SIEM Network Behaviour Monitoring Framework using Deep Learning Approach for Campus Network Infrastructure

One major problem faced by network users is an attack on the security of the network especially if the network is vulnerable due to poor security policies. Network security is largely an exercise to protect not only the network itself but most importantly, the data. This exercise involves hardware and software technology. Secure and effective access management falls under the purview of network security. It focuses on threats both internally and externally, intending to protect and stop the threats from entering or spreading into the network. A specialized collection of physical devices, such as routers, firewalls, and anti-malware tools, is required to address and ensure a secure network. Almost all agencies and businesses employ highly qualified information security analysts to execute security policies and validate the policies’ effectiveness on regular basis. This research paper presents a significant and flexible way of providing centralized log analysis between network devices. Moreover, this paper proposes a novel method for compiling and displaying all potential threats and alert information in a single dashboard using a deep learning approach for campus network infrastructure

Topological Data Analysis for Enhancing Embedded Analytics for Enterprise Cyber Log Analysis and Forensics

Forensic analysis of logs is one responsibility of an enterprise cyber defense team; inherently, this is a big data task with thousands of events possibly logged in minutes of activity. Logged events range from authorized users typing incorrect passwords to malignant threats. Log analysis is necessary to understand current threats, be proactive against emerging threats, and develop new firewall rules. This paper describes embedded analytics for log analysis, which incorporates five mechanisms: numerical, similarity, graph-based, graphical analysis, and interactive feedback. Topological Data Analysis (TDA) is introduced for log analysis with TDA providing novel graph-based similarity understanding of threats which additionally enables a feedback mechanism to further analyze log files. Using real-world firewall log data from an enterprise-level organization, our end-to-end evaluation shows the effective detection and interpretation of log anomalies via the proposed process, many of which would have otherwise been missed by traditional means

Marketing Aspects of Technology Ventures

Cílem diplomové práce je analýza marketingových nástrojů použitých firmou XAX a následně vyhodnotit a navrhnout zvýšení jejich efektivity. Popis strategie společnosti a faktory ovlivňující budou identifikovány. Práce obsahuje návrhy a doporučení na zvýšení efektivity marketingových nástrojů dané firmy v oblasti High-tech odvětví.The aim of diploma thesis is to analyze marketing tools used in Company XAX and under this condition evaluate and purpose increase efficiency used tools. The current marketing strategy of the company is described and main influencing factors are identified. The thesis contains proposals and recommendations for tools usage in the field of High-tech marketing.

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Augmenting security event information with contextual data to improve the detection capabilities of a SIEM

The increasing number of cyber security breaches have revealed a need for proper cyber security measures. The emergence of the internet and the increase in overall connectivity means that data is more easily accessible and available. Using the available data in a security context may provide a system with an external contextual insight such as environmental awareness or current affair awareness. A security information and event management (SIEM) system is a security system that correlates security event information from surrounding systems and decides whether the surrounding environment (possibly an enterprise's network) is vulnerable or even under attack by a malicious person whether they be internal (authorised) or external (unauthorised). In this thesis, the aim is to provide such a system with con- text by adding non-security related information from surrounding available sources known as context information feeds. Contextual information feeds are added to the SIEM and tested using randomised events. There are various context information types used in this thesis, namely: social media, meteorological, calendar information and terror threat level. The SIEM is tested with each contextual data feed active and the results are recorded. The testing shows that the addition of contextual data feeds actively affects the sensitivity of OSSIM and hence results in higher alarms raised during elevated context triggered states. The system showed a greater and deeper visibility of its surrounding environment and hence an improved detection capability

Моделирование идентификации профиля кибератак на основе анализа поведения устройств в сети провайдера телекоммуникационных услуг

There are currently many threats to network security. This is especially true for telecom operators and telecommunication service providers, which are a key link in the data transmission infrastructure for any company. To ensure the protection of their infrastructure and cloud services provided to end-users, telecom operators have to use non-trivial solutions. At the same time, the accuracy of defining attacks by security systems is not the least. In the framework of this study, an approach was developed and attack detection was modeled based on the analysis of state chains of network nodes. The proposed approach allows the comparison of events occurring in the network with events recorded by intrusion detection systems. In our study, we solve the problem of formalizing a typical attack profile in a network of telecommunication service providers by constructing a sequence of transitions of states of network nodes and the time of the state change of individual devices under study. The study covers the most popular types of attacks. To formalize the rules for classifying states, the study uses a decision tree algorithm to build a chain of security events. In the experimental part of the study, the accuracy of the classification of known types of attacks recorded in security event logs using ROC analysis was assessed. The results obtained made it possible to evaluate the effectiveness of the developed model for recognizing network attacks in the infrastructure of telecommunication service providers. The experimental results show fairly high accuracy in determining the popular type of attack. This will also help in the future to reduce the response time to security incidents in a large network, due to earlier detection of illegitimate behavior.В настоящее время существует множество угроз сетевой безопасности. Это особенно актуально для операторов связи и провайдеров телекоммуникационных услуг, являющихся ключевым звеном инфраструктуры передачи данных для любой компании. Для обеспечения защиты собственной инфраструктуры и облачных сервисов, предоставляемых конечным пользователям, операторам связи приходится применять нетривиальные решения. При этом не последнее место занимает точность определения атак системами безопасности. В рамках настоящего исследования разработан подход и проведено моделирование обнаружения атак на основе анализа цепочек состояний сетевых узлов. Предложенный подход позволяет осуществлять сопоставление событий, происходящих в сети, с событиями, фиксируемыми системами обнаружения вторжений. В нашем исследовании мы решаем проблему формализации типичного профиля атаки в сети провайдеров телекоммуникационных услуг путем построения последовательности переходов состояний узлов сети и времени изменения состояния отдельных исследуемых устройств. Исследование затрагивает наиболее популярные типы атак. Для формализации правил классификации состояний в исследовании используется алгоритм дерева решений для построения цепочки событий безопасности. В экспериментальной части исследования проведена оценка точности классификации известных типов атак, зафиксированных в журналах событий безопасности с использованием ROC-анализа. Полученные результаты позволили оценить эффективность разработанной модели для распознавания сетевых атак в инфраструктуре провайдеров телекоммуникационных услуг. Экспериментальные результаты показывают достаточно высокую точность определения популярного типа атаки. Это позволит в будущем также сократить время реагирования на инциденты безопасности в большой сети за счет более раннего обнаружения нелегитимного поведения

