#ANOMALY DETECTION OFFLINE#

To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations.

However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. LogEvent2vec with bary and Random Forest can achieve the best F1-score and LogEvent2vec with tf-idf and Naive Bayes needs the least computational time. The experimental results demonstrate that LogEvent2vec can significantly reduce computational time by 30 times and improve accuracy, comparing with word2vec. We have conducted extensive experiments on a real public log dataset from BlueGene/L (BGL).

After getting the log event vector, we transform log event vector to log sequence vector by bary or tf-idf and three kinds of supervised models (Random Forests, Naive Bayes, and Neural Networks) are trained to detect the anomalies. LogEvent2vec can work with any coordinate transformation methods and anomaly detection models.

#ANOMALY DETECTION OFFLINE#

To reduce computational cost and avoid multiple transformations, in this paper, we propose an offline feature extraction model, named LogEvent2vec, which takes the log event as input of word2vec to extract the relevance between log events and vectorize log events directly. Therefore, the vector of words from word2vec can not be used directly, which needs to be transformed into the vector of log events and further transformed into the vector of log sequences. Anomalies in logs are dependent on not only an individual log message but also on the log message sequence. However, the computing cost of training word2vec is high. Word2vec can extract the relevance between words and vectorize the words. More and more works start to apply natural language processing (NLP) methods, and in particular word2vec, in the log feature extraction. Log anomaly detection is an efficient method to manage modern large-scale Internet of Things (IoT) systems. To facilitate future research, we release the codes and datasets at. DMT has been successfully deployed to serve the main traffic in the commercial Recommender System in JD.com. Experiments on JD real production dataset demonstrate the effectiveness of DMT, which significantly outperforms state-of-art methods. Besides, it exploits unbiased learning to reduce the selection bias in the training data. It utilizes Multi-gate Mixture-of-Experts to optimize multiple objectives. We propose Deep Multifaceted Transformers (DMT), a novel framework that can model users' multiple types of behavior sequences simultaneously with multiple Transformers. In this paper, we argue that it is crucial to formulate users' different interests based on multiple types of behaviors and perform multi-task learning for significant improvement in multiple objectives simultaneously. Existing recommendation algorithms usually learn the ranking scores of items by optimizing a single task (e.g., Click-through rate prediction) based on users' historical click sequences, but they generally pay few attention to simultaneously modeling users' multiple types of behaviors or jointly optimize multiple objectives (e.g., both Click-through rate and Conversion rate), which are both vital for e-commerce sites. Recommender Systems have been playing essential roles in e-commerce portals.

Anomaly detection