Design and management Startegy of E-commerce

Tato bakalářská práce se zabývá možnostmi realizace a vylepšení elektronického obchodu a vytvářením modelu strategického řízení elektronického obchodu, který mohou využít jak začínající firmy, tak společnosti které elektronický obchod již využívají, ale plánují jej nadále rozvíjet.This bachelor’s thesis deals with possibilities of realization and improvement of e-commerce and with creation of management strategy of e-commerce which can be used by starting companies as well as companies which already have e-shop but are planning to develop it.

Graph-based Network Traffic Analysis for Incident Investigation

In this presentation, we introduce a new approach to analyzing network traffic data using associations. In the beginning, we discuss the benefits and issues of currently used analysis tools. Next, we propose a new data representation model and utilization of a graph database to store such data. In the main part of the presentation, we introduce the Granef toolkit and its use for incident investigation

Trace-Share: Towards Provable Network Traffic Measurement and Analysis

Research in network traffic measurement and analysis is a long-lasting field with growing interest from both scientists and the industry. However, even after so many years, results replication, criticism, and review are still rare. The aim of our research is to overcome the mentioned controversy with focus on the whole issue covering all areas of data anonymization, authenticity, recency, publicity, and their usage for research provability. We believe that these challenges can be solved by utilization of semi-labeled datasets composed of real-world network traffic and annotated units with interest-related packet traces only

Toward Graph-Based Network Traffic Analysis and Incident Investigation

Even though network traffic is typically encrypted, and it is almost impossible to look into the content of transmitted data, the analysis of metadata and characteristics of individual connections still plays an essential role in an incident or criminal investigation. In recent years, we have seen a significant development of various approaches for storing and analyz-ing large-scale data, including graph databases. Such an approach offers great potential for expert analysts performing digital forensics and network traffic investigation, as it corresponds to their natural perception of the data. In addition, it allows a simple connection of different types and sources of data, which represents the primary focus of our research

Incident Investigation: From Packets to Graph-Based Analysis

Analysis of network traffic allows us to explore events in the monitored network (even retrospectively). It benefits from the fact that it is almost impossible to maliciously affect the captured data (as opposed to system logs, for example). Therefore, it is a reliable source that suitably complements cyber incident investigation. The analysis of network traffic is currently performed by the use of tools such as Wireshark or Arkime, which allow manual data browsing, filtering, aggregation, and provide interactive visualizations but don't account for the fact that the human brain perceives the data as associations/graphs. This interactive keynote will show you how network traffic is typically analyzed today and how it can be adapted to human thinking by using a graph database. In the introductory part, you will see what a typical network attack looks like, how it can be analyzed using Wireshark, and what the advantages and disadvantages of today's analysis techniques are. We will then show you how to transform network data into a format suitable for a graph database while at the same time preserving the natural perception of network traffic. In the final part of the keynote, we will introduce the Granef toolkit (https://granef.csirt.muni.cz/) and use it to analyze the given data. Through simple tutorial exercises, participants will have the opportunity to explore graph-based analysis on their own and gain new insights into network traffic data

GRANEF: Utilization of a Graph Database for Network Forensics

Understanding the information in captured network traffic, extracting the necessary data, and performing incident investigations are principal tasks of network forensics. The analysis of such data is typically performed by tools allowing manual browsing, filtering, and aggregation or tools based on statistical analyses and visualizations facilitating data comprehension. However, the human brain is used to perceiving the data in associations, which these tools can provide only in a limited form. We introduce a GRANEF toolkit that demonstrates a new approach to exploratory network data analysis based on associations stored in a graph database. In this article, we describe data transformation principles, utilization of a scalable graph database, and data analysis techniques. We then discuss and evaluate our proposed approach using a realistic dataset. Although we are at the beginning of our research, the current results show the great potential of association-based analysis

Stream-Based IP Flow Analysis

As the complexity of Internet services, transmission speed, and data volume increases, current IP flow monitoring and analysis approaches cease to be sufficient, especially within high-speed and large-scale networks. Although IP flows consist only of selected network traffic features, their processing faces high computational demands, analysis delays, and large storage requirements. To address these challenges, we propose to improve the IP flow monitoring workflow by stream-based collection and analysis of IP flows utilizing a distributed data stream processing. This approach requires changing the paradigm of IP flow data monitoring and analysis, which is the main goal of our research. We analyze distributed stream processing systems, for which we design a novel performance benchmark to determine their suitability for stream-based processing of IP flow data. We define a stream-based workflow of IP flow collection and analysis based on the benchmark results, which we also implement as a publicly available and open-source framework Stream4Flow. Furthermore, we propose new analytical methods that leverage the stream-based IP flow data processing approach and extend network monitoring and threat detection capabilities

Real-time Analysis of NetFlow Data for Generating Network Traffic Statistics using Apache Spark

Abstract—In this paper, we present a framework for the realtime generation of network traffic statistics on Apache Spark Streaming, a modern distributed stream processing system. Our previous results showed that stream processing systems provide enough throughput to process a large volume of NetFlow data and hence they are suitable for network traffic monitoring. This paper describes the integration of Apache Spark Streaming into a current network monitoring architecture. We prove that it is possible to implement the same basic methods for NetFlow data analysis in the stream processing framework as in the traditional ones. Moreover, our stream processing implementation discovers new information which is not available when using traditional network monitoring approaches

Knowledge of Marketing for E-business

Diplomová práce se zabývá problematikou současných možností elektronického marketingu a způsobů jejich využití. Práce rozebírá metody elektronického marketingu a řeší problematiku plánování internetové marketingové strategie včetně možností měření její úspěšnosti.The focus of master’s thesis is to examine current possibilities of electronic marketing and means of their utilisation. The thesis analyses various methods of internet marketing and process of planning e-marketing strategy, including the possibilities of measuring its success.

On Information Value of Top N Statistics

In the era of Internet of Things (IoT), the volume of the monitored data from IoT network is enormous. However, not all data provide sufficient or relevant information. Since the analysis of big data is both resource and time exhausting, only relevant information should be analysed. In this paper, we scrutinize the widely used Top N statistics and evaluate its information value with respect to gathering information about individual hosts in the network. All theoretical discussions are evaluated on the real-world data. Moreover, we provide an assessment of statistic's suitability for identifying a host in network traffic. The results of the paper should assist data analyst of IoT network data