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In contrast to previous surveys, the present work is not focused on reviewing the datasets used in the network security field. The fact is that many of the available public labeled datasets represent the network behavior just for a particular time period. Given the rate of change in malicious behavior and the serious challenge to label, and maintain these datasets, they become quickly obsolete. Therefore, this work is focused on the analysis of current labeling methodologies applied to network-based data. In the field of network security, the process of labeling a representative network traffic dataset is particularly challenging and costly since very specialized knowledge is required to classify network traces. Consequently, most of the current traffic labeling methods are based on the automatic generation of synthetic network traces, which hides many of the essential aspects necessary for a correct differentiation between normal and malicious behavior. Alternatively, a few other methods incorporate non-experts users in the labeling process of real traffic with the help of visual and statistical tools. However, after conducting an in-depth analysis, it seems that all current methods for labeling suffer from fundamental drawbacks regarding the quality, volume, and speed of the resulting dataset. This lack of consistent methods for continuously generating a representative dataset with an accurate and validated methodology must be addressed by the network security research community. Moreover, a consistent label methodology is a fundamental condition for helping in the acceptance of novel detection approaches based on statistical and machine learning techniques.

Deep learning models have recently demonstrated remarkable results in a variety of tasks, which is why they are being increasingly applied in high-stake domains, such as industry, medicine, and finance. Considering that automatic predictions in these domains might have a substantial impact on the well-being of a person, as well as considerable financial and legal consequences to an individual or a company, all actions and decisions that result from applying these models have to be accountable. Given that a substantial amount of data that is collected in high-stake domains are in the form of time series, in this paper we examine the current state of eXplainable AI (XAI) methods with a focus on approaches for opening up deep learning black boxes for the task of time series classification. Finally, our contribution also aims at deriving promising directions for future work, to advance XAI for deep learning on time series data., 8 pages, 0 figures, Accepted as a poster presentation

Model based reinforcement learning (MBRL) uses an imperfect model of the world to imagine trajectories of future states and plan the best actions that maximize a given reward. These trajectories are imperfect and MBRL attempts to overcome this by relying on model predictive control (MPC) to continuously re-imagine trajectories from scratch. Such re-generation of imagined trajectories carries the major computational cost and increasing complexity in tasks with longer receding horizon. We investigate how far in the future the imagined trajectories can be relied upon while still maintaining acceptable reward. After taking each action, information becomes available about its immediate effect and its impact on outcomes expected of future actions. Hereby, we propose four methods for deciding whether to trust and act upon imagined trajectories: i) looking at recent errors with respect to expectations, ii) comparing the confidence in an action imagined against its execution, iii) observing the deviation in projected future states iv) observing the deviation in projected future rewards. An experiment analyzing the effects of acting upon imagination shows that our methods reduce computation by at least 20\% and up to 80\%, depending on the environment, while retaining acceptable reward.

This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task with a multidimensional input consisting of the vehicle telemetry, and a continuous action space. To find out which RL methods better solve the problem and whether the obtained models generalize to driving on unknown tracks, we put 10 variants of deep deterministic policy gradient (DDPG) to race in two experiments: i)~studying how RL methods learn to drive a racing car and ii)~studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.