analysis can efficiently exploit the power consumption of a McEliece implementation to recover the private key. Then, we apply Threshold Implementation scheme in order to protect from the proposed attack. This is, to the best of our knowledge, the first time of applying Threshold Implementation in a public key cryptosystem. Next, we investigate the reduction of shares in Threshold Implementation so as to bring down its overhead for constrained applications. Our study shows that Threshold Implementation using only two shares reduces the overheads while still provides reliable first-order resistance but in the meantime it also leaks a strong second-order leakage. We also propose a hiding countermeasure, namely balanced encoding scheme based on the idea of Dual- Rail Pre-charge logic style in hardwares. We show that it is effective to mitigate the leakage and can be combined with masking schemes to achieve better resistance. Finally, we study paired t-test versus Welch's t-test in the original TVLA and show its robustness against environmental noises. We also found that using moving average in computing t statistics can detect higher-order leakage faster.
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Jang, Jae-Won. "Security of Non-Volatile Memories - Attack Models, Analyses, and Counter-Measures." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5709.

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The unprecedented demand for performance in the latest technologies will ultimately require changes in the way we design cache. Emerging high density embedded memories such as Spin-Transfer Torque Random Access Memory (STTRAM) have emerged as a possible candidate for universal memory due to its high speed, low power, non-volatility, and low cost. Although attractive, STTRAM is susceptible to contactless tampering through malicious exposure to magnetic field with the intention to steal or modify the bitcell content. In this thesis, we explore various attack techniques on STTRAM and then propose a novel array-based sensor to detect the polarity and magnitude of such attacks and then propose two design techniques to mitigate the attack. With our research, we have been able to successfully implement and accurately detect an attack while providing sufficient compensation window (few ns to ~100 us) to enable proactive protection measures. Finally, we show that variable-strength ECC can adapt correction capability to tolerate failures with various strength of an attack.
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Larsson, Niklas, and Josefsson Fredrik Ågren. "A study of slow denial of service mitigation tools and solutions deployed in the cloud." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157721.

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Slow rate Denial of Service (DoS) attacks have been shown to be a very effective way of attacking vulnerable servers while using few resources. This thesis investigates the effectiveness of mitigation tools used for protection against slow DoS attacks, specifically slowheader and slow body. Finally, we propose a service that cloud providers could implement to ensure better protection against slow rate DoS attacks. The tools studied in this thesis are, a Web Application firewall, a reverse proxy using an event-based architecture and Amazon’s Elastic Load Balancing. To gather data a realistic HTTP load script was built that simulated load on the server while using probe requests to gather response time data from the server. The script recorded the impact the attacks had for each server configuration.The results show that it’s hard to protect against slow rate DoS attacks while only using firewalls or load balancers. We found that using a reverse proxy with an event-based architecture was the best way to protect against slow rate DoS attacks and that such a service would allow the customer to use their server of choice while also being protected.
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Goldschmidt, Patrik. "Potlačení DoS útoků s využitím strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449294.

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Útoky typu odoprenia služby (DDoS) sú v dnešných počítačových sieťach stále frekventovanejším bezpečnostným incidentom. Táto práca sa zameriava na detekciu týchto útokov a poskytnutie relevantných informácii za účelom ich mitigácie v reálnom čase. Spomínaná funkcionalita je dosiahnutá s využitím techník prúdového dolovania z dát a strojového učenia. Výsledkom práce je sada nástrojov zastrešujúca celý proces strojového učenia - od vlastnej extrakcie príznakov cez predspracovanie dát až po export natrénovaného modelu pripraveného na nasadenie v produkcii. Experimentálne výsledky vyhodnotené na viacerých reálnych a syntetických dátových sadách poukazujú na presnosť systému väčšiu ako 99% s možnosťou spoľahlivej detekcie prebiehajúceho útoku do 4 sekúnd od jeho začiatku.
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Books on the topic "Attack mitigation"

1

Dzikansky, Mordecai Z. Terrorist suicide bombings: Attack interdiction, mitigation, and response. CRC Press, 2012.

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Dübendorfer, Thomas P. Impact analysis, early detection, and mitigation of large-scale Internet attacks. Shaker, 2005.

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Hersman, Rebecca K. C. DOD and consequence management: Mitigating the effects of chemical and biological attack. Institute for National Strategic Studies, National Defense University, 1999.

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Raghavan, S. V., and E. Dawson, eds. An Investigation into the Detection and Mitigation of Denial of Service (DoS) Attacks. Springer India, 2011. http://dx.doi.org/10.1007/978-81-322-0277-6.

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Casola, Linda, and Dionna Ali, eds. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies. National Academies Press, 2019. http://dx.doi.org/10.17226/25534.

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E, Dawson, and SpringerLink (Online service), eds. An Investigation into the Detection and Mitigation of Denial of Service (DoS) Attacks: Critical Information Infrastructure Protection. Springer India Pvt. Ltd., 2011.

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Augustyn, Teresa. An evaluation of grizzly bear - human conflict in the northwest boreal region of Alberta (1991 to 2000) and potential mitigation. Fisheries & Wildlife Management Division, Resource Status and Assessment Branch, 2001.

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Altvater, Elmar. Ablasshandel gegen Klimawandel?: Marktbasierte Instrumente in der globalen Klimapolitik und ihre Alternativen : Reader des Wissenschaftlichen Beirats von Attac. VSA-Verlag, 2008.

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Attack, United States Congress House Committee on Homeland Security Subcommittee on the Prevention of Nuclear and Biological. Mitigating catastrophic events through effective medical response: Hearing before the Subcommittee on Prevention of Nuclear and Biological Attack of the Committee on Homeland Security, House of Representatives, One Hundred Ninth Congress, first session, October 20, 2005. U.S. G.P.O., 2007.

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Robert, Slater, Mordecai Dzikansky, and Gil Kleiman. Terrorist Suicide Bombings: Attack Interdiction, Mitigation, and Response. Taylor & Francis Group, 2011.

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Book chapters on the topic "Attack mitigation"

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Brooks, Richard R., and İlker Özçelik. "Attack Mitigation." In Distributed Denial of Service Attacks. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-10.

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Haber, Morey J., Brian Chappell, and Christopher Hills. "Mitigation Strategies." In Cloud Attack Vectors. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8236-6_7.

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Hasan, Nausherwan. "Sulfate Attack Mitigation." In Durability and Sustainability of Concrete. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51573-7_7.

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Grammatikakis, Konstantinos-Panagiotis, and Nicholas Kolokotronis. "Attack Graph Generation." In Cyber-Security Threats, Actors, and Dynamic Mitigation. CRC Press, 2021. http://dx.doi.org/10.1201/9781003006145-8.

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Chen, Zhongtian, and Hao Han. "Attack Mitigation by Data Structure Randomization." In Foundations and Practice of Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51966-1_6.

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Yildiz, Mürsel, Ahmet Cihat Toker, Fikret Sivrikaya, Seyit Ahmet Camtepe, and Sahin Albayrak. "User Facilitated Congestion and Attack Mitigation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30422-4_26.

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Zou, Qingtian, Lan Zhang, Anoop Singhal, Xiaoyan Sun, and Peng Liu. "Attacks on ML Systems: From Security Analysis to Attack Mitigation." In Information Systems Security. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23690-7_7.

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Jadhav, Swati, Arjun Thakur, Shravani Nalbalwar, Shubham Shah, and Sankalp Chordia. "Detection and Mitigation of ARP Spoofing Attack." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3010-4_33.

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Alghenaim, Mohammed Fahad, Nur Azaliah Abu Bakar, Fiza Abdul Rahim, Vanye Zira Vanduhe, and Gamal Alkawsi. "Phishing Attack Types and Mitigation: A Survey." In Data Science and Emerging Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0741-0_10.

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Szynkiewicz, Paweł. "Signature-Based Detection of Botnet DDoS Attacks." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_6.

