Essays about: "Computer Network Security."

Showing result 1 - 5 of 117 essays containing the words Computer Network Security..

  1. 1. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Javier Ferre Martin; [2023]
    Keywords : Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

    Abstract : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. READ MORE

  3. 2. Implicit Message Integrity Provision : In Heterogeneous Vehicular Systems

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Paul Molloy; [2023]
    Keywords : Privacy; Code Generation; Vehicle-to-infrastructure; Vehicular ad hoc Networks; Standardization; Remote Procedure Calls; Safety; Integritet; Kodgenerering; Fordon-till-infrastruktur; Ad hoc-nät för Fordon; Standardisering; Samtal om fjärrprocedur; Säkerhet;

    Abstract : Vehicles on the road today are complex multi-node computer networks. Security has always been a critical issue in the automotive computing industry. It is becoming even more crucial with the advent of autonomous vehicles and driver assistant technology. There is potential for attackers to control vehicles maliciously. READ MORE

  4. 3. Intrusion Detection systems : A comparison in configuration and implementation between OSSEC and Snort

    University essay from Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)

    Author : Peter Stegeby; [2023]
    Keywords : Intrusion detection; HIDS; NIDS; Signature-based; Linux; Windows; Sniffing packets; Upptäcka intrång; HIDS; NIDS; Signatur-baserad; Linux; Windows; Paketsniffing.;

    Abstract : Hackare fortsätter att bli bättre på att få otillåten tillgång till våra datorer och kan undvika de mest grundläggande intrångsskyddade system och brandväggar på en standarddator. Då numren av intrång växer varje år och kostar företag miljoner av dollar, så verkar gapet mellan attackerare och försvarare att bli större. READ MORE

  5. 4. Cyber Threat Detection using Machine Learning on Graphs : Continuous-Time Temporal Graph Learning on Provenance Graphs

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jakub Reha; [2023]
    Keywords : Graph neural networks; Temporal graphs; Benchmark datasets; Anomaly detection; Heterogeneous graphs; Provenance graphs; Grafiska neurala nätverk; temporala grafer; benchmark-datauppsättningar; anomalidetektering; heterogena grafer; härkomstgrafer;

    Abstract : Cyber attacks are ubiquitous and increasingly prevalent in industry, society, and governmental departments. They affect the economy, politics, and individuals. READ MORE

  6. 5. Hidden Markov Models for Intrusion Detection Under Background Activity

    University essay from KTH/Matematisk statistik

    Author : Robert Siridol-Kjellberg; [2023]
    Keywords : Hidden Markov models; Cyber security; Intrusion detection; Clustering; Background subtraction; Dolda Markovmodeller; Cybersäkerhet; Dataintrång; Klustring; Bakgrundssubtraktion;

    Abstract : Detecting a malicious hacker intruding on a network system can be difficult. This challenge is made even more complex by the network activity generated by normal users and by the fact that it is impossible to know the hacker’s exact actions. READ MORE