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Attack Techniques and Threat Identification for Vulnerabilities

2022-06-22 15:27:49
Constantin Adam, Muhammed Fatih Bulut, Daby Sow, Steven Ocepek, Chris Bedell, Lilian Ngweta


Modern organizations struggle with insurmountable number of vulnerabilities that are discovered and reported by their network and application vulnerability scanners. Therefore, prioritization and focus become critical, to spend their limited time on the highest risk vulnerabilities. In doing this, it is important for these organizations not only to understand the technical descriptions of the vulnerabilities, but also to gain insights into attackers' perspectives. In this work, we use machine learning and natural language processing techniques, as well as several publicly available data sets to provide an explainable mapping of vulnerabilities to attack techniques and threat actors. This work provides new security intelligence, by predicting which attack techniques are most likely to be used to exploit a given vulnerability and which threat actors are most likely to conduct the exploitation. Lack of labeled data and different vocabularies make mapping vulnerabilities to attack techniques at scale a challenging problem that cannot be addressed easily using supervised or unsupervised (similarity search) learning techniques. To solve this problem, we first map the vulnerabilities to a standard set of common weaknesses, and then common weaknesses to the attack techniques. This approach yields a Mean Reciprocal Rank (MRR) of 0.95, an accuracy comparable with those reported for state-of-the-art systems. Our solution has been deployed to IBM Security X-Force Red Vulnerability Management Services, and in production since 2021. The solution helps security practitioners to assist customers to manage and prioritize their vulnerabilities, providing them with an explainable mapping of vulnerabilities to attack techniques and threat actors

Abstract (translated)



3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot