The coronavirus pandemic has fostered an explosion of misinformation about the disease, including the risk and effectiveness of vaccination. AI tools for automatic Scientific Claim Verification (SCV) can be crucial to defeat misinformation campaigns spreading through social media channels. However, over the past years, many concerns have been raised about the robustness of AI to adversarial attacks, and the field of automatic SCV is not exempt. The risk is that such SCV tools may reinforce and legitimize the spread of fake scientific claims rather than refute them. This article investigates the problem of generating adversarial attacks for SCV tools and shows that it is far more difficult than the generic NLP adversarial attack problem. The current NLP adversarial attack generators, when applied to SCV, often generate modified claims with entirely different meaning from the original. Even when the meaning is preserved, the modification of the generated claim is too simplistic (only a single word is changed), leaving many weaknesses of the SCV tools undiscovered. We propose T5-ParEvo, an iterative evolutionary attack generator, that is able to generate more complex and creative attacks while better preserving the semantics of the original claim. Using detailed quantitative and qualitative analyses, we demonstrate the efficacy of T5-ParEvo in comparison with existing attack generators.

Graph Representation Learning (GRL) methods which effectively capture a node’s neighborhood structure in their representations can show excellent performance on important machine learning tasks such as node and graph classification. Recent work has focused on scaling GRL to massive graphs, but existing methods are transductive (must be re-trained for unseen nodes) and are often geared to learn proximity rather than node structure. Graph Neural Network methods can learn structure, but are often supervised, prone to learn proximity, and do not scale well for massive graphs. Transfer learning has the potential to enable scaling to massive graphs, while preventing overfitting, and creating universal models for use on a wide variety of datasets. We propose a novel method that enables transfer learning. Our model performs better at tasks which require capture of nodes’ structural information and scales as well as the current state of the art to very large graphs.

Node representation learning (NRL) generates numerical vectors (embeddings) for the nodes of a graph. Structural NRL specifically assigns similar node embeddings for those nodes that exhibit similar structural roles. This is in contrast with its proximity-based counterpart, wherein similarity between embeddings reflects spatial proximity among nodes. Structural NRL is useful for tasks such as node classification where nodes of the same class share structural roles, though there may exist a distant, or no path between them. Athough structural NRL has been well-studied in static graphs, it has received limited attention in the temporal setting. Here, the embeddings are required to represent the evolution of nodes' structural roles over time. The existing methods are limited in terms of efficiency and effectiveness: they scale poorly to even moderate number of timestamps, or capture structural role only tangentially. In this work, we present a novel unsupervised approach to structural representation learning for temporal graphs that overcomes these limitations. For each node, our approach clusters then aggregates the embedding of a node's neighbors for each timestamp, followed by a further temporal aggregation of all timestamps. This is repeated for (at most) d iterations, so as to acquire information from the d -hop neighborhood of a node. Our approach takes linear time in the number of overall temporal edges, and possesses important theoretical properties that formally demonstrate its effectiveness. Extensive experiments on synthetic and real datasets show superior performance in node classification and regression tasks, and superior scalability of our approach to large graphs.

Botnets are an ever-growing threat to private users, small companies, and even large corporations. They are known for spamming, mass downloads, and launching distributed denial-of-service (DDoS) attacks that have a destructive impact on large corporations. With the rise of internet-of-things (IoT) devices, they are also used to mine cryptocurrency, intercept data in transit and send logs containing sensitive information to the master botnet. Many approaches have been developed to detect botnet activities. A few approaches employ graph neural networks (GNN) to analyze the behavior of hosts using a directed graph to represent their communications. However, while designed to capture structural graph properties, GNN may overfit, and therefore fail to capture these properties when the network is unknown. In this work we hypothesize that structural graph patterns can be used to effectively detect Botnets. We then propose a structural iterative representation learning approach for graph nodes, which is designed to perform well on unseen data, called Inferential SIR-GN. Our model creates a vector representation for each node that epitomizes its structural information. We demonstrate that this set of node representation vectors can be used with a neural network classifier to identify bot nodes within an unknown network with better performance than the current state-of-the-art GNN based method.

To mitigate a malware threat it is important to understand the malware’s behavior. The MITRE ATT&ACK ontology specifies an enumeration of tactics, techniques, and procedures (TTP) that characterize malware. However, absent are automated procedures that would characterize, given the malware executable, which part of the execution flow is connected with a specific TTP. This paper is the first in providing an automation methodology to locate TTP in a sub-part of the control flow graph that describes the execution flow of a mal-ware executable. This methodology merges graph representation learning and tools for machine learning explanation.