A Framework for Detecting Cyber Attacks: Study and Analysis

La tesi tracta de l'ús d'Osquery com a eina de seguretat per a sistemes informàtics. S'explica com instal·lar i configurar Osquery i altres eines relacionades amb Ansible. S'analitzen diferents casos d'ús d'Osquery i es mostren exemples de com utilitzar-lo per detectar amenaces i realitzar auditories de seguretat. Es recomana als professionals de la seguretat que revisin i actualitzin regularment les seves polítiques i controls de seguretat i que implementin programes de formació i conscienciació per als empleats per ajudar a prevenir violacions de seguretat

A nuclear training simulator implementing a capability for multiple, concurrent-training sessions

The Advanced Test Reactor (ATR) Simulator at the Test Reactor Area of the Idaho National Engineering Laboratory (INEL) has recently been upgraded to reflect plant installation of a distributed control system (DCS). The ATR Simulator re-design implements traditional needs for software extensibility and plant installation prototyping, but the driving force behind its new design was an instruction requirement for multiple, concurrent-training sessions. Support is provided for up to three concurrent, independent or interacting, training sessions of reactor, balance of plant, and experiment loop operators. This capability has been achieved by modifying the existing design to consistently apply client-server, parent-child, and peer-to-peer processing technologies, and then to encapsulate concurrency software into all interfaces. When the resulting component-oriented design is linked with build and runtime flexibility in a distributed computing environment, traditional needs for extensibility and parallel software and scenario development are satisfied with minimal additional effort. Sensible configuration management practices coupled with the ability to perform piecewise system builds also greatly facilitate prototyping of plant changes prior to installation

ANALYSIS OF DATA & COMPUTER NETWORKS IN STUDENTS' RESIDENTIAL AREA IN UNIVERSITI TEKNOLOGI PETRONAS

In Universiti Teknologi Petronas (UTP), most of the students depend on the Internet and computer network connection to gain academics information and share educational resources. Even though the Internet connections and computers networks are provided, the service always experience interruption, such as slow Internet access, viruses and worms distribution, and network abuse by irresponsible students. Since UTP organization keeps on expanding, the need for a better service in UTP increases. Several approaches were put into practice to address the problems. Research on data and computer network was performed to understand the network technology applied in UTP. A questionnaire forms were distributed among the students to obtain feedback and statistical data about UTP's network in Students' Residential Area. The studies concentrate only on Students' Residential Area as it is where most of the users reside. From the survey, it can be observed that 99% of the students access the network almost 24 hours a day. In 2005, the 2 Mbps allocated bandwidth was utilized 100% almost continuously but in 2006, the bottleneck of Internet access has reduced significantly since the bandwidth allocated have been increased to 8 Mbps. Server degradation due to irresponsible acts by users also adds burden to the main server. In general, if the proposal to ITMS (Information Technology & Media Services) Department for them to improve their Quality of Service (QoS) and established UTP Computer Emergency Response Team (UCert), most of the issues addressed in this report can be solved

Modeling and Analysis of Mixed Synchronous/Asynchronous Systems

Practical safety-critical distributed systems must integrate safety critical and non-critical data in a common platform. Safety critical systems almost always consist of isochronous components that have synchronous or asynchronous interface with other components. Many of these systems also support a mix of synchronous and asynchronous interfaces. This report presents a study on the modeling and analysis of asynchronous, synchronous, and mixed synchronous/asynchronous systems. We build on the SAE Architecture Analysis and Design Language (AADL) to capture architectures for analysis. We present preliminary work targeted to capture mixed low- and high-criticality data, as well as real-time properties in a common Model of Computation (MoC). An abstract, but representative, test specimen system was created as the system to be modeled

The future roadmap of in-vehicle network processing: a HW-centric (R-)evolution

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The automotive industry is undergoing a deep revolution. With the race towards autonomous driving, the amount of technologies, sensors and actuators that need to be integrated in the vehicle increases exponentially. This imposes new great challenges in the vehicle electric/electronic (E/E) architecture and, especially, in the In-Vehicle Network (IVN). In this work, we analyze the evolution of IVNs, and focus on the main network processing platform integrated in them: the Gateway (GW). We derive the requirements of Network Processing Platforms that need to be fulfilled by future GW controllers focusing on two perspectives: functional requirements and structural requirements. Functional requirements refer to the functionalities that need to be delivered by these network processing platforms. Structural requirements refer to design aspects which ensure the feasibility, usability and future evolution of the design. By focusing on the Network Processing architecture, we review the available options in the state of the art, both in industry and academia. We evaluate the strengths and weaknesses of each architecture in terms of the coverage provided for the functional and structural requirements. In our analysis, we detect a gap in this area: there is currently no architecture fulfilling all the requirements of future automotive GW controllers. In light of the available network processing architectures and the current technology landscape, we identify Hardware (HW) accelerators and custom processor design as a key differentiation factor which boosts the devices performance. From our perspective, this points to a need - and a research opportunity - to explore network processing architectures with a strong HW focus, unleashing the potential of next-generation network processors and supporting the demanding requirements of future autonomous and connected vehicles.Peer ReviewedPostprint (published version

A statistical model of internet traffic.

