The spread of attacks in computer networks and within systems can have severe consequences for both individuals and organizations. One approach to preventing the spread of attacks is to use ontological aid, which is the use of ontologies to provide a structured representation of knowledge about the attack and its components, especially the ones who often disguise themselves to remain undetected for a long time within the system. As soon as one particular stage of such an attack is detected, it is imperative to reduce the amount of spread so that no permanent damage can be done. For this, the security analyst must boil down to technical details from a behavioral perspective so that proper defensive initiatives can be taken. We propose an ontology that will aid security analysts to find out the list of vulnerabilities to be patched so that an ongoing attack campaign can be prevented from spreading even more.

Advanced persistent threats (APT) have increased in recent times as a result of the rise in interest by nation-states and sophisticated corporations to obtain high-profile information. Typically, APT attacks are more challenging to detect since they leverage zero-day attacks and common benign tools. Furthermore, these attack campaigns are often prolonged to evade detection. We leverage an approach that uses a provenance graph to obtain execution traces of host nodes in order to detect anomalous behavior. By using the provenance graph, we extract features that are then used to train an online adaptive metric learning. Online metric learning is a deep learning method that learns a function to minimize the separation between similar classes and maximizes the separation between dis- similar instances. We compare our approach with baseline models and we show our method outperforms the baseline models by increasing detection accuracy on average by 11.3% and increases True positive rate (TPR) on average by 18.3%. We also show that our method outperforms several state-of-the-art models performances in comprehensive attack datasets in both binary and multi-class settings.

Recent improvement in genomic research is paving the way towards significant progress in diagnosis and treatment of diseases. A disease risk query returns the probability of a patient to develop a particular disease based on her genomic and clinical data. Despite various innovative prospects, frequent and ubiquitous usage of genomic data in medical tests and personalized medicine may cause various privacy threats like genetic discrimination, exposure of susceptibility to diseases, and revelation of genomic data of relatives. Another major concern is on ensuring the reliability of the genome data and the correctness of the computed disease risk, which is known as authentication. We develop a novel secret sharing approach to protect privacy of sensitive genomic and clinical data, disease markers, disease name, and the query answer while ensuring authenticated result of the disease risk query. Experiments with real datasets show that our approach for authenticated disease risk queries achieves a high level of privacy with reduced processing and storage overhead.