ATRAPOS: Evaluating Metapath Query Workloads in Real Time

Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring, analysing, and extracting knowledge from such networks relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present ATRAPOS, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. ATRAPOS selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that ATRAPOS accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios.

Efficient deep learning models for land cover image classification

The availability of the sheer volume of Copernicus Sentinel imagery has created new opportunities for land use land cover (LULC) mapping at large scales using deep learning. Training on such large datasets though is a non-trivial task. In this work we experiment with the BigEarthNet dataset for LULC image classification and benchmark different state-of-the-art models, including Convolution Neural Networks, Multi-Layer Perceptrons, Visual Transformers, EfficientNets and Wide Residual Networks (WRN) architectures. Our aim is to leverage classification accuracy, training time and inference rate. We propose a framework based on EfficientNets for compound scaling of WRNs in terms of network depth, width and input data resolution, for efficiently training and testing different model setups. We design a novel scaled WRN architecture enhanced with an Efficient Channel Attention mechanism. Our proposed lightweight model has an order of magnitude less trainable parameters, achieves 4.5% higher averaged f-score classification accuracy for all 19 LULC classes and is trained two times faster with respect to a ResNet50 state-of-the-art model that we use as a baseline. We provide access to more than 50 trained models, along with our code for distributed training on multiple GPU nodes.

A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence

The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that -given the appropriate tools and methods-may be identified, crawled and subsequently leveraged to actionable cyber-threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker forums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state-of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.

Social Media Monitoring for IoT Cyber-Threats

The rapid development of IoT applications and their use in various fields of everyday life has resulted in an escalated number of different possible cyber-threats, and has consequently raised the need of securing IoT devices. Collecting Cyber-Threat Intelligence (e.g., zero-day vulnerabilities or trending exploits) from various online sources and utilizing it to proactively secure IoT systems or prepare mitigation scenarios has proven to be a promising direction. In this work, we focus on social media monitoring and investigate real-time Cyber-Threat Intelligence detection from the Twitter stream. Initially, we compare and extensively evaluate six different machine-learning based classification alternatives trained with vulnerability descriptions and tested with real-world data from the Twitter stream to identify the best-fitting solution. Subsequently, based on our findings, we propose a novel social media monitoring system tailored to the IoT domain; the system allows users to identify recent/trending vulnerabilities and exploits on IoT devices. Finally, to aid research on the field and support the reproducibility of our results we publicly release all annotated datasets created during this process.