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AbstractThe distributed denial of service (DDoS) attack is an attempt to disrupt the proper availability of a targeted server, service or network. The attack is achieved by corrupting or overwhelming the target’s communications with a flood of malicious network traffic. In the current era of mass connectivity DDoS attacks emerge as one of the biggest threats, staidly causing greater collateral damage and heaving a negate impacting on the integral Internet Infrastructure. DDoS attacks come in a variety of types and schemes, they continue to evolve, steadily becoming more sophisticated and larger at scale. A close investigation of attack vectors and refining current security measures is required to efficiently mitigate new DDoS threats. The solution described in this article concerns a less explored variation of signature-based techniques for DDoS mitigation. The approach exploits one of the traits of modern DDoS attacks, the utilization of Packet generation algorithms (PGA) in the attack execution. Proposed method performs a fast, protocol-level detection of DDoS network packets and can easily be employed to provide an effective, supplementary protection against DDoS attacks.
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Conference papers on the topic "Attack mitigation"

1

Ali, Wasif, Ahthasham Sajid, Tanvi Ajit Ghodke, Rida Malik, Neeru Malik, and Keshav Kaushik. "Honeypot Comparison of Attack Detection and Mitigation of SSH Attack." In 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2024. https://doi.org/10.1109/delcon64804.2024.10866522.

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Ibrahim, Ahmed Jamal, and Sandor R. Repas. "DDOS Attack Mitigation Based on FPGA Implementation." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698517.

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James, Gracious, Chris Abraham, Saurabh Dipte, Anushka Gaat, and Amroz Siddiqui. "Distributed Denial of Service Attack Mitigation Using Reinforcement Learning." In 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP). IEEE, 2024. http://dx.doi.org/10.1109/icecsp61809.2024.10698040.

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Gilmary, Rosario, Kaviya N, Manvizhi N, Arunkumar M, and Arunkumar R. "Intelligent DDoS Attack Prevention and Mitigation in SDN Environment." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894440.

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Kanter, Morgon, and Stephen Taylor. "Attack Mitigation through Diversity." In MILCOM 2013 - 2013 IEEE Military Communications Conference. IEEE, 2013. http://dx.doi.org/10.1109/milcom.2013.239.

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Roy, Sangita, and Ashok Singh Sairam. "Network Attack Detection and Mitigation." In 2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW). IEEE, 2015. http://dx.doi.org/10.1109/sasow.2015.33.

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Ezekiel, Sharon, Dinil Mon Divakaran, and Mohan Gurusamy. "Dynamic attack mitigation using SDN." In 2017 27th International Telecommunication Networks and Applications Conference (ITNAC). IEEE, 2017. http://dx.doi.org/10.1109/atnac.2017.8215430.

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Then Zhao Xuan, Sean, and Vinesha Selvarajah. "Web Shell Attack and Mitigation." In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon). IEEE, 2022. http://dx.doi.org/10.1109/mysurucon55714.2022.9972572.

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Gheorghe, Laura, Razvan Rughinis, and Nicolae Tapus. "Attack Evaluation and Mitigation Framework." In 2010 Sixth International Conference on Networking and Services (ICNS). IEEE, 2010. http://dx.doi.org/10.1109/icns.2010.63.

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Kaushik, Keshav, Govind Sharma, Preeti Narooka, Gunjan Chhabra, and Ankit Vishnoi. "HTML Smuggling: Attack and Mitigation." In 2023 16th International Conference on Security of Information and Networks (SIN). IEEE, 2023. http://dx.doi.org/10.1109/sin60469.2023.10474837.

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Reports on the topic "Attack mitigation"

1

Applegate, C., and A. Campbell. Best Practices for Timing Attack Mitigation. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1838256.

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Schaffer, Kim. CMVP approved non-invasive attack mitigation test metrics:. National Institute of Standards and Technology, 2020. http://dx.doi.org/10.6028/nist.sp.800-140f.

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Letchford, Joshua. Game Theory for Proactive Dynamic Defense and Attack Mitigation in Cyber-Physical Systems. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1330190.

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Schaffer, Kim. CMVP Approved Non-Invasive Attack Mitigation Test Metrics: CMVP Validation Authority Updates to ISO/IEC 24759. National Institute of Standards and Technology, 2021. http://dx.doi.org/10.6028/nist.sp.800-140fr1-draft.

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Smythe, Lucy, and Lydia Harriss. Defence of space-based assets. Parliamentary Office of Science and Technology, 2021. http://dx.doi.org/10.58248/pn654.