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Security Analysis of System Behaviour - From "Security by Design" to "Security at Runtime" -

The Internet today provides the environment for novel applications and processes which may evolve way beyond pre-planned scope and purpose. Security analysis is growing in complexity with the increase in functionality, connectivity, and dynamics of current electronic business processes. Technical processes within critical infrastructures also have to cope with these developments. To tackle the complexity of the security analysis, the application of models is becoming standard practice. However, model-based support for security analysis is not only needed in pre-operational phases but also during process execution, in order to provide situational security awareness at runtime. This cumulative thesis provides three major contributions to modelling methodology. Firstly, this thesis provides an approach for model-based analysis and verification of security and safety properties in order to support fault prevention and fault removal in system design or redesign. Furthermore, some construction principles for the design of well-behaved scalable systems are given. The second topic is the analysis of the exposition of vulnerabilities in the software components of networked systems to exploitation by internal or external threats. This kind of fault forecasting allows the security assessment of alternative system configurations and security policies. Validation and deployment of security policies that minimise the attack surface can now improve fault tolerance and mitigate the impact of successful attacks. Thirdly, the approach is extended to runtime applicability. An observing system monitors an event stream from the observed system with the aim to detect faults - deviations from the specified behaviour or security compliance violations - at runtime. Furthermore, knowledge about the expected behaviour given by an operational model is used to predict faults in the near future. Building on this, a holistic security management strategy is proposed. The architecture of the observing system is described and the applicability of model-based security analysis at runtime is demonstrated utilising processes from several industrial scenarios. The results of this cumulative thesis are provided by 19 selected peer-reviewed papers

Federated Agentless Detection of Endpoints Using Behavioral and Characteristic Modeling

During the past two decades computer networks and security have evolved that, even though we use the same TCP/IP stack, network traffic behaviors and security needs have significantly changed. To secure modern computer networks, complete and accurate data must be gathered in a structured manner pertaining to the network and endpoint behavior. Security operations teams struggle to keep up with the ever-increasing number of devices and network attacks daily. Often the security aspect of networks gets managed reactively instead of providing proactive protection. Data collected at the backbone are becoming inadequate during security incidents. Incident response teams require data that is reliably attributed to each individual endpoint over time. With the current state of dissociated data collected from networks using different tools it is challenging to correlate the necessary data to find origin and propagation of attacks within the network. Critical indicators of compromise may go undetected due to the drawbacks of current data collection systems leaving endpoints vulnerable to attacks. Proliferation of distributed organizations demand distributed federated security solutions. Without robust data collection systems that are capable of transcending architectural and computational challenges, it is becoming increasingly difficult to provide endpoint protection at scale. This research focuses on reliable agentless endpoint detection and traffic attribution in federated networks using behavioral and characteristic modeling for incident response

Performance Metrics for Network Intrusion Systems

Intrusion systems have been the subject of considerable research during the past 33 years, since the original work of Anderson. Much has been published attempting to improve their performance using advanced data processing techniques including neural nets, statistical pattern recognition and genetic algorithms. Whilst some significant improvements have been achieved they are often the result of assumptions that are difficult to justify and comparing performance between different research groups is difficult. The thesis develops a new approach to defining performance focussed on comparing intrusion systems and technologies. A new taxonomy is proposed in which the type of output and the data scale over which an intrusion system operates is used for classification. The inconsistencies and inadequacies of existing definitions of detection are examined and five new intrusion levels are proposed from analogy with other detection-based technologies. These levels are known as detection, recognition, identification, confirmation and prosecution, each representing an increase in the information output from, and functionality of, the intrusion system. These levels are contrasted over four physical data scales, from application/host through to enterprise networks, introducing and developing the concept of a footprint as a pictorial representation of the scope of an intrusion system. An intrusion is now defined as “an activity that leads to the violation of the security policy of a computer system”. Five different intrusion technologies are illustrated using the footprint with current challenges also shown to stimulate further research. Integrity in the presence of mixed trust data streams at the highest intrusion level is identified as particularly challenging. Two metrics new to intrusion systems are defined to quantify performance and further aid comparison. Sensitivity is introduced to define basic detectability of an attack in terms of a single parameter, rather than the usual four currently in use. Selectivity is used to describe the ability of an intrusion system to discriminate between attack types. These metrics are quantified experimentally for network intrusion using the DARPA 1999 dataset and SNORT. Only nine of the 58 attack types present were detected with sensitivities in excess of 12dB indicating that detection performance of the attack types present in this dataset remains a challenge. The measured selectivity was also poor indicting that only three of the attack types could be confidently distinguished. The highest value of selectivity was 3.52, significantly lower than the theoretical limit of 5.83 for the evaluated system. Options for improving selectivity and sensitivity through additional measurements are examined.Stochastic Systems Lt