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Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model

2022 | Management Information Systems Quarterly | Citations: 0
Authors: Samtani, Sagar; Chai, Yidong; Chen, Hsinchun

Abstract: Black hat hackers use malicious exploits to circumvent security controls and tak ... Expand

Abstract: Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-theart non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVADSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors. Collapse

Semantic filters: Okapi BM25

Topics: IT security web application information retrieval darknet PHP

Methods: computational algorithm case study experiment usability testing Okapi BM25
TextBenDS: a Generic Textual Data Benchmark for Distributed Systems
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2021 | Information Systems Frontiers | Citations: 0
Authors: Truică, Ciprian-Octavian; Apostol, Elena-Simona; Darmont, Jérôme; Assent, Ira

Abstract: Extracting top-k keywords and documents using weighting schemes are popular tech ... Expand

Abstract: Extracting top-k keywords and documents using weighting schemes are popular techniques employed in text mining and machine learning for different analysis and retrieval tasks. The weights are usually computed in the data preprocessing step, as they are costly to update and keep track of all the modifications performed on the dataset. Furthermore, calculation errors are introduced when analyzing only subsets of the dataset, i.e., wrong weighting are computed as weighting schemes use the number of documents for scoring keywords and documents. Therefore, in a Big Data context, it is crucial to lower the runtime of computing weighting schemes, without hindering the analysis process and the accuracy of the machine learning algorithms. To address this requirement for the task of computing top-k keywords and documents (which largely relies on weighting schemes), it is customary to design benchmarks that compare weighting schemes within various configurations of distributedframeworks and database management systems. Thus, we propose TextBenDS - a generic document-oriented benchmark for storing textual data and constructing weighting schemes. Our benchmark offers a generic data model designed with a multidimensional approach for storing text documents. We also propose using aggregation queries with various complexities and selectivities for constructing term weighting schemes, that are utilized in extracting top-k keywords and documents. We evaluate the computing performance of the queries on several distributed environments set within the Apache Hadoop ecosystem. Our experimental results provide interesting insights. As an example, MongoDB shows the best overall performance, while Spark’s execution time remains almost constant regardless of weighting schemes. Collapse

Semantic filters: Okapi BM25

Topics: information retrieval MongoDB database system distributed system MapReduce

Methods: term frequency–inverse document frequency Okapi BM25 experiment design science qualitative content analysis
A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles
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2020 | Information Systems Frontiers | Citations: 0
Authors: Martinez-Gil, Jorge; Paoletti, Alejandra Lorena; Pichler, Mario

Abstract: Automatic matching of job offers and job candidates is a major problem for a num ... Expand

Abstract: Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin. Collapse

Semantic filters: Okapi BM25

Topics: logistics management marketing management information retrieval Java

Methods: experiment Okapi BM25 computational algorithm descriptive statistic literature study
Toward Automatic Fake News Classification

2019 | HICSS | Citations: 0
Authors: Ghosh, Souvick; Shah, Chirag

Abstract: The interaction of technology with humans have many adverse effects. The rapid g ... Expand

Abstract: The interaction of technology with humans have many adverse effects. The rapid growth and outreach of the social media and the Web have led to the dissemination of questionable and untrusted content among a wider audience, which has negatively influenced their lives and judgment. Different election campaigns around the world highlighted how ''fake news'' - misinformation that looks genuine - can be targeted towards specific communities to manipulate and confuse them. Ever since, automatic fake news detection has gained widespread attention from the scientific community. As a result, many research studies have been conducted to tackle the detection and spreading of fake news. While the first step of such tasks would be to classify claims associated based on their credibility, the next steps would involve identifying hidden patterns in style, syntax, and content of such news claims. We provide a comprehensive overview of what has already been done in this domain and other similar fields, and then propose a generalized method based on Deep Neural Networks to identify if a given claim is fake or genuine. By using different features like the authenticity of the source, perceived cognitive authority, style, and content-based factors, and natural language features, it is possible to predict fake news accurately. We have used a modular approach by combining techniques from information retrieval, natural language processing, and deep learning. Our classifier comprises two main sub-modules. The first sub-module uses the claim to retrieve relevant articles from the knowledge base which can then be used to verify the truth of the claim. It also uses word-level features for prediction. The second sub-module uses a deep neural network to learn the underlying style of fake content. Our experiments conducted on benchmark datasets show that for the given classification task we can obtain up to 82.4% accuracy by using a combination of two models; the first model was up to 72% accurate while the second model was around 81\% accurate. Our detection model has the potential to automatically detect and prevent the spread of fake news, thus, limiting the caustic influence of technology in the human lives. Collapse

Semantic filters: Okapi BM25

Topics: fake news information retrieval knowledge base social media internet technology

Methods: deep learning computational algorithm natural language processing experiment machine learning
A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

2019 | HICSS | Citations: 0
Authors: Hasan, Mir Anamul; Schwartz, Daniel G

Abstract: Selection of a suitable Ph.D. supervisor is a very important step in a student’s ... Expand

Abstract: Selection of a suitable Ph.D. supervisor is a very important step in a student’s career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student’s research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor’s involvement and no subjective measures are employed. Collapse

Semantic filters: Okapi BM25

Topics: information retrieval decision support system Elasticsearch database system systems implementation

Methods: Okapi BM25 computational algorithm survey term frequency–inverse document frequency trade-off analysis method
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
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2019 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Kratzwald, Bernhard; Feuerriegel, Stefan

Abstract: Traditional information retrieval (such as that offered by web search engines) i ... Expand

