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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT dataset 26

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A Horizontal Federated Learning Approach to IoT Malware Traf...

26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

Deviations in Efficiencies of Logarithmically Growing Sorting Algorithms at Infinitely Large datasets

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Deviations in Efficiencies of Logarithmically Growing Sortin...

Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Steve Mathew D A School of Information Technology and Engineering Vellore Institute of Technology Vellore India Programming and Data Science Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9781665426923

Asymptotic Notations have been very effective in representing the efficiencies of algorithms by analyzing the number of steps and the nature of traversals and operations that the algorithm is bound to perform. Though they represent the nature of growth of the time or space consumed by the algorithms, they only consider the fastest-growing term and not the other terms. The result from this kind of analysis often seems to be fruitful in grading the algorithms based on their efficiency, but theoretically, there seem to be evident deviations when these complexities are extrapolated to larger datasets, hypothetically ‘infinitely large’ datasets. This deviation is tracked down when the constant and slow-growing terms involved are considered. The practical difficulty of empirically verifying this fact due to the ambiguity in defining a very large number of inputs is overcome by theoretically calculating the ratio between the time complexities of algorithms of interest and applying their limit to be infinity. In this study, the algorithms heapsort and merge sort are analyzed and the drastic deviation between them is shown mathematically though they both have the complexity of $n(log(n))$ , concluding that the merge sort is actually twice as fast as heap sort when approaching infinitely large data elements.

关键词： Computers Technological innovation Complexity theory Time complexity Sorting

Calculation of Signal Information Delay in Intelligent Communication Networks 3

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Calculation of Signal Information Delay in Intelligent Commu...

3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Kuchuk, Nina Mozhaiev, Mykhailo Kalinin, Yevhen Davydov, Viacheslav Mozhaev, Oleksandr Kuchuk, Heorhii National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine Computer Technology and Telecommunications Researches Sector National Scientific Center «hon. Prof Bokarius Forensic Science Institute» Kharkiv Ukraine National University of Life and Environmental Sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine Kharkiv National University of Internal Affairs Department of Cyber Security and Data Technologies Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

An analysis of the signaling systems used in the Intelligent Communication Network has been carried out. The main probabilistic characteristics of signal information delay are determined. A method for calculating these characteristics for such error correction methods is proposed: basic error control method and preventive cyclical control method. The analysis of the delay of signal units from loading the signal network is carried out. Simple formulas are proposed for approximating the load applied to the signaling link. © 2022 IEEE.

关键词： Error correction

FLEXIBLE CORRECT-BY-CONSTRUCTION programming

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arXiv 2022年

作者： Runge, Tobias Bordis, Tabea Potanin, Alex Thüm, Thomas Schaefer, Ina Karlsruhe Institute of Technology Germany Institute of Software Engineering and Automotive Informatics TU Braunschweig Germany School for Data-Science and Computational Thinking Stellenbosch University South Africa School of Computing Australian National University Australia Institute of Software Engineering and Programming Languages University of Ulm Germany

Correctness-by-Construction (CbC) is an incremental program construction process to construct functionally correct programs. The programs are constructed stepwise along with a specification that is inherently guaranteed to be satisfied. CbC is complex to use without specialized tool support, since it needs a set of predefined refinement rules of fixed granularity which are additional rules on top of the programming language. Each refinement rule introduces a specific programming statement and developers cannot depart from these rules to construct programs. CbC allows to develop software in a structured and incremental way to ensure correctness, but the limited flexibility is a disadvantage of CbC. In this work, we compare classic CbC with CbC-Block and TraitCbC. Both approaches CbC-Block and TraitCbC, are related to CbC, but they have new language constructs that enable a more flexible software construction approach. We provide for both approaches a programming guideline, which similar to CbC, leads to well-structured programs. CbC-Block extends CbC by adding a refinement rule to insert any block of statements. Therefore, we introduce CbC-Block as an extension of CbC. TraitCbC implements correctness-by-construction on the basis of traits with specified methods. We formally introduce TraitCbC and prove soundness of the construction strategy. All three development approaches are qualitatively compared regarding their programming constructs, tool support, and usability to assess which is best suited for certain tasks and developers. © 2022, CC BY.

关键词： Formal verification

A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT dataset

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A Horizontal Federated Learning Approach to IoT Malware Traf...

