scholarly journals CTI View: APT Threat Intelligence Analysis System

2022 ◽  
Vol 2022 ◽  
pp. 1-15
Author(s):  
Yinghai Zhou ◽  
Yi Tang ◽  
Ming Yi ◽  
Chuanyu Xi ◽  
Hai Lu
Keyword(s):  
Language Processing ◽  
Automation System ◽  
Intelligence Analysis ◽  
Entity Extraction ◽  
Text Extraction ◽  
Advanced Persistent Threat ◽  
Active Defense ◽  
Main Work ◽  
Threat Intelligence ◽  
Analysis System

With the development of advanced persistent threat (APT) and the increasingly severe situation of network security, the strategic defense idea with the concept of “active defense, traceability, and countermeasures” arises at the historic moment, thus cyberspace threat intelligence (CTI) has become increasingly valuable in enhancing the ability to resist cyber threats. Based on the actual demand of defending against the APT threat, we apply natural language processing to process the cyberspace threat intelligence (CTI) and design a new automation system CTI View, which is oriented to text extraction and analysis for the massive unstructured cyberspace threat intelligence (CTI) released by various security vendors. The main work of CTI View is as follows: (1) to deal with heterogeneous CTI, a text extraction framework for threat intelligence is designed based on automated test framework, text recognition technology, and text denoising technology. It effectively solves the problem of poor adaptability when crawlers are used to crawl heterogeneous CTI; (2) using regular expressions combined with blacklist and whitelist mechanism to extract the IOC and TTP information described in CTI effectively; (3) according to the actual requirements, a model based on bidirectional encoder representations from transformers (BERT) is designed to complete the entity extraction algorithm for heterogeneous threat intelligence. In this paper, the GRU layer is added to the existing BERT-BiLSTM-CRF model, and we evaluate the proposed model on the marked dataset and get better performance than the current mainstream entity extraction mode.

scholarly journals EX-Action: Automatically Extracting Threat Actions from Cyber Threat Intelligence Report Based on Multimodal Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huixia Zhang ◽  
Guowei Shen ◽  
Chun Guo ◽  
Yunhe Cui ◽  
Chaohui Jiang
Keyword(s):  
Language Processing ◽  
Learning Algorithm ◽  
Complex Structure ◽  
Extraction Methods ◽  
Experimental Result ◽  
Intelligence Analysis ◽  
Multimodal Learning ◽  
Threat Intelligence ◽  
Cyber Threat ◽  
Cyber Threat Intelligence

With the increasing complexity of network attacks, an active defense based on intelligence sharing becomes crucial. There is an important issue in intelligence analysis that automatically extracts threat actions from cyber threat intelligence (CTI) reports. To address this problem, we propose EX-Action, a framework for extracting threat actions from CTI reports. EX-Action finds threat actions by employing the natural language processing (NLP) technology and identifies actions by a multimodal learning algorithm. At the same time, a metric is used to evaluate the information completeness of the extracted action obtained by EX-Action. By the experiment on the CTI reports that consisted of sentences with complex structure, the experimental result indicates that EX-Action can achieve better performance than two state-of-the-art action extraction methods in terms of accuracy, recall, precision, and F1-score.

scholarly journals ENTITY EXTRACTION FROM UNSTRUCTURED MEDICAL TEXT

2020 ◽  
Vol 5 (8) ◽  
Author(s):  
G Deepank ◽  
R Tharun Raj ◽  
Aditya Verma
Keyword(s):  
Language Processing ◽  
Medical Records ◽  
Question Answering ◽  
Entity Extraction ◽  
Data Repositories ◽  
Efficient Treatment ◽  
Learning Techniques ◽  
Treatment Plans ◽  
Data Elements ◽  
Rich Data

Electronic medical records represent rich data repositories loaded with valuable patient information. As artificial intelligence and machine learning in the field of medicine is becoming more popular by the day, ways to integrate it are always changing. One such way is processing the clinical notes and records, which are maintained by doctors and other medical professionals. Natural language processing can record this data and read more deeply into it than any human. Deep learning techniques such as entity extraction which involves identifying and returning of key data elements from an electronic medical record, and other techniques involving models such as BERT for question answering, when applied to all these medical records can create bespoke and efficient treatment plans for the patients, which can help in a swift and carefree recovery.

scholarly journals A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications

2020 ◽  
Vol 34 (08) ◽  
pp. 13369-13381
Author(s):  
Shivashankar Subramanian ◽  
Ioana Baldini ◽  
Sushma Ravichandran ◽  
Dmitriy A. Katz-Rogozhnikov ◽  
Karthikeyan Natesan Ramamurthy ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Generic Drugs ◽  
Low Cost ◽  
Processing System ◽  
Drug Repurposing ◽  
Cancer Type ◽  
Entity Extraction ◽  
Scientific Publications

More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.

