Värdeflödesanalys och Data Envelopment Analysis : En fallstudie för att värdera effektiviteten hos ett logistikföretag

Companies face new challenges almost every day. In order to stay competitive, it is important that companies strive for continuous development and improvement. By describing companies through their processes it is possible to get a clear overview of the entire operation, which can contribute, to a well-established overall understanding of the company. This is a case study based on Stort AB which is a small logistics company specialized in international transportation and logistics solutions. The purpose of this study is to perform value stream mapping in order to create a more efficient production process and propose possible improvements in order to reduce processing time. After performing value stream mapping, data envelopment analysis is used to calculate how lean Stort AB is today and how lean the company can become by implementing the proposed improvements. The results show that the production process can improve efficiency by minimizing waste produced by a bad workplace layout and over-processing. The authors suggested solution is to introduce standardized processes and invest in technical instruments in order to automate the process to reduce process time. According to data envelopment analysis the business is 41 percent lean at present and may soon become 55 percent lean and finally reach an optimum 100 percent lean mode if the process is automated.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Logghantering : En undersökning av logghantering och logghanteringssystem

This research includes a review of the log management of the company Telia. The research has also included a comparison of the two log management sys- tems Splunk and ELK. The review of the company’s log management shows that log messages are being stored in files on a hard drive that can be accessed through the network. The log messages are system-specific. ELK is able to fetch log messages of different formats simultaneously, but this feature is not possible in Splunk where the process of uploading log messages has to be re- peated for log messages that have different formats. Both systems store log messages through a file system on a hard drive, where the systems are installed. In networks that involve multiple servers, ELK is distributing the log messages between the servers. Thus, the workload to perform searches and storing large amounts of data is reduced. Using Splunk in networks can also reduce the workload. This is done by using forwarders that send the log messages to one or multiple central servers which stores the messages. Searches of log messages in Splunk are performed by using a graphical interface. Searches in ELK is done by using a REST-API which can be used by external systems as well, to retrieve search results. Splunk also has a REST-API that can be used by external sys- tems to receive search results. The research revealed that ELK had a lower search time than Splunk. However, no method was found that could be used to measure the indexing time of ELK, which meant that no comparison could be made with respect to the indexing time for Splunk. For future work there should be an investigation whether there is any possibility to measure the indexing time of ELK. Another recommendation is to include more log management sys- tem in the research to improve the results that may be suitable candidates for the company Telia. An improvement suggestion as well, is to do performance tests in a network with multiple servers and thereby draw conclusions how the performance is in practice.