International Conference on Advanced Communication Technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

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ANALYZING ROBUSTNESS OF ANGLUIN'S L∗ ALGORITHM IN PRESENCE OF NOISE

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arXiv 2023年

作者： Ye, Lina Khmelnitsky, Igor Haddad, Serge Barbot, Benoît Bollig, Benedikt Leucker, Martin Neider, Daniel Roy, Rajarshi Université Paris-Saclay CNRS ENS Paris-Saclay CentraleSupélec LMF France Université Paris-Saclay CNRS ENS Paris-Saclay LMF France Université Paris-Est Créteil France Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany TU Dortmund University Germany Center for Trustworthy Data Science and Security University Alliance Ruhr Germany Max Planck Institute for Software Systems Kaiserslautern Germany

Angluin's L∗ algorithm learns the minimal deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approximatively correct (PAC) version substitutes an equivalence query by numerous random membership queries to get a high level confidence to the answer. Thus it can be applied to any kind of device and may be viewed as an algorithm for synthesizing an automaton abstracting the behavior of the device based on observations. Here we are interested on how Angluin's PAC learning algorithm behaves for devices which are obtained from a DFA by introducing some noise. More precisely we study whether Angluin's algorithm reduces the noise and produces a DFA closer to the original one than the noisy device. We propose several ways to introduce the noise: (1) the noisy device inverts the classification of words w.r.t. the DFA with a small probability, (2) the noisy device modifies with a small probability the letters of the word before asking its classification w.r.t. the DFA, (3) the noisy device combines the classification of a word w.r.t. the DFA and its classification w.r.t. a counter automaton, and (4) the noisy DFA is obtained by a random process from two DFA such that the language of the first one is included in the second one. Then when a word is accepted (resp. rejected) by the first (resp. second) one, it is also accepted (resp. rejected) and in the remaining cases, it is accepted with probability 0.5. Our main experimental contributions consist in showing that: (1) Angluin's algorithm behaves well whenever the noisy device is produced by a random process, (2) but poorly with a structured noise, and, that (3) is able to eliminate pathological behaviours specified in a regular way. Theoretically, we show that randomness almost surely yields systems with non-recursively enumerable languages. © 2023, CC BY.

关键词： Random processes

The SCIP Optimization Suite 9.0

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arXiv 2024年

作者： Bolusani, Suresh Besançon, Mathieu Bestuzheva, Ksenia Chmiela, Antonia Dionísio, João Donkiewicz, Tim van Doornmalen, Jasper Eifler, Leon Ghannam, Mohammed Gleixner, Ambros Graczyk, Christoph Halbig, Katrin Hedtke, Ivo Hoen, Alexander Hojny, Christopher van der Hulst, Rolf Kamp, Dominik Koch, Thorsten Kofler, Kevin Lentz, Jurgen Manns, Julian Mexi, Gioni Mühmer, Erik Pfetsch, Marc E. Schlösser, Franziska Serrano, Felipe Shinano, Yuji Turner, Mark Vigerske, Stefan Weninger, Dieter Xu, Liding Gaul, Oliver Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany Université Grenoble Alpes Inria LIG Grenoble38000 France CMUP Department of Computer Science Faculty of Sciences University of Porto R Campo Alegre Porto4169-007 Portugal RWTH Aachen University Lehrstuhl für Operations Research Kackertstr. 7 Aachen52072 Germany Eindhoven University of Technology Department of Mathematics and Computer Science P.O. Box 513 Eindhoven5600 MB Netherlands Friedrich-Alexander Universität Erlangen-Nürnberg Department of Data Science Cauerstr. 11 Erlangen91058 Germany Schenker AG Global Data & AI Kruppstr. 4 Essen45128 Germany University of Twente Department of Discrete Mathematics and Mathematical Programming P.O. Box 217 Enschede7500 AE Netherlands University of Bayreuth Universitaetsstr. 30 Bayreuth95440 Germany Technische Universität Berlin Straße des 17. Juni 135 Berlin10623 Germany Zuse Institute Berlin Department A2IM Takustr. 7 Berlin14195 Germany DAGOPT Optimization Technologies GmbH Austria Technische Universität Darmstadt Fachbereich Mathematik Dolivostr. 15 Darmstadt64293 Germany Fair Isaac Germany GmbH Takustr. 7 Berlin14195 Germany COPT GmbH Berlin Germany GAMS Software GmbH c/o Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany Friedrich-Alexander Universität Erlangen-Nürnberg Department of Mathematics Cauerstr. 11 Erlangen91058 Germany École polytechnique LIX CNRR Rue Honoré d’Estienne d’Orves. 1 Palaiseau9120 France

