Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

We study the problem of unlearning datapoints from a learnt model. The learner first
receives a dataset $ S $ drawn iid from an unknown distribution, and outputs a model …

System auditing provides a low-level view into cyber threats by monitoring system entity
interactions. In response to advanced cyber-attacks, one prevalent solution is to apply data …

With privacy legislation empowering the users with the right to be forgotten, it has become
essential to make a model amenable for forgetting some of its training data. However …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Machine learning (ML) based malicious traffic detection is an emerging security paradigm,
particularly for zero-day attack detection, which is complementary to existing rule based …

Logs are semi-structured text generated by logging statements in software source code. In
recent decades, software logs have become imperative in the reliability assurance …

Abstract The Internet of Things (IoT) envisions a smart environment powered by connectivity
and heterogeneity where ensuring reliable services and communications across multiple …