PhDWe present a method to extract a time series (Number of Active Requests (NAR)) from web cache logs which serves as a transport level measurement of internet traffic. This series also reflects the performance or Quality of Service of a web cache. Using time series modelling, we interpret the properties of this kind of internet traffic and its effect on the performance perceived by the cache user. Our preliminary analysis of NAR concludes that this dataset is suggestive of a long-memory self-similar process but is not heavy-tailed. Having carried out more in-depth analysis, we propose a three stage modelling process of the time series: (i) a power transformation to normalise the data, (ii) a polynomial fit to approximate the general trend and (iii) a modelling of the residuals from the polynomial fit. We analyse the polynomial and show that the residual dataset may be modelled as a FARIMA(p, d, q) process. Finally, we use Canonical Variate Analysis to determine the most significant defining properties of our measurements and draw conclusions to categorise the differences in traffic properties between the various caches studied. We show that the strongest illustration of differences between the caches is shown by the short memory parameters of the FARIMA fit. We compare the differences revealed between our studied caches and draw conclusions on them. Several programs have been written in Perl and S programming languages for this analysis including totalqd.pl for NAR calculation, fullanalysis for general statistical analysis of the data and armamodel for FARIMA modelling

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

[EN] In this paper, an application for the management, supervision and failure forecast of a ship¿s energy storage system is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for the measurement and supervision of the parameters inherent to energy storage and energy supply are reviewed. The importance of energy storage systems in ships, the causes and models of battery aging, types of failures, and predictive diagnosis techniques for valve-regulated lead-acid (VRLA) batteries used for assisted and safe navigation are discussed. In ships, battery banks are installed in chambers that normally do not have temperature regulation and therefore are significantly conditioned by the outside temperature. A specific method based on the analysis of the time-series data of random and seasonal factors is proposed for the comparative trend analyses of both the battery internal temperature and the battery installation chamber temperature. The objective is to apply predictive fault diagnosis to detect any undesirable increase in battery temperature using prior indicators of heat dissipation process failure¿to avoid the development of the most frequent and dangerous failure modes of VRLA batteries such as dry out and thermal runaway. It is concluded that these failure modes can be conveniently diagnosed by easily recognized patterns, obtained by performing comparative trend analyses to the variables measured onboard by NMEA sensors.García Moreno, E.; Quiles Cucarella, E.; Correcher Salvador, A.; Morant Anglada, FJ. (2019). Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis. Sensors. 19(20):1-24. https://doi.org/10.3390/s19204480S1241920Dudojc, B., & Mindykowski, J. (2019). New Approach to Analysis of Selected Measurement and Monitoring Systems Solutions in Ship Technology. Sensors, 19(8), 1775. doi:10.3390/s19081775Khan, M., Swierczynski, M., & Kær, S. (2017). Towards an Ultimate Battery Thermal Management System: A Review. Batteries, 3(4), 9. doi:10.3390/batteries3010009IEEE P1451.6—Proposed Standard for a High-Speed CANopen- Based Transducer Network Interface for Intrinsically Safe and Non-Intrinsically Safe Applications http://grouper.ieee.org/groups/1451/6/Song, E., & Lee, K. (2008). Understanding IEEE 1451-Networked smart transducer interface standard - What is a smart transducer? IEEE Instrumentation & Measurement Magazine, 11(2), 11-17. doi:10.1109/mim.2008.4483728Signal K Signalk.org/overview.htmlLead Acid Battery Working–Lifetime Study http://www.power-thru.com/documents/The%20Truth%20About%20Batteries%20-%20POWERTHRU%20White%20Paper.pdfLee, C.-Y., Peng, H.-C., Lee, S.-J., Hung, I.-M., Hsieh, C.-T., Chiou, C.-S., … Huang, Y.-P. (2015). A Flexible Three-in-One Microsensor for Real-Time Monitoring of Internal Temperature, Voltage and Current of Lithium Batteries. Sensors, 15(5), 11485-11498. doi:10.3390/s150511485Hong, J., Wang, Z., & Liu, P. (2017). Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles. Energies, 10(7), 919. doi:10.3390/en10070919Jouhara, H., Khordehgah, N., Serey, N., Almahmoud, S., Lester, S. P., Machen, D., & Wrobel, L. (2019). Applications and thermal management of rechargeable batteries for industrial applications. Energy, 170, 849-861. doi:10.1016/j.energy.2018.12.218Salameh, Z. M., Casacca, M. A., & Lynch, W. A. (1992). A mathematical model for lead-acid batteries. IEEE Transactions on Energy Conversion, 7(1), 93-98. doi:10.1109/60.124547Copetti, J. B., Lorenzo, E., & Chenlo, F. (1993). A general battery model for PV system simulation. Progress in Photovoltaics: Research and Applications, 1(4), 283-292. doi:10.1002/pip.4670010405Ceraolo, M. (2000). New dynamical models of lead-acid batteries. IEEE Transactions