MSC Codes 90C05, 90C10, 90C11, 90C30, 90C90, 65Y05The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bugfixes. SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver. Copyright © 2024, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

Bayesian dose-regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

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arXiv 2020年

作者： Gerard, Emma Zohar, Sarah Thai, Hoai-Thu Lorenzato, Christelle Riviere, Marie-Karelle Ursino, Moreno Centre de Recherche des Cordeliers Sorbonne Université Inserm Université de Paris ParisF-75006 France Oncology biostatistics Biostatistics and Programming department Sanofi R&D Vitry-sur-Seine France Statistical Methodology Group Biostatistics and Programming department Sanofi R&D Chilly-Mazarin France Translation Disease Modeling Digital and Data Science Sanofi R&D Chilly-Mazarin France F-CRIN PARTNERS platform AP-HP Université de Paris Paris France

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose-regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) information to estimate the maximum tolerated dose-regimen (MTD-regimen), at the end of the dose-escalation stage of a trial to be recommended for the next phase. We modeled the binary toxicity via a PD endpoint and estimated the dose-regimen toxicity relationship through the integration of a dose-regimen PD model and a PD toxicity model. For the dose-regimen PD model, we considered nonlinear mixed-effects models, and for the PD toxicity model, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. We evaluated the operating characteristics of the DRtox through simulation studies under various scenarios. The results showed that our method outperforms traditional model-based designs demonstrating a higher percentage of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information in the DRtox helped provide more precise estimates for the entire dose-regimen toxicity curve;therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox should be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected. © 2020, CC-BY.

关键词： Toxicity

Enabling Research through the SCIP Optimization Suite 8.0

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arXiv 2023年

作者： Bestuzheva, Ksenia Besançon, Mathieu Chen, Wei-Kun Chmiela, Antonia Donkiewicz, Tim van Doornmalen, Jasper Eifler, Leon Gaul, Oliver Gamrath, Gerald Gleixner, Ambros Gottwald, Leona Graczyk, Christoph Halbig, Katrin Hoen, Alexander Hojny, Christopher van der Hulst, Rolf Koch, Thorsten Lübbecke, Marco Maher, Stephen J. Matter, Frederic Mühmer, Erik Müller, Benjamin Pfetsch, Marc E. Rehfeldt, Daniel Schlein, Steffan Schlösser, Franziska Serrano, Felipe Shinano, Yuji Sofranac, Boro Turner, Mark Vigerske, Stefan Wegscheider, Fabian Wellner, Philipp Weninger, Dieter Witzig, Jakob Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany ZIB-AIS2T Germany School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China RWTH Aachen University Lehrstuhl für Operations Research Kackertstr. 7 Aachen52072 Germany Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands ZIB-AIS2T I2DAMO GmbH Englerallee 19 Berlin14195 Germany ZIB-AIS2T HTW Berlin Germany Friedrich-Alexander Universität Erlangen-Nürnberg Department of Data Science Cauerstr. 11 Erlangen91058 Germany University of Twente Department of Discrete Mathematics and Mathematical Programming P.O. Box 217 Enschede7500 AE Netherlands Technische Universität Berlin Software and Algorithms for Discrete Optimization Straße des 17. Juni 135 Berlin10623 Germany ZIB Department A2IM Takustr. 7 Berlin14195 Germany RWTH Aachen-Lehrstuhl für OR Germany University of Exeter College of Engineering Mathematics and Physical Sciences Exeter United Kingdom Technische Universität Darmstadt Fachbereich Mathematik Dolivostr. 15 Darmstadt64293 Germany TU Darmstadt Fachbereich Mathematik Germany ZIB-A2IM Germany TU Berlin Germany ZIB-A2IM Software and Algorithms for Discrete Optimization Institute of Mathematics TU Berlin Germany GAMS Software GmbH c/o ZIB-AIS2T Germany

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. The focus of this paper is on the role of the SCIP Optimization Suite in supporting research. SCIP's main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP's application as a research tool and as a platform for further developments. Further, the paper gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon *** Codes 90C05, 90C10, 90C11, 90C30, 90C90, 65Y05 Copyright © 2023, The Authors. All rights reserved.

关键词： Integer programming