We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs). AGS-GNN exploits the node features and the connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. In homophilic graphs, vertices of the same class are more likely to be adjacent, but vertices of different classes tend to be adjacent in heterophilic graphs. GNNs have been successfully applied to homophilic graphs, but their utility to heterophilic graphs remains challenging. The state-of-the-art GNNs for heterophilic graphs use the full neighborhood of a node instead of sampling it, and hence do not scale to large graphs and are not inductive. We develop dual-channel sampling techniques based on feature-similarity and feature-diversity to select subsets of neighbors for a node that capture adaptive information from homophilic and heterophilic neighborhoods. Currently, AGS-GNN is the only algorithm that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, a novel contribution in this context. We pre-compute the sampling distribution in parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (< 100K nodes) and large (- 100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compared to the state-of-the-art approaches. AGS-GNN achieves test accuracy comparable to the best-performing heterophilic GNNs, even outperforming methods that use the entire graph for node classification. AGS-GNN converges faster than methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

Defending cybersystems needs accurate mapping of software and hardware vulnerabilities to generalized descriptions of weaknesses, and weaknesses to exploits. These mappings enable cyber defenders to build plans for effective defense and assessment of potential risks to a cybersystem. With close to 200k vulnerabilities, manual mapping is not a feasible option. However, automated mapping is challenging due to limited training data, computational intractability, and limitations in computational natural language processing. Tools based on breakthroughs in Transformer-based language models have been demonstrated to classify vulnerabilities with high accuracy. We make three key contributions in this paper: (1) We present a new framework, VWC-BERT, that augments the Transformer-based hierarchical multi-class classification framework of Das et al. (V2W-BERT) with the ability to map weaknesses to exploits. (2) We implement VWC-BERT on modern AI accelerator platforms using two data parallel techniques for the pre-training phase and demonstrate nearly linear speedups across NVIDIA accelerator platforms. We observe nearly linear speedups for up to 16 V100 and 8 A100 GPUs, and about 3.4× speedup for A100 relative to V100 GPUs. Enabled by scaling, we also demonstrate higher accuracy using a larger language model, RoBERTa-Large. We show up to 87% accuracy for strict and up to 98% accuracy for relaxed classification. (3) We develop a novel parallel link manager for the link prediction phase and demonstrate up to 21× speedup with 16 V100 GPUs relative to one V100 GPU, and thus reducing the runtime from 2.5 hours to 10 minutes. We believe that generalizability and scalability of VWC-BERT will benefit both the theoretical development and practical deployment of novel cyberdefense solutions and vulnerability classification.

We consider the problem of automating the mapping of observed vulnerabilities in software listed in Common Vulnerabilities and Exposures (CVE) reports to weaknesses listed in Common Weakness Enumerations (CWE) reports, a hierarchically designed dictionary of software weaknesses. Mapping of CVEs to CWEs provides a means to understand how they might be exploited for malicious purposes, and to mitigate their impact. Since manual mapping of CVEs to CWEs is not a viable approach due to their ever-increasing sizes, automated approaches need to be devised but obtaining highly accurate mapping is a challenging problem. We present a novel Transformer-based learning framework (V2W-BERT) in this paper to solve this problem by bringing together ideas from natural language processing, link prediction and transfer learning. Our method outperforms previous approaches not only for CWE instances with abundant data to train, but also for rare CWE classes with little or no data. Using vulnerability and weakness reports from MITRE and the National Vulnerability Database, we achieve up to 97% prediction accuracy for randomly partitioned data and up to 94% prediction accuracy in temporally partitioned data. We demonstrate significant improvements in using historical data to predict weaknesses for future instances of CVEs. We believe that our work will would influence the design of better automated mapping approaches, and also that this technology could be deployed for more effective cybersecurity.

In a heterogeneous computing cluster, cluster objectives are conflicting to each other. Selecting a right combination of machines is necessary to enhance cluster performance, and to optimize all the cluster objectives. In this paper, we perform empirical performance analyses of a real cluster with our year-long collected data, formulate a new many-objective optimization problem for clusters, and integrate a greedy approach with the existing NSGA-III algorithm to solve this problem. From our experimental results, we find our approach performs better than existing optimization approaches.