Semantically Rich Framework to Automate Cyber Insurance Services

Sane, Ketki; Joshi, Karuna Pande; Mittal, Sudip

doi:10.1109/tsc.2021.3113272

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…For example, Mittal et al developed CyberTwitter, which mines threat intelligence from Twitter and generates actionable alerts for cybersecurity threats and exploits [19]. Similar methods have been developed that leverage a Cybersecurity Knowledge Graph (CKG) to represent and store cybersecurity intelligence to be used as training data for AI-based cybersecurity systems, or from extraction or ingestion pipelines [14]- [18], [24], [25].…”

Section: Related Work a Leveraging Textual Cybersecurity Datamentioning

confidence: 99%

CyBERT: Contextualized Embeddings for the Cybersecurity Domain

Ranade

Piplai

Joshi

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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We present CyBERT, a domain-specific Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data. State-of-the-art natural language models that can process dense, fine-grained textual threat, attack, and vulnerability information can provide numerous benefits to the cybersecurity community. The primary contribution of this paper is providing the security community with an initial fine-tuned BERT model that can perform a variety of cybersecurity-specific downstream tasks with high accuracy and efficient use of resources. We create a cybersecurity corpus from open-source unstructured and semi-unstructured Cyber Threat Intelligence (CTI) data and use it to fine-tune a base BERT model with Masked Language Modeling (MLM) to recognize specialized cybersecurity entities. We evaluate the model using various downstream tasks that can benefit modern Security Operations Centers (SOCs). The finetuned CyBERT model outperforms the base BERT model in the domain-specific MLM evaluation. We also provide use-cases of CyBERT application in cybersecurity based downstream tasks.

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Section: Related Work a Leveraging Textual Cybersecurity Datamentioning

confidence: 99%

CyBERT: Contextualized Embeddings for the Cybersecurity Domain

Ranade

Piplai

Joshi

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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“…The supervised model is a feedforward neural network and it produces an entity-relationship set as an output based on an underlying UCO ontology [18]. Some dependent systems include [6], [16], [19]- [27]. The output entity relationships are constructed using manyto-many, many-to-one, and one-to-one relations.…”

Section: B Relationship Extractionmentioning

confidence: 99%

Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs

Mitra

Piplai

Mittal

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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Today there is a significant amount of fake cybersecurity related intelligence on the internet. To filter out such information, we build a system to capture the provenance information and represent it along with the captured Cyber Threat Intelligence (CTI). In the cybersecurity domain, such CTI is stored in Cybersecurity Knowledge Graphs (CKG). We enhance the exiting CKG model to incorporate intelligence provenance and fuse provenance graphs with CKG. This process includes modifying traditional approaches to entity and relation extraction. CTI data is considered vital in securing our cyberspace. Knowledge graphs containing CTI information along with its provenance can provide expertise to dependent Artificial Intelligence (AI) systems and human analysts.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Mansoor Ahmed . [3], [4], [5], [6], [7], [8], [9], [10], [11]. AI-and ML-assisted cybersecurity offers data-driven automation that could enable security systems to identify and respond to cyber threats in real time.…”

Section: Introductionmentioning

confidence: 99%

Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

et al. 2022

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The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions. Intrusion Detection Systems (IDS), using some forms of AI, have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. These systems are hosted in the organizational Cyber Security Operation Center (CSoC) as a defense tool to monitor and detect malicious network flow that would otherwise impact the Confidentiality, Integrity, and Availability (CIA). CSoC analysts rely on these systems to make decisions about the detected threats. However, IDSs designed using Deep Learning (DL) techniques are often treated as black box models and do not provide a justification for their predictions. This creates a barrier for CSoC analysts, as they are unable to improve their decisions based on the model's predictions. One solution to this problem is to design explainable IDS (X-IDS). This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS. In particular, we discuss black box and white box approaches comprehensively. We also present the tradeoff between these approaches in terms of their performance and ability to produce explanations. Furthermore, we propose a generic architecture that considers human-in-the-loop which can be used as a guideline when designing an X-IDS. Research recommendations are given from three critical viewpoints: the need to define explainability for IDS, the need to create explanations tailored to various stakeholders, and the need to design metrics to evaluate explanations.INDEX TERMS Explainable intrusion detection systems, explainable artificial intelligence, machine learning, deep learning, white box, black box, explainability, cybersecurity.

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Semantically Rich Framework to Automate Cyber Insurance Services

Cited by 4 publications

References 13 publications

CyBERT: Contextualized Embeddings for the Cybersecurity Domain

CyBERT: Contextualized Embeddings for the Cybersecurity Domain

Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs

Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

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