Network intrusion detection systems (NIDS) are increasingly developed using machinelearning (ML) techniques. However, incorporating ML into NIDS introduces a new vulnerability: the threats and limitations arising fro...
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ISBN:
(纸本)9783031649479;9783031649486
Network intrusion detection systems (NIDS) are increasingly developed using machinelearning (ML) techniques. However, incorporating ML into NIDS introduces a new vulnerability: the threats and limitations arising from adversarial machinelearning (AML) attacks. Specific to this application domain, AML could enable an attacker to disguise incoming malicious packets and fool a NIDS to classify them as benign. Although AML has been researched actively in other domains, assessing its impact in networks remains an outstanding challenge, especially since network protocols pose a constrained domain for adversarial packet generation. More specifically, there is a need to experiment with the latest advances in AML attacks - usually developed for an unconstrained domain - on such domains. This paper presents a novel approach to this problem, where a variety of attacks could still be applied and correctly evaluated in a constrained domain. We show an implementation of this approach for NIDS, by developing an adversarial packet validator for different network protocols. By conducting extensive experiments using multiple data sets, ML models, and attacks, we show how our approach can bridge the gap between progress in AML and a constrained domain like NIDS. Evaluation enabled by our approach and its implementation suggests that black-box evasion attacks continue to be a threat to NIDS, despite many constraints offered by this domain.
The key issues in Reservoir Usage are the Computation of Reservoir Storage and Prediction of Water Levels using machinelearning. The research integrates complex underground water techniques with AI to forecast water ...
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The field of natural language processing (NLP) is that of artificial intelligence and computer science, which investigates how computers influence human language. Speech recognition, translation, and sentiment analysi...
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Facial recognition technology has become integral to numerous security and authentication systems. This study introduces a facial recognition system based on the K-Nearest Neighbors (KNN) algorithm. The system ensures...
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Road accidents are a pervasive global issue with profound consequences for individuals, communities, and economies. This research investigates the diverse impacts of traffic accidents on human lives, healthcare system...
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Road accidents are a pervasive global issue with profound consequences for individuals, communities, and economies. This research investigates the diverse impacts of traffic accidents on human lives, healthcare systems, and economic development. Accurate accident severity analysis is crucial for effective management and prevention. To enhance prediction accuracy, this study explores the integration of machinelearning methods, including Random Forest, Support Vector machine, K-Nearest Neighbours, and Decision Tree [1]. Utilizing the Road Traffic Accident dataset, the research focuses on feature extraction and selection, aiming to classify accident severity into three levels: minor, severe, and fatal. Despite the dataset's real-world basis and inherent imbalance, this study contributes valuable insights to the discourse on road safety and accident severity prediction.
The maternal health includes the health conditions of women in three stages that are during pregnancy, childbirth and the postpartum time. We can also say that maternal health is the health of women at the time during...
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When discussing the dynamic management of virtual machines in cloud computing environment, the current cloud cluster virtual machine management algorithm faces the problems of too single optimization goal, lack of gen...
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The increasing need for effective vertical transit systems in present-day areas has led to the creation of clever elevator system solutions. In order to improve floor-level prioritizing, this study presents a Smart El...
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The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based algorithm that has made some significant advances since its introduction in 2020. A detailed overview of the basic SSA and several SSA-based ...
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The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based algorithm that has made some significant advances since its introduction in 2020. A detailed overview of the basic SSA and several SSA-based variants is presented in this paper. To be specific, first, the principle of the basic SSA is introduced including its search mechanism and implementation process. Second, many improved SSAs are reviewed including the hybrid, chaotic, adaptive, binary and multi-objective SSAs. In addition, the applications of the SSAs are presented in some real scenarios such as the machinelearning areas, energy systems, path planning and image processing. Finally, further research directions of the SSAs are discussed. This survey paper aims to provide a timely review on the latest developments of the SSAs.
There is a dearth of coursework on designing and implementing services that incorporate machinelearning models in university curricula despite the growing industry demand. We describe the design of a course titled &q...
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ISBN:
(纸本)9798350330656;9798350330649
There is a dearth of coursework on designing and implementing services that incorporate machinelearning models in university curricula despite the growing industry demand. We describe the design of a course titled "ML Production systems" which covers the implementation, deployment, monitoring, and updating of machinelearning models as part of user-facing web services. The course is designed around a semester-long project to implement and deploy a home sale price prediction service. The course is a required course in a Master's in machinelearning program and graduate machinelearningengineering certificate program. The course was taught in an online synchronous format to twenty-nine students in Fall 2023;fifteen of the students had more than one year of professional experience as software engineers, while the remaining fourteen students were accelerated Master's students. Analysis of open-ended written student feedback indicated that students in both groups had a positive experience and found the course to be valuable. Assessment of learning outcome achievements with a final exam and project completion rates indicated that nearly all of the students successfully achieved the learning outcomes. No statistically-significant differences in achievement between the working professionals and accelerated Master's students were detected. We believe that this course and its successful delivery will serve as a blueprint for other faculty who may want to implement similar courses.
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