论文标题
预测组织网络安全风险：一种深度学习的方法
Predicting Organizational Cybersecurity Risk: A Deep Learning Approach
论文作者
论文摘要
恶意黑客进行的网络攻击每年对组织，政府和个人造成无法弥补的损害。黑客使用在黑客论坛上发现的漏洞来进行复杂的网络攻击，从而探索这些论坛至关重要。我们建议一个黑客论坛实体识别框架（黑客）来识别利用目标的利用和实体。然后，黑客使用双向长期记忆模型（BILSTM）来创建一个预测模型，以实现利用的目标。该算法的结果将使用手动标记的金标准测试数据集评估，使用精度，精度，回忆和F1得分作为指标。我们选择将模型与最先进的经典机器学习和深度学习基准模型进行比较。结果表明，我们提出的黑客Bilstm模型的表现优于F1得分（79.71％）中所有经典的机器学习和深度学习模型。除LSTM以外的所有基准测试值，这些结果在0.05或更低时具有统计学意义。初步工作的结果表明，我们的模型可以帮助关键的网络安全利益相关者（例如，分析师，研究人员，教育工作者）确定利用漏洞的目标。
Cyberattacks conducted by malicious hackers cause irreparable damage to organizations, governments, and individuals every year. Hackers use exploits found on hacker forums to carry out complex cyberattacks, making exploration of these forums vital. We propose a hacker forum entity recognition framework (HackER) to identify exploits and the entities that the exploits target. HackER then uses a bidirectional long short-term memory model (BiLSTM) to create a predictive model for what companies will be targeted by exploits. The results of the algorithm will be evaluated using a manually labeled gold-standard test dataset, using accuracy, precision, recall, and F1-score as metrics. We choose to compare our model against state of the art classical machine learning and deep learning benchmark models. Results show that our proposed HackER BiLSTM model outperforms all classical machine learning and deep learning models in F1-score (79.71%). These results are statistically significant at 0.05 or lower for all benchmarks except LSTM. The results of preliminary work suggest our model can help key cybersecurity stakeholders (e.g., analysts, researchers, educators) identify what type of business an exploit is targeting.
