Cyber-attacks become more sophisticated and complex especially when adversaries steal user credentials to traverse the network of an organization. Detecting a breach is extremely difficult and this is confirmed by the findings of studies related to cyber-attacks on organizations. A study conducted last year by IBM found that it takes 206 days on average to US companies to detect a data breach. As a consequence, the effectiveness of existing defensive tools is in question. In this work we deal with the detection of malicious authentication events, which are responsible for effective execution of the stealthy attack, called lateral movement. Authentication event logs produce a pure categorical feature space which creates methodological challenges for developing outlier detection algorithms. We propose an auto semi-supervised outlier ensemble detector that does not leverage the ground truth to learn the normal behavior. The automatic nature of our methodology is supported by established unsupervised outlier ensemble theory. We test the performance of our detector on a real-world cyber security dataset provided publicly by the Los Alamos National Lab. Overall, our experiments show that our proposed detector outperforms existing algorithms and produces a 0 False Negative Rate without missing any malicious login event and a False Positive Rate which improves the state-of-the-art. In addition, by detecting malicious authentication events, compared to the majority of the existing works which focus solely on detecting malicious users or computers, we are able to provide insights regarding when and at which systems malicious login events happened. Beyond the application on a public dataset we are working with our industry partner, POST Luxembourg, to employ the proposed detector on their network.

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Managed security service providers increasingly rely on machine-learning methods to exceed traditional, signature-based threat detection and classification methods. As machine-learning often improves with more data available, smaller organizations and clients find themselves at a disadvantage: Without the ability to share their data and others willing to collaborate, their machine-learned threat detection will perform worse than the same model in a larger organization. We show that Federated Learning, i.e. collaborative learning without data sharing, successfully helps to overcome this problem. Our experiments focus on a common task in cyber security, the detection of unwanted URLs in network traffic seen by security-as-a-service providers. Our experiments show that i) Smaller participants benefit from larger participants ii) Participants seeing different types of malicious traffic can generalize better to unseen types of attacks, increasing performance by 8% to 15% on average, and up to 27% in the extreme case. iii) Participating in Federated training never harms the performance of the locally trained model. In our experiment modeling a security-as-a service setting, Federated Learning increased detection up to 30% for some participants in the scheme. This clearly shows that Federated Learning is a viable approach to address issues of data sharing in common cyber security settings.

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Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when using machine learning as an approach to a problem in networking and translate testing performance to real networks deployments successfully. This paper, rather than presenting a particular technical result, discusses the necessary considerations to obtain good results when using machine learning to analyze network-related data.@en

Automated network traffic analysis using machine learning techniques plays an important role in managing networks and IT infrastructure. A key challenge to the correct and effective application of machine learning is dealing with non-stationary learning data sources and concept drift. Traffic evolves overtime due to new technology, software, services being used, changes in user behavior but also due to changes in network graphs like dynamic IP address assignment. In this paper, we present an automatic online method to detect changepointsin network traffic based on IP flow record analysis. This technique is used to segment an observed behavior into smaller consecutive behaviors differing one from another. The segmented traffic is used to learn small communication profile characterizing accurately the activities present between two observed changepoints. We validate our method using synthetic data and outlinea real-world application to botnet hosts behavior modeling.@en

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information often comes with using simpler models on aggregated data (e.g. IP flow records) due to time and space constraints. A step towards utilizing IP flow records more effectively are stream learning techniques. We propose a method to collect a limited yet relevant amount of data in order to learn a class of complex models, finite state machines, in real-time. These machines are used as communication profiles to fingerprint, identify or classify hosts and services and offer high detection rates while requiring less training data and thus being faster to compute than simple models.@en

We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classication to data scientists, without the need to understand the algorithm itself but rather the limitations of PDFA as a model. With applications of automata learning in diverse elds such as network trac analysis, software engineering and biology, a stratied package opens opportunities for practitioners.@en