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Space-based assets (satellites and the terrestrial ground stations that communicate with them) provide critical support to military and civilian operations. They are vulnerable to unintentional damage and disruption, and to deliberate attack. This POSTnote outlines how the UK uses and accesses satellites, potential risks to satellites, and approaches to mitigation.
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Eddy, W. TCP SYN Flooding Attacks and Common Mitigations. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4987.

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Nakibly, G., and F. Templin. Routing Loop Attack Using IPv6 Automatic Tunnels: Problem Statement and Proposed Mitigations. RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6324.

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Godfrey, Jody. Transit Workforce Development Challenges and Mitigation Practices. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2361.

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The objective of this research is to summarize previously conducted research related to workforce development challenges in the transit industry, detailing major findings and subsequent recommendations based on the annotated bibliography of the current atmosphere and most successful ways to mitigate those challenges to attract and retain talent in the transit industry. According to the U.S. Chamber of Commerce, the U.S. had over 10 million job openings and only 5.7 million unemployed workers in March 2023. In 2017, the Washington Post posited that there were not enough workers to fill the vacancies, as the U.S. Census Bureau announced a 17-year low in unemployment while the Bureau of Labor Statistics hit record streaks for numbers of job openings. The COVID-19 pandemic exacerbated the employment challenges that the U.S. already faced. To top it all, the pandemic also led to increased retirement rates among those close to retirement age. This provides transit industry executives and transportation professionals, with ideas of tools that are available to help attract more qualified candidates to the transit industry and ways to advance the development of the transit workforce. This research highlights some of the social media recruiting; online hiring platform improvements; and partnerships with career centers, universities, and recruitment centers to attract new employees to the transit workforce. In addition, increases in pay, benefits, bonuses, providing flexible schedule options and some other atypical ideas have successfully been used to retain workers in the transit workforce. This research focuses on ways in which all transit stakeholders can invest in all aspects of industry workforce development to ensure qualified employees choose the transit industry and that they are subsequently trained to be the most beneficial assets to the organization and remain there via effective retention strategies.
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Mankins, David, Rajesh Krishnan, Ceilyn Boyd, John Zao, and Michael Frentz. Mitigating Distributed Denial of Service Attacks with Dynamic Resource Pricing. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada406438.

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Kerber, Steve, and Robin Zevotek. Study of Residential Attic Fire Mitigation Tactics and Exterior Fire Spread Hazards on Firefighter Safety Released. UL Firefighter Safety Research Institute, 2014. http://dx.doi.org/10.54206/102376/lihb1439.

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Attic fires pose many hazards for the fire service. When a fire occurs in an attic, it is common it goes unnoticed/reported until smoke or flames are visible from the outside of the structure. Because they take longer to detect, attic fires are more dangerous for firefighters and residents. In a fire situation, the attic ventilation system, which is designed to reduce moisture accumulation by drawing fresh air low from the eaves and exhausting moisture laden warm air near the peak, create an optimal fire growth and spread situation by supplying oxygen to the fire and exhausting hot gases. An estimated 10,000 residential attic fires are reported to U.S. fire departments each year and cause an estimated 30 civilian deaths, 125 civilian injuries and $477 million in property loss. The location of the attic creates several difficulties for the fire service. Firefighters must decide whether to fight the fire from inside the structure, from the outside or a combination of the two. This the decision is complicated by the constant hazard of ceiling collapse, which has the potential to rapidly deteriorate conditions in the living spaces. A piece of gypsum board may fall or be pulled from the ceiling making the relatively clear and cool conditions in the living space change very quickly endangering firefighters executing a search and rescue operation as part of their life safety mission. Further complicating the decision are the hazards associated with roof structure collapse, creating deadly conditions for firefighters operating on and under the roof. Structural collapse accounted for 180 firefighter deaths between 1979 and 2002 of which one-third occurred in residential structures . Many of these incidents involved a roof falling on firefighters or firefighters falling through the roof during firefighting operations on attic fires. The purpose of this study is to increase firefighter safety by providing the fire service with scientific knowledge on the dynamics of attic and exterior fires and the influence of coordinated fire mitigation tactics from full-scale fire testing in realistic residential structures.
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