Threat Analysis Using Topic Models in Large-Scale Vulnerability Databases and Security Incident Case Documents

2021 ◽  
Author(s):  
Hiroki Koyanagi ◽  
Kazuo Takaragi ◽  
Sven Wohlgemuth ◽  
Katsuyuki Umezawa
Keyword(s):  
Large Scale ◽  
Topic Models ◽  
Incident Case ◽  
Security Incident ◽  
Threat Analysis

Hierarchical features-based targeted aspect extraction from online reviews

2021 ◽  
Vol 25 (1) ◽  
pp. 205-223
Author(s):  
Jin He ◽  
Lei Li ◽  
Yan Wang ◽  
Xindong Wu
Keyword(s):  
Large Scale ◽  
Topic Model ◽  
Topic Models ◽  
Online Reviews ◽  
Specific Aspect ◽  
Aspect Extraction ◽  
Additional Information ◽  
Real World Datasets ◽  
Hierarchical Features ◽  
Feature Semantics

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification.

Application of topic models to a compendium of ChIP-Seq datasets uncovers recurrent transcriptional regulatory modules

2020 ◽  
Vol 36 (8) ◽  
pp. 2352-2358
Author(s):  
Guodong Yang ◽  
Aiqun Ma ◽  
Zhaohui S Qin ◽  
Li Chen
Keyword(s):  
Cell Lines ◽  
Large Scale ◽  
Topic Models ◽  
R Package ◽  
Potential Interaction ◽  
Computational Method ◽  
Supplementary Information ◽  
Regulatory Module ◽  
Transcriptional Regulatory

Abstract Motivation The availability of thousands of genome-wide coupling chromatin immunoprecipitation (ChIP)-Seq datasets across hundreds of transcription factors (TFs) and cell lines provides an unprecedented opportunity to jointly analyze large-scale TF-binding in vivo, making possible the discovery of the potential interaction and cooperation among different TFs. The interacted and cooperated TFs can potentially form a transcriptional regulatory module (TRM) (e.g. co-binding TFs), which helps decipher the combinatorial regulatory mechanisms. Results We develop a computational method tfLDA to apply state-of-the-art topic models to multiple ChIP-Seq datasets to decipher the combinatorial binding events of multiple TFs. tfLDA is able to learn high-order combinatorial binding patterns of TFs from multiple ChIP-Seq profiles, interpret and visualize the combinatorial patterns. We apply the tfLDA to two cell lines with a rich collection of TFs and identify combinatorial binding patterns that show well-known TRMs and related TF co-binding events. Availability and implementation A software R package tfLDA is freely available at https://github.com/lichen-lab/tfLDA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Joint Model of Conversational Discourse and Latent Topics on Microblogs

2018 ◽  
Vol 44 (4) ◽  
pp. 719-754 ◽  
Author(s):  
Jing Li ◽  
Yan Song ◽  
Zhongyu Wei ◽  
Kam-Fai Wong
Keyword(s):  
Large Scale ◽  
Topic Models ◽  
Joint Model ◽  
Automatic Evaluation ◽  
Topic Extraction ◽  
Conversational Discourse ◽  
Data Sparseness ◽  
Latent Topics ◽  
Occurrence Patterns ◽  
Content Information

Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.

Research on Key Security Strategies of Cloud Computing

2013 ◽  
Vol 756-759 ◽  
pp. 1185-1189
Author(s):  
Duan Yang Zhao ◽  
Qing Xiang Xu ◽  
Xia Xia Hu
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Large Scale ◽  
Security And Privacy ◽  
Security Threats ◽  
Incident Response ◽  
Security Incident ◽  
Security Issues ◽  
Security Models ◽  
The Common

More and more organizations and individuals outsource their storage and computing business into cloud computing, which is a representation of a movement towards the intensive, large scale specialization and economy. Cloud computing brings about convenience and efficiency, but challenges in the areas of data security and privacy protection. This paper identifies the most vulnerable security threats in cloud computing, which will enable both end users and vendors to know about the key security threats associated with cloud computing, and to know about critical analysis about the different security models and tools proposed. Key security strategies from the infrastructure, operation and security incident response relieve the common security issues of cloud computing.