on Power Systems, 15(4), 1184-1190. doi:10.1109/59.898088Chen, M., & Rincon-Mora, G. A. (2006). Accurate Electrical Battery Model Capable of Predicting Runtime and I–V Performance. IEEE Transactions on Energy Conversion, 21(2), 504-511. doi:10.1109/tec.2006.874229Gomadam, P. M., Weidner, J. W., Dougal, R. A., & White, R. E. (2002). Mathematical modeling of lithium-ion and nickel battery systems. Journal of Power Sources, 110(2), 267-284. doi:10.1016/s0378-7753(02)00190-8Zhang, J., & Lee, J. (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 196(15), 6007-6014. doi:10.1016/j.jpowsour.2011.03.101Cho, S., Jeong, H., Han, C., Jin, S., Lim, J. H., & Oh, J. (2012). State-of-charge estimation for lithium-ion batteries under various operating conditions using an equivalent circuit model. Computers & Chemical Engineering, 41, 1-9. doi:10.1016/j.compchemeng.2012.02.003Xu, J., Wang, J., Li, S., & Cao, B. (2016). A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles. Sensors, 16(8), 1328. doi:10.3390/s16081328Osaka, T., Momma, T., Mukoyama, D., & Nara, H. (2012). Proposal of novel equivalent circuit for electrochemical impedance analysis of commercially available lithium ion battery. Journal of Power Sources, 205, 483-486. doi:10.1016/j.jpowsour.2012.01.070Guenther, C., Barillas, J. K., Stumpp, S., & Danzer, M. A. (2012). A dynamic battery model for simulation of battery-to-grid applications. 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). doi:10.1109/isgteurope.2012.6465855Worwood, D., Kellner, Q., Wojtala, M., Widanage, W. D., McGlen, R., Greenwood, D., & Marco, J. (2017). A new approach to the internal thermal management of cylindrical battery cells for automotive applications. Journal of Power Sources, 346, 151-166. doi:10.1016/j.jpowsour.2017.02.023Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013). A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources, 241, 680-689. doi:10.1016/j.jpowsour.2013.05.040Modelisation du Vieillissement et Determination de l’Etat de Sante de Batteries Lithium-Ion pour Application Vehicule Electrique et Hybride https://tel.archives-ouvertes.fr/tel-00957678Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., … Hammouche, A. 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(2013). Parametrisation of the influence of different cycling conditions on the capacity fade and the internal resistance increase for lithium nickel manganese cobalt oxide/graphite cells. Journal of Electroanalytical Chemistry, 707, 110-116. doi:10.1016/j.jelechem.2013.08.032Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008). Prognostics in Battery Health Management. IEEE Instrumentation & Measurement Magazine, 11(4), 33-40. doi:10.1109/mim.2008.4579269Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013). Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. Journal of Power Sources, 239, 680-688. doi:10.1016/j.jpowsour.2012.11.146Zou, Y., Hu, X., Ma, H., & Li, S. E. (2015). Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. 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Revision 1.5 https://www.maretron.com/support/manuals/NBE100UM_1.0.htmlOneNet https://www.nmea.org/content/STANDARDS/OneNetMaretron Press Kit https://www.maretron.com/company/presskit.phpDCM100 User’s Manual https://www.maretron.com/support/manuals/DCM100UM_1.5.htmlAirmar Technology Corporation www.airmar.comGarcía, E., Quiles, E., Correcher, A., & Morant, F. (2018). Sensor Buoy System for Monitoring Renewable Marine Energy Resources. 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Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 196(23), 10314-10321. doi:10.1016/j.jpowsour.2011.08.04

ANALYSIS OF DATA & COMPUTER NETWORKS IN STUDENTS' RESIDENTIAL AREA IN UNIVERSITI TEKNOLOGI PETRONAS

In Universiti Teknologi Petronas (UTP), most of the students depend on the Internet and computer network connection to gain academics information and share educational resources. Even though the Internet connections and computers networks are provided, the service always experience interruption, such as slow Internet access, viruses and worms distribution, and network abuse by irresponsible students. Since UTP organization keeps on expanding, the need for a better service in UTP increases. Several approaches were put into practice to address the problems. Research on data and computer network was performed to understand the network technology applied in UTP. A questionnaire forms were distributed among the students to obtain feedback and statistical data about UTP's network in Students' Residential Area. The studies concentrate only on Students' Residential Area as it is where most of the users reside. From the survey, it can be observed that 99% of the students access the network almost 24 hours a day. In 2005, the 2 Mbps allocated bandwidth was utilized 100% almost continuously but in 2006, the bottleneck of Internet access has reduced significantly since the bandwidth allocated have been increased to 8 Mbps. Server degradation due to irresponsible acts by users also adds burden to the main server. In general, if the proposal to ITMS (Information Technology & Media Services) Department for them to improve their Quality of Service (QoS) and established UTP Computer Emergency Response Team (UCert), most of the issues addressed in this report can be solved