Review of popular word embedding models for event log anomaly detection purposes

System logs are the diagnostic window to the state of health of the server. Logs are collected to files from which system administrators can monitor the status and events in the server. The logs are usually unstructured textual messages which are difficult to go through manually, because of the ever-growing data. Natural language processing contains different styles and techniques for a computer to interpret textual data. Word2vec and fastText are popular word embedding methods which project words to vectors of real numbers. Doc2vec is the equivalent for paragraphs and it is an extension to Word2vec. With these embedding models I will attempt to create an anomaly detector to assist the log monitoring task. For the actual anomaly detection, I will utilize Independent component analysis (ICA), Hidden Markov Model (HMM) and Long short-term memory to dig deeper in to the vectorized event log messages. The embedding models are then reviewed for their performance in this task. The results of this study show that there is no clear difference between the success of Word2vec and fastText, but it seems that Doc2vec does not work well with the short messages the event logs contain. The anomaly detector would still need some tuning in order to work reliably in production, but it is a decent attempt to achieve useful tool for event log analysing

Developing a log file analysis tool:a machine learning approach for anomaly detection

Abstract. Log files, which record information about all events during the execution of a software, are important in troubleshooting tasks. However, modern software systems produce large quantities of complex logs, and their manual inspection is laborious and time-consuming. Therefore, technologies such as machine learning have been used to automate log file analysis. Anomaly detection is an especially popular approach, since anomalies in the log files are typically caused by erroneous behaviour of the software. In this study, open source data mining and machine learning solutions are utilized to process log files collected from devices running embedded Linux. Following the Design Science Research methodology, a Python program called sgologs is developed. The tool uses components from logparser and loglizer toolkits to pre-process the input log file, train an unsupervised machine learning model, and detect anomalies on the input file. The loglizer tools have not been used with Linux logs in previous research, possibly because they are rather difficult for automated processing. This finding is verified in this study as well, as the measured anomaly detection accuracy scores are quite modest. Nevertheless, sgologs is able to detect anomalies in the log files, with swift processing times, at least when certain things are taken into consideration. If the user is aware of these factors, sgologs can definitely point towards real anomalies in the Linux log files. Thus, the tool could be used in real-life settings to simplify debugging tasks, whenever logs are used as a source of information

Understanding Bots on Social Media - An Application in Disaster Response

abstract: Social media has become a primary platform for real-time information sharing among users. News on social media spreads faster than traditional outlets and millions of users turn to this platform to receive the latest updates on major events especially disasters. Social media bridges the gap between the people who are affected by disasters, volunteers who offer contributions, and first responders. On the other hand, social media is a fertile ground for malicious users who purposefully disturb the relief processes facilitated on social media. These malicious users take advantage of social bots to overrun social media posts with fake images, rumors, and false information. This process causes distress and prevents actionable information from reaching the affected people. Social bots are automated accounts that are controlled by a malicious user and these bots have become prevalent on social media in recent years. In spite of existing efforts towards understanding and removing bots on social media, there are at least two drawbacks associated with the current bot detection algorithms: general-purpose bot detection methods are designed to be conservative and not label a user as a bot unless the algorithm is highly confident and they overlook the effect of users who are manipulated by bots and (unintentionally) spread their content. This study is trifold. First, I design a Machine Learning model that uses content and context of social media posts to detect actionable ones among them; it specifically focuses on tweets in which people ask for help after major disasters. Second, I focus on bots who can be a facilitator of malicious content spreading during disasters. I propose two methods for detecting bots on social media with a focus on the recall of the detection. Third, I study the characteristics of users who spread the content of malicious actors. These features have the potential to improve methods that detect malicious content such as fake news.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis

Textual data are a rich source of knowledge hence, sentence comparison has become one of the important tasks in text mining related works.Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem.One of the reason is two sentences can convey the same meaning with totally dissimilar words.This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences.Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures

Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu

Automatic speech recognition (ASR) is potentially helpful for children who suffer from dyslexia. Highly phonetically similar errors of dyslexic children‟s reading affect the accuracy of ASR. Thus, this study aims to evaluate acceptable accuracy of ASR using automatic transcription and phonetic labelling of dyslexic children‟s reading in BM. For that, three objectives have been set: first to produce manual transcription and phonetic labelling; second to construct automatic transcription and phonetic labelling using forced alignment; and third to compare between accuracy using automatic transcription and phonetic labelling and manual transcription and phonetic labelling. Therefore, to accomplish these goals methods have been used including manual speech labelling and segmentation, forced alignment, Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for training, and for measure accuracy of ASR, Word Error Rate (WER) and False Alarm Rate (FAR) were used. A number of 585 speech files are used for manual transcription, forced alignment and training experiment. The recognition ASR engine using automatic transcription and phonetic labelling obtained optimum results is 76.04% with WER as low as 23.96% and FAR is 17.9%. These results are almost similar with ASR engine using manual transcription namely 76.26%, WER as low as 23.97% and FAR a 17.9%. As conclusion, the accuracy of automatic transcription and phonetic labelling is acceptable to use it for help dyslexic children learning using ASR in Bahasa Melayu (BM

A Survey on Computational Propaganda Detection

Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue. In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.Comment: propaganda detection, disinformation, misinformation, fake news, media bia