ICS Threat Analysis Using a Large-Scale Honeynet

2015 ◽  
Author(s):  
Alexandru Vlad Serbanescu ◽  
Sebastian Obermeier ◽  
Der-Yeuan Yu
Keyword(s):  
Large Scale ◽  
Threat Analysis

Segitiga Kebijakan dan Analisis SWOT Mengenai Implementasi Kebijakan PSBB dalam Penanggulangan COVID-19 di DKI Jakarta

2021 ◽  
Vol 3 (1) ◽  
pp. 24
Author(s):  
Risa Laras Wati ◽  
Belinda Meliana Elisabet ◽  
Goalbertus Goenawan ◽  
Nurhanifah Nurhanifah ◽  
Hadi Pratomo
Keyword(s):  
Large Scale ◽  
Swot Analysis ◽  
Central Government ◽  
Secondary Data ◽  
Provincial Government ◽  
Capital City ◽  
Community Leaders ◽  
Planning Tool ◽  
Threat Analysis ◽  
The Status

Latar Belakang. Provinsi DKI Jakarta merupakan provinsi dengan kasus tertinggi COVID-19 di Indonesia. Pada 23 Juni 2020 terdapat 10.123 kasus terkonfirmasi dan merupakan provinsi yang pertama kali menetapkan status Pembatasan Sosial Berskala Besar (PSBB).Tujuan. Mengkaji implementasi kebijakan PSBB untuk merumuskan strategi pelaksanaan kebijakan di masa mendatang.Metode. Implementasi kebijakan PSBB Provinsi DKI Jakarta dianalisis dengan menggunakan analisis SWOT (Strength, Weakness, Opportunity, Threat). Analisis SWOT merupakan suatu alat perencanaan strategis yang banyak digunakan dalam program pengembangan masyarakat, kesehatan dan pendidikan. Penelitian menggunakan data sekunder berupa kebijakan-kebijakan PSBB yang diperoleh melalui situs resmi Pemerintah Provinsi DKI Jakarta dan berita-berita terkait kebijakan PSBB DKI Jakarta yang dimuat di media cetak maupun elektronik pada rentang waktu antara 25 Februari 2020 sampai dengan 5 Juni 2020.Hasil. Adanya dukungan dari tokoh masyarakat, aplikasi untuk mendukung pekerjaan dan pendidikan jarak jauh, serta adanya dukungan dari pemerintah pusat, TNI dan Polri serta tokoh masyarakat merupakan suatu peluang agar bisa menerapkan kebijakan secara maksimal.Kesimpulan. Kebijakan PSBB sudah diimplementasikan dengan baik karena sudah mengatur seluruh aspek kehidupan masyarakat dan berhasil menekan penyebaran COVID-19 di Ibu Kota. ABSTRACTIntroduction. DKI Jakarta is the province with the highest COVID-19 cases in Indonesia, with 10.123 confirmed cases as of 23th June 2020, and it was the first province to determine the status of Large-Scale Social Restrictions (PSBB).Objective To review the implementation of large scale social restrictions policy to formulate a strategy for implementing future policies Methods. The implementation of the DKI Jakarta PSBB policy was analyzed using a SWOT (Strength, Weakness, Opportunity, Threat) analysis. SWOT analysis is a strategic planning tool that is widely used in community development, health and education programs. The study uses secondary data in the form of PSBB policies obtained through the official website of the DKI Jakarta Provincial Government as well as news related to the DKI Jakarta PSBB policies published in print and electronic media in the period between 25 th February 2020 to 5 th June 2020 Results. The existence of support from community leaders, applications to support work and study from home, as well as support from the Central Government, TNI and Polri as well as community leaders are an opportunity to be able to implement policies optimallyConclusion. The PSBB policy has been well implemented because it has managed all aspects of people’s lives and has succeeded in suppressing the spread of COVID-19 in the capital city.

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