THE CHALLENGES OF SECURITY THREAT IN NIGERIA CYBERSPACE

2021 ◽  
Vol 5 (1) ◽  
pp. 193-201
Author(s):  
I. R. Saidu ◽  
T. Suleiman ◽  
U. E. Akpan
Keyword(s):  
Security Policy ◽  
Research Work ◽  
Critical Infrastructures ◽  
Intelligence Analysis ◽  
Security Threats ◽  
Security Threat ◽  
Secondary Sources ◽  
Threat Intelligence ◽  
Cyber Threat ◽  
Cyber Threat Intelligence

This research work was conducted to examine critically and systematically cyber threat intelligence challenges and prospects in Nigeria. It judges the value and relevance of cyber threat intelligence in the society where they are lacking in providing necessary information. Dealing with these challenges that may cause threat intelligence to be useless has become a major concern to Nigeria. The work was intended to achieve the following objectives: to examine the nature of cybersecurity in Nigeria, to analyse the cybersecurity threats that can disrupt the functioning of the country, to identify the challenges facing the Nigeria cyberspace and the conduct of a cyber threat intelligence analysis, to discuss the means by which cyber threat can be used to boost Nigeria’s National Security Policy, to make recommendations to preserve important intelligence capabilities while ensuring the protection of its critical infrastructures through the use of threat intelligence. The scope of the study was limited to the period 2009 – 2019. The research was analytical. Relevant data were collected from both primary and secondary sources of data. The data analysis used the percentage instrument and the following conclusions were drawn: that threat data overload, threat data quality, privacy and legal issues and interoperability issues are some of the challenges of cyber threat intelligence; also, the need to continually invest in research, build local cyber threat management infrastructure and enhance the ability to anticipate, detect, respond and contain information security threats is very crucial. Nigeria 

An Opinion Mining Approach for Drug Reviews in Spanish

2021 ◽  
pp. 445-480
Author(s):  
Karina Castro-Pérez ◽  
José Luis Sánchez-Cervantes ◽  
María del Pilar Salas-Zárate ◽  
Maritza Bustos-López ◽  
Lisbeth Rodríguez-Mazahua
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Adverse Drug Events ◽  
Hybrid Approach ◽  
Unstructured Data ◽  
Degenerative Diseases ◽  
Analysis System ◽  
Essential Contribution ◽  
Processing Techniques ◽  
Manual Review

In recent years, the application of opinion mining has increased as a boom and growth of social media and blogs on the web, and these sources generate a large volume of unstructured data; therefore, a manual review is not feasible. For this reason, it has become necessary to apply web scraping and opinion mining techniques, two primary processes that help to obtain and summarize the data. Opinion mining, among its various areas of application, stands out for its essential contribution in the context of healthcare, especially for pharmacovigilance, because it allows finding adverse drug events omitted by the pharmaceutical companies. This chapter proposes a hybrid approach that uses semantics and machine learning for an opinion mining-analysis system by applying natural-language-processing techniques for the detection of drug polarity for chronic-degenerative diseases, available in blogs and specialized websites in the Spanish language.

scholarly journals MD-MinerP: Interaction Profiling Bipartite Graph Mining for Malware-Control Domain Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Tzung-Han Jeng ◽  
Yi-Ming Chen ◽  
Chien-Chih Chen ◽  
Chuan-Chiang Huang
Keyword(s):  
Network Traffic ◽  
Graph Mining ◽  
Single Point ◽  
Bipartite Graphs ◽  
Traffic Data ◽  
Domain Names ◽  
Advanced Persistent Threat ◽  
Threat Intelligence ◽  
Extraction Stage

Despite the efforts of information security experts, cybercrimes are still emerging at an alarming rate. Among the tools used by cybercriminals, malicious domains are indispensable and harm from the Internet has become a global problem. Malicious domains play an important role from SPAM and Cross-Site Scripting (XSS) threats to Botnet and Advanced Persistent Threat (APT) attacks at large scales. To ensure there is not a single point of failure or to prevent their detection and blocking, malware authors have employed domain generation algorithms (DGAs) and domain-flux techniques to generate a large number of domain names for malicious servers. As a result, malicious servers are difficult to detect and remove. Furthermore, the clues of cybercrime are stored in network traffic logs, but analyzing long-term big network traffic data is a challenge. To adapt the technology of cybercrimes and automatically detect unknown malicious threats, we previously proposed a system called MD-Miner. To improve its efficiency and accuracy, we propose the MD-MinerP here, which generates more features with identification capabilities in the feature extraction stage. Moreover, MD-MinerP adapts interaction profiling bipartite graphs instead of annotated bipartite graphs. The experimental results show that MD-MinerP has better area under curve (AUC) results and found new malicious domains that could not be recognized by other threat intelligence systems. The MD-MinerP exhibits both scalability and applicability, which has been experimentally validated on actual enterprise network traffic.

The intelligence analysis of personal characters about ordinary world

2019 ◽  
Author(s):  
Ying Yuan
Keyword(s):  
Subjective Experience ◽  
Reading Level ◽  
Big Five Personality ◽  
Intelligence Analysis ◽  
The Novel ◽  
Analysis Method ◽  
Psychological Analysis ◽  
Intelligent Analysis ◽  
Personality Prediction ◽  
Analysis System

Abstract Psychological analysis of characters in ordinary novels is mainly a qualitative analysis, which is easily affected by the researchers’ reading level, theoretical literacy, subjective experience, and other factors. With the development of computer technology and big data, stable and systematic personality can more accurately describe the psychology of text characters. This article adopts the method of literary intelligence analysis based on data mining and statistics, through the Chinese psychological analysis system, the language of the characters in the novel of ordinary world can be counted, processed, and disposed, and then obtains the big five personality prediction scores of the characters. Furthermore, the validity of the intelligent analysis method is confirmed by examining the verification of the predictive scores in the text and literature. After verification by many parties, the predicted results of this article are supported by the text and literature, which shows that literary intelligence analysis of novel characters’ personalities is effective.

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