京都大学 / 新制・課程博士 / 博士(医学) / 甲第23381号 / 医博第4750号 / 新制||医||1052(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 河本 宏, 教授 濵﨑 洋子, 教授 上野 英樹 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

This master's thesis is meant to provide techniques in designing and building a standalone packet filtering firewall in Linux machines, mainly for small sites who don’t give much service to Internet users. It deals with attenuating the effect of the most common types of attacks using iptables. It guides how to design, implement, run, and maintain Firewall. Techniques for continuously monitoring attacks is attempted. It also give a historical, architectural and technical overview of firewalls and security attacks.

Master's thesis is focused on intrusion detection for denied of service attacks. These distributed DoS attacks are threat for all users on the Internet, so there is deployment of intrusion detection and intrusion prevention systems against these attacks. The theoretical part describes the DoS attacks and its variants used most frequently. It also mentioned variants for detecting DoS attacks. There is also described, which tools are used to detect DDoS attacks most frequently. The practical part deals with the deployment of software tools for detecting DDoS attacks, and create traffic to test detection abilities of these tools.

This diploma thesis deals with issue of IDS/IPS systems and possibilities of their utilization within the university network of Mendel University in Brno. The thesis includes a description how to install and configure Snort IDS, including addon modules based on predefined parameters and the ability to detect malious traffic within college computer network of Mendel University in Brno. The results include verification of correct detection of selected attack types and the discussion of False Positive and False Negative.

This thesis elaborates on the design process of a mobile phone based application for indoor positioning at workplaces. The aim of the application is to ease workflow management and help increase the work efficiency of individuals and teams by reducing the amount of time spent in looking and waiting for each other. In doing so, the research takes a closer look on the user’s perspective on sharing one’s location data. An attempt is made to explore users’ behavior, investigating if any privacy concerns arise out of sharing one’s indoor location data and how it effects the adoption of the service within the context of a workspace. This exploratory approach employed a number of qualitative tools in order to gather data and analyze it. In order to understand the complex context of a work environment where activities (or actions) are defined by a number of factors, actors, mediators, communication channels, etc., the research followed an activity centred approach. The resulting solution is in the form of a service that provides layers of contextual information, responding to the overall activity being performed and the smaller actions that constitute it. A prototype of this application is then taken for user testing. The test results show that the users were hesitant in sharing their location data; citing a number of speculated scenarios where this information may be used in ways that induced a sense of being spied upon. However, in the overall acceptance and adoption of the system, the context of use (the workspace) was found to play a very crucial role.

Background. Security Information and Event Management (SIEM) systems today are sophisticated sets of software packages combined with hardware platforms, which can perform real-time analysis on security events and can respond to them before potential damage due to the actions of intruders. A huge number of systems rely on the continuous transmission of data through computer networks. Nowadays it is difficult to imagine a sphere of human activity that would not be affected by information technologies and would not use computer networks. Along with the means of protecting information, the technologies that are used by cybercriminals to achieve their goals are also improving. Moreover, the so-called insiders - information security perpetrators inside the protected perimeter, who can cause much more damage by their actions, as they are among the legitimate users and can have access to more confidential information - are becoming a growing threat. Objectives. To identify insider activity within an acceptable time inside the network, we need to develop a methodology to detect abnormal activity within the network using advanced data processing techniques, based on machine learning. After recreating the data processing system, we will also need to determine the most efficient algorithm that can be applied to the task of insider detection. Methods. The work analyzed research papers with similar objectives to investigate methods and technologies for securing against intruder intrusions, in conjunction with a study of machine learning techniques for detecting anomalies in the data. Experimental data were also collected containing information about network activity within the same network over two weeks. With this data, it is possible to conduct an experiment in network traffic processing using state-of-the-art technology. Results. During the study of relevant works, several effective ways to detect anomalies in the data were identified, technologies for processing large amounts of data using NoSQL were studied, and work on creating an experimental bench was performed. As a result, the experimental data obtained was sufficient to verify the effectiveness of the obtained solution. Conclusions. As a result, we analyzed existing approaches to detect insider activity within a computer system. Algorithms based on machine learning and big data processing methods were evaluated. In addition, a model for representing big data in NoSQL format was developed, which made it possible to create an architecture of a system for detecting insiders in computer networks using a graph database and machine learning methods.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23604号 / 医博第4791号 / 新制||医||1055(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 戸口田 淳也, 教授 河本 宏, 教授 江藤 浩之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(医科学) / 甲第23611号 / 医科博第134号 / 新制||医科||9(附属図書館) / 京都大学大学院医学研究科医科学専攻 / (主査)教授 遊佐 宏介, 教授 小川 誠司, 教授 山中 伸弥 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第19899号 / 農博第2182号 / 新制||農||1043(附属図書館) / 学位論文||H28||N5003(農学部図書室) / 32976 / 京都大学大学院農学研究科応用生物科学専攻 / (主査)教授 今井 裕, 教授 久米 新一, 教授 廣岡 博之 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20673号 / 医博第4283号 / 新制||医||1024(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 長船 健二, 教授 横出 正之, 教授 山下 潤 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM