|
|
The Use of ADS-B Information for Airborne Separation and Collision Avoidance
Jošth Adamová, Eva ; Jonák, Jaroslav (referee) ; Novák, Andrej (referee) ; Vosecký, Slavomír (advisor)
Otázka bezpečnosti všeobecného letectva bola vždy problematická. Nedostatok harmonizovaných technických noriem týkajúcich sa výkonu zariadení umožňujúcich lietadlám všeobecného letectva vidieť a byť videný, sa stal hlavnou prekážkou k ich rozšírenému používaniu v Európe. Rastúca komplexita a hustota leteckej dopravy, a skutočnosť, že sa vzdušný priestor s príchodom nových užívateľov (napr. systémov bezpilotných lietadiel, ktoré zaznamenávajú v posledných rokoch rastúci trend) stále viac naplňuje, zdôrazňujú dôležitosť a potrebu zmeny. Cieľom tejto dizertačnej práce bolo rozpracovať možnosti zlepšenia prevádzkovej bezpečnosti všeobecného letectva v neriadenom vzdušnom priestore, s prihliadnutím na značné výzvy spojené so zavádzaním bezpilotných lietadiel do vzdušného priestoru. S celkovým rámcom ATM prispôsobujúcim sa týmto novým užívateľom vzdušného priestoru, sa ADS-B technológia so svojim potenciálom stáva významným činiteľom. Táto práca skúmala možnosti zlepšenia kooperatívnej “surveillance” v neriadenom vzdušnom priestore (začínajúc od, ale neobmedzujúc sa na ADS-B) a prostredníctvom súboru experimentov hodnotila prijateľnosť, uskutočniteľnosť, a opätovnú použiteľnosť rôznych existujúcich antikolíznych systémov a “situational awareness” systémov, či už šitých na mieru pre všeobecné letectvo alebo nie. Súčasťou výskumu bolo aj skúmanie možného prispôsobenia konceptu “Remain Well Clear” vyvíjaného pre drony, prevádzkovým potrebám všeobecného letectva. Výskumné aktivity v rámci tejto dizertačnej práce prebiehali v dvoch fázach. V rámci prvej fázy, ktorá trvala od roku 2015 do roku 2019, sa uskutočnila séria experimentov. Druhá fáza sa zamerala na prehľadnú analýzu systémov zavedených od posledného experimentu, ktorá vyvrcholila v posledných mesiacoch. Práca zdôrazňuje riešenia, ktoré s vhodnými úpravami majú potenciál byť efektívne prispôsobené na používanie vo všeobecnom letectve.
|
|
|
Similarity Search in Network Security Alerts
Štoffa, Imrich ; Kučera, Jan (referee) ; Žádník, Martin (advisor)
Network monitoring systems generate a high number of alerts reporting on anomalies and suspicious activity of IP addresses. From a huge number of alerts, only a small fraction is high priority and relevant from human evaluation. The rest is likely to be neglected. Assume that by analyzing large sums of these low priority alerts we can discover valuable information, namely, coordinated IP addresses and type of alerts likely to be correlated. This knowledge improves situational awareness in the field of network monitoring and reflects the requirement of security analysts. They need to have at their disposal proper tools for retrieving contextual information about events on the network, to make informed decisions. To validate the assumption new method is introduced to discover groups of coordinated IP addresses that exhibit temporal correlation in the arrival pattern of their events. The method is evaluated on real-world data from a sharing platform that accumulates 2.2 million alerts per day. The results show, that method indeed detected truly correlated groups of IP addresses.
|
|
|
Profiling of Network Entities to Improve Situational Awareness
Bolf, René ; Tisovčík, Peter (referee) ; Žádník, Martin (advisor)
Having a good situational awareness is an important part of computer security. Knowing what is connected to the network, where it is located, and who is communicating can help make better and faster decisions when security incidents occur. This thesis is focusing on the profiling of network entities at the device level. More specifically, it focuses on the passive identification of operating systems. Every packet transferred in the network carries a specific information in its packet header that reflects the initial settings of a host's operating system. The set of these information is called the "fingerprint" of an operating system. In the thesis, there is described an implementation of a machine learning classifier using the decision tree method, which uses features from TCP and IP headers. The classifier was evaluated on a data set containing data from real network traffic and has achieved accuracy of 96 % when classifying into 9 classes of operating systems.
|
|
|
The Proposal for the Strategic Development of a Startup Operating in the Field of Online Media on the Czech Market
Oulehla, Radim ; Kaňovská, Lucie (referee) ; Bumberová, Veronika (advisor)
The thesis deals with the proposal of a strategy of a start-up operating in the field of online media on the Czech market. The first part of the thesis explains to the general concepts related to strategic management, selected analyses of the external and internal environment of the company. The analytical part of the thesis is devoted to the application of selected methods, identification of the external and internal environment of the startup and situational assessment. The findings from the analytical part of the thesis are then used to develop an appropriate strategy proposal that leads to increased sales and the creation of a stronger community of loyal customers.
|
|
|
The Proposal for the Strategic Development of a Startup Operating in the Field of Online Media on the Czech Market
Oulehla, Radim ; Kaňovská, Lucie (referee) ; Bumberová, Veronika (advisor)
The thesis deals with the proposal of a strategy of a start-up operating in the field of online media on the Czech market. The first part of the thesis explains to the general concepts related to strategic management, selected analyses of the external and internal environment of the company. The analytical part of the thesis is devoted to the application of selected methods, identification of the external and internal environment of the startup and situational assessment. The findings from the analytical part of the thesis are then used to develop an appropriate strategy proposal that leads to increased sales and the creation of a stronger community of loyal customers.
|
|
|
Profiling of Network Entities to Improve Situational Awareness
Bolf, René ; Tisovčík, Peter (referee) ; Žádník, Martin (advisor)
Having a good situational awareness is an important part of computer security. Knowing what is connected to the network, where it is located, and who is communicating can help make better and faster decisions when security incidents occur. This thesis is focusing on the profiling of network entities at the device level. More specifically, it focuses on the passive identification of operating systems. Every packet transferred in the network carries a specific information in its packet header that reflects the initial settings of a host's operating system. The set of these information is called the "fingerprint" of an operating system. In the thesis, there is described an implementation of a machine learning classifier using the decision tree method, which uses features from TCP and IP headers. The classifier was evaluated on a data set containing data from real network traffic and has achieved accuracy of 96 % when classifying into 9 classes of operating systems.
|
|
|
Similarity Search in Network Security Alerts
Štoffa, Imrich ; Kučera, Jan (referee) ; Žádník, Martin (advisor)
Network monitoring systems generate a high number of alerts reporting on anomalies and suspicious activity of IP addresses. From a huge number of alerts, only a small fraction is high priority and relevant from human evaluation. The rest is likely to be neglected. Assume that by analyzing large sums of these low priority alerts we can discover valuable information, namely, coordinated IP addresses and type of alerts likely to be correlated. This knowledge improves situational awareness in the field of network monitoring and reflects the requirement of security analysts. They need to have at their disposal proper tools for retrieving contextual information about events on the network, to make informed decisions. To validate the assumption new method is introduced to discover groups of coordinated IP addresses that exhibit temporal correlation in the arrival pattern of their events. The method is evaluated on real-world data from a sharing platform that accumulates 2.2 million alerts per day. The results show, that method indeed detected truly correlated groups of IP addresses.
|
guest ::
