Abstract Malware is increasingly endowed with steganographic mechanisms for concealing malicious data to avoid detection or bypass security measures. As a result, an emerging wave of threats named stegomalware has started to rise. Among the various approaches, real-world stegomalware primarily hides information within digital images, for instance, to retrieve additional payloads or configuration data. Unfortunately, developing attack-agnostic mitigation tools is difficult, especially due to the tight relation between the image format and the steganographic technique. Therefore, this paper presents an autoencoder-based approach to perform sanitization , i.e., to disrupt the malicious content hidden in images without altering their visual quality. For this purpose, we used an enhanced U-Net-like neural architecture, and we compared our idea against other mechanisms, including JPG transcoding and simple addition of Gaussian noise. Results obtained by considering different hiding patterns and realistic payloads showcased the effectiveness of our approach. Moreover, the U-Net-based sanitization solution prevents the recovery of the payload while preserving the original image quality and reducing risks arising from side-channel attacks.

Abstract Modern IoT ecosystems are the preferred target of threat actors wanting to incorporate resource-constrained devices within a botnet or leak sensitive information. A major research effort is then devoted to create countermeasures for mitigating attacks, for instance, hardware-level verification mechanisms or effective network intrusion detection frameworks. Unfortunately, advanced malware is often endowed with the ability of cloaking communications within network traffic, e.g., to orchestrate compromised IoT nodes or exfiltrate data without being noticed. Therefore, this paper showcases how different autoencoder-based architectures can spot the presence of malicious communications hidden in conversations, especially in the TTL of IPv4 traffic. To conduct tests, this work considers IoT traffic traces gathered in a real setting and the presence of an attacker deploying two hiding schemes (i.e., naive and “elusive” approaches). Collected results showcase the effectiveness of our method as well as the feasibility of deploying autoencoders in production-quality IoT settings.

Abstract Detecting deviant traces in business process logs is crucial for modern organizations, given the harmful impact of deviant behaviours (e.g., attacks or faults). However, training a Deviance Prediction Model (DPM) by solely using supervised learning methods is impractical in scenarios where only few examples are labelled. To address this challenge, we propose an Active-Learning-based approach that leverages multiple DPMs and a temporal ensembling method that can train and merge them in a few training epochs. Our method needs expert supervision only for a few unlabelled traces exhibiting high prediction uncertainty. Tests on real data (of either complete or ongoing process instances) confirm the effectiveness of the proposed approach.

Indexing multimedia content with rich and accurate metadata allows for improving the quality of the search engines’ results and boosting the recommender systems performances, which can benefit from this information to yield more effective recommendation lists. Therefore, the adoption of tools able to automatically label multimedia content with informative tags represents an important task for all the companies offering streaming entertainment services. However, domain experts generally perform the tagging process manually, making it time-consuming and error-prone. In the last few years, Machine Learning techniques have been proposed as a promising solution to automate this type of task, but the lack of clean and labeled training data hinders the learning of robust classification models. To cope with the issues described above, in this work, we devised a Deep Learning based solution for semi-automatic multi-label classification integrating post-hoc explanation techniques. Specifically, model explanation methods are exploited to assist the operator in the labeling process by facilitating an understanding of the model predictions. The proposed approach has been validated on a real dataset, and the experimental results demonstrate its effectiveness.

In many application contexts, a business process' executions are subject to performance constraints expressed in an aggregated form, usually over predefined time windows, and detecting a likely violation to such a constraint in advance could help undertake corrective measures for preventing it. This paper illustrates a prediction-aware event processing framework that addresses the problem of estimating whether the process instances of a given (unfinished) window w will violate an aggregate performance constraint, based on the continuous learning and application of an ensemble of models, capable each of making and integrating two kinds of predictions: single-instance predictions concerning the ongoing process instances of w, and time-series predictions concerning the "future" process instances of w (i.e. those that have not started yet, but will start by the end of w). Notably, the framework can continuously update the ensemble, fully exploiting the raw event data produced by the process under monitoring, suitably lifted to an adequate level of abstraction. The framework has been validated against historical event data coming from real-life business processes, showing promising results in terms of both accuracy and efficiency.

Increasing attention has been paid to the detection and analysis of “deviant” instances of a business process that are connected with some kind of “hidden” undesired behavior (e.g. frauds and faults). In particular, several recent works faced the problem of inducing a binary classification model (here named deviance detection model ) that can discriminate between deviant traces and normal ones, based on a set of historical log traces (labeled as either deviant or normal). Current solutions rely on applying standard classifier-induction methods to a feature-based representation of the given traces, where the features include sequence-based patterns extracted from the corresponding sequences of activities. However, there is no consensus on which kinds of patterns are the most suitable for such a task. On the other hand, mixing multiple pattern families together may produce a heterogenous, redundant and sparse representation of the traces that likely leads to poor deviance detection models. In this paper, we propose an ensemble-learning method for solving this problem, where multiple base classifiers are trained on different feature-based views of the log (each obtained by mapping the traces onto a distinguished collection of patterns). A stacking procedure is used to combine the discovered base models into an overall probabilistic model that associates any new trace with an estimate of the probability that it reflects a deviant process instance. This helps the analyst prioritize the inspection of the cases that are more likely to be deviant. The method also takes advantage of all nonstructural data available in the log, and employs a resampling mechanism to deal with the rarity of deviances in the training log. It has been conceived as the core of a comprehensive framework for detecting and analyzing business process deviances. The framework supports the analyst to investigate suspect deviances, and provides some feedback to the learning method for improving the accuracy of the discovered deviance detection models. Tests on several real-life datasets proved the validity of the approach, as concerns its capability to discover an accurate deviance detection model, and to effectively exploit new (originally unlabeled) traces via active learning and self-training mechanisms.