Abstract: Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein. Conversely, question-answering systems change how humans interact with information systems: users can now ask specific questions and obtain a tailored answer—both conveniently in natural language. Despite obvious benefits, their use is often limited to an academic context, largely because of expensive domain customizations, which means that the performance in domain-specific applications often fails to meet expectations. This article proposes cost-efficient remedies: (i) we leverage metadata through a filtering mechanism, which increases the precision of document retrieval, and (ii) we develop a novel fuse-and-oversample approach for transfer learning to improve the performance of answer extraction. Here, knowledge is inductively transferred from related, yet different, tasks to the domain-specific application, while accounting for potential differences in the sample sizes across both tasks. The resulting performance is demonstrated with actual use cases from a finance company and the film industry, where fewer than 400 question-answer pairs had to be annotated to yield significant performance gains. As a direct implication to management, this presents a promising path to better leveraging of knowledge stored in information systems. Collapse

Semantic filters: Okapi BM25

Topics: information retrieval domain-specific language knowledge sharing Wikipedia open source

Methods: experiment deep learning artificial neural network question answering term frequency–inverse document frequency
An Embedding Based IR Model for Disaster Situations
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2018 | Information Systems Frontiers | Citations: 4
Authors: Bandyopadhyay, Ayan; Ganguly, Debasis; Mitra, Mandar; Saha, Sanjoy Kumar; Jones, Gareth J.F.

Abstract: Twitter (http://twitter.com) is one of the most popular social networking platfo ... Expand

Abstract: Twitter (http://twitter.com) is one of the most popular social networking platforms. Twitter users can easily broadcast disaster-specific information, which, if effectively mined, can assist in relief operations. However, the brevity and informal nature of tweets pose a challenge to Information Retrieval (IR) researchers. In this paper, we successfully use word embedding techniques to improve ranking for ad-hoc queries on microblog data. Our experiments with the ‘Social Media for Emergency Relief and Preparedness’ (SMERP) dataset provided at an ECIR 2017 workshop show that these techniques outperform conventional term-matching based IR models. In addition, we show that, for the SMERP task, our word embedding based method is more effective if the embeddings are generated from the disaster specific SMERP data, than when they are trained on the large social media collection provided for the TREC (http://trec.nist.gov/) 2011 Microblog track dataset. Collapse

Semantic filters: Okapi BM25

Topics: Twitter information retrieval microblog social media earthquake

Methods: word embedding word2vec Okapi BM25 experiment language model
Towards Agent-based Large-scale Decision Support System: The Effect of Facilitator

2018 | HICSS | Citations: 0
Authors: Ito, Takayuki

Abstract: Good discussions are essential for group decisions, especially when the number o ... Expand

Abstract: Good discussions are essential for group decisions, especially when the number of people in thea group has many people is large. So, it is important to Pprovidinge good support is critical for having establishing and maintaining coherent discussions that avoid some of thesuch anti-social bad behaviors, like as flaming, that have, which has been observed in some large discussion groups. We have developed a large-scale online decision support system that has facilitator support functions, and deployed it in case studies for several real-world online discussion supports as case studies. In this paper, weWe propose a facilitator-mediated online discussion model in order to lead discussions to in a better direction for ato reach decisions. Our extreme ultimate goal is an to realize automated facilitator agent that can adequately leadhelp participants to achieve reach reasonable decisions. In reality, online discussion is often fails plagued byinto "flaming,", which is the act of posting or sending offensive messages during a discussion. Such flaming phenomena have been focused on as anti-social bad behavior of in online discussion forums. After several cases studies, we learned several lessons. Critically, The most important achievement is that in any all of our social experiments, no flaming has not been observed in our facilitator-mediated decision support system. Also, we obtained Our some insights also suggest in whichthat the social presence of a facilitator would have largegreatly aeffect for participants’ behavior. Collapse

Semantic filters: Okapi BM25

Topics: social presence internet technology virtual world decision support system information retrieval

Methods: experiment case study survey Okapi BM25 cluster analysis
Emergency Vocabulary
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2018 | Information Systems Frontiers | Citations: 3
Authors: Nemeskey, Dávid Márk; Kornai, András

Abstract: For disaster preparedness, a key aspect of the work is the identification, ahead ... Expand

Abstract: For disaster preparedness, a key aspect of the work is the identification, ahead of time, of the vocabulary of emergency messages. Here we describe how static repositories of traditional news reports can be rapidly exploited to yield disaster- or accident-implicated words and named entities. Collapse

Semantic filters: Okapi BM25

Topics: tornado tsunami earthquake armed conflict terrorist incident

Methods: experiment CoreNLP pilot experiment computational algorithm Okapi BM25
A two-stage decision model for information filtering
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2012 | Decision Support Systems | Citations: 0
Authors: Li, Yuefeng; Zhou, Xujuan; Bruza, Peter; Xu, Yue; Lau, Raymond Y. K.

Abstract: Information mismatch and overload are two fundamental issues influencing the eff ... Expand

Abstract: Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems. Collapse

Semantic filters: Okapi BM25

Topics: information retrieval knowledge work information overload

Methods: computational algorithm experiment support vector machine Okapi BM25 Student's t-test

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Found 15 articles

Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model

TextBenDS: a Generic Textual Data Benchmark for Distributed Systems

A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Toward Automatic Fake News Classification

A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization

An Embedding Based IR Model for Disaster Situations

Towards Agent-based Large-scale Decision Support System: The Effect of Facilitator

Emergency Vocabulary

A two-stage decision model for information filtering