The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

Malware typically uses Domain Generation Algorithms (DGAs) as a mechanism to contact their Command and Control server. In recent years, different approaches to automatically detect generated domain names have been proposed, based on machine learning. The first problem that we address is the difficulty to systematically compare these DGA detection algorithms due to the lack of an independent benchmark. The second problem that we investigate is the difficulty for an adversary to circumvent these classifiers when the machine learning models backing these DGA-detectors are known. In this paper we compare two different approaches on the same set of DGAs: classical machine learning using manually engineered features and a 'deep learning' recurrent neural network. We show that the deep learning approach performs consistently better on all of the tested DGAs, with an average classification accuracy of 98.7% versus 93.8% for the manually engineered features. We also show that one of the dangers of manual feature engineering is that DGAs can adapt their strategy, based on knowledge of the features used to detect them. To demonstrate this, we use the knowledge of the used feature set to design a new DGA which makes the random forest classifier powerless with a classification accuracy of 59.9%. The deep learning classifier is also (albeit less) affected, reducing its accuracy to 85.5%. CCS CONCEPTS• Security and privacy → Malware and its mitigation; • Computing methodologies → Neural networks; Classification and regression trees;

In this paper, we present a critical assessment of the use of device fingerprinting for risk-based authentication in a state-of-practice identity and access management system. Risk-based authentication automatically elevates the level of authentication whenever a particular risk threshold is exceeded. Contemporary identity and access management systems frequently leverage browser-based device fingerprints to recognize trusted devices of a certain individual. We analyzed the variability and the predictability of mobile device fingerprints. Our research shows that particularly for mobile devices the fingerprints carry a lot of similarity, even across models and brands, making them less reliable for risk assessment and step-up authentication.

This study extensively scrutinizes 14 months of registration data to identify large-scale malicious campaigns present in the .eu TLD. We explore the ecosystem and modus operandi of elaborate cybercriminal entities that recurrently register large amounts of domains for one-shot, malicious use. Although these malicious domains are short-lived, by incorporating registrant information, we establish that at least 80.04% of them can be framed in to 20 larger campaigns with varying duration and intensity. We further report on insights in the operational aspects of this business and observe, amongst other findings, that their processes are only partially automated. Finally, we apply a post-factum clustering process to validate the campaign identification process and to automate the ecosystem analysis of malicious registrations in a TLD zone.