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new dd4e9dad Fix PR #312 (#314)
dd4e9dad is described below
commit dd4e9dad729b0326d0a3f49863ce0688958ccad9
Author: dpdjvhxm <[email protected]>
AuthorDate: Tue Apr 23 10:37:10 2024 +0900
Fix PR #312 (#314)
* Test
* Update gatsby-config.js
* Changes search result text
* Fix gatsby-config.js and title
* Change meta description
* Fix sitemap.xml for broken links
* Update landing page: Key Update and image
* Create Blog
“2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications”
* Update Blog
“2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications”
* Update Blog
“2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications”
* Update blog, cms
* Update blog
* Fix spelling error
* Activate "blog"
* Update download page, modified blog
* modified footer, download
* Update landing image and blog scss
* modified: src/components/styles/Blog-post.scss
* modified: src/pages/download/index.md
* modified: src/templates/blog-post.js
* Create Blog
“2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models”
* Create Blog
“2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis”
---
...use-of-chatgpt-with-interactive-graph-models.md | 69 +++++++++++++++++++++
...-cyber-attack-with-apache-ages-link-analysis.md | 72 ++++++++++++++++++++++
src/templates/blog-post.js | 2 +-
3 files changed, 142 insertions(+), 1 deletion(-)
diff --git
a/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md
b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md
new file mode 100644
index 00000000..a1fb7cf6
--- /dev/null
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b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md
@@ -0,0 +1,69 @@
+---
+templateKey: blog-post
+title: "Beyond ChatGPT: How to Maximize the Use of ChatGPT with Interactive
+ Graph Models."
+date: 2024-04-23T01:18:22.687Z
+description: "Beyond ChatGPT: How to Maximize the Use of ChatGPT with
+ Interactive Graph Models."
+featuredpost: true
+featuredimage: /img/logo.png
+---
+<!--StartFragment-->
+
+n just a matter of a year, ChatGPT has taken the world by storm, ushering in a
new era of AI transformation. Its remarkable text-to-text generation
capabilities have set new standards for informative and high-quality responses.
Knowledge creation AI models like ChatGPT excel at engaging in conversations
and retrieving information from vast databases. However, while it produces
accurate and diverse answers, the delivery in text format can sometimes be hard
to resonate with and fall short [...]
+
+##### The Role of Graph Data Modeling
+
+A graph is a data structure that mirrors the natural interconnectedness found
in the real world, enabling it to offer not just the desired information but
also to suggest and provide additional, related data and information.
+
+Figure 1. Recommendation system use case, expressing the meta information of a
movie through connection relationship in the Graph Model
+
+Learn more about the mechanics of Graph Databases->
+
+##### The intricacies of Input Text & Output Text
+
+To maximize the potential of ChatGPT, we must extend our goal beyond relying
solely on existing text data and embrace the inclusion of "unstructured"
information like images in the knowledge generation process. ChatGPT excels at
delivering accurate answers, but achieving this precision often demands
intricate and nuanced text prompts. Navigation and instruction of ChatGPT could
often be challenging without guidance in the input text methods. In this
regard, it is imperative to harness GP [...]
+
+To extend the information output from ChatGPT meaningfully, the specific
information that the user needs must be identified from the collected pieces of
information derived. In reality, however, GPT offers multifaceted textual
information, which can be overwhelming for users seeking value within its
output. To address this challenge, transforming the information produced by GPT
into a 'knowledge graph' becomes a valuable approach. By mapping the
connections between data points output by [...]
+
+
+
+Figure 2. ChatGPT's text output in over 70 lines
+
+##### Creation and management of GPT knowledge using image information and
graphs
+
+Beyond obtaining information through 'text-to-text', let's elucidate on how to
easily manage knowledge in the form of a graph by inputting images as a service
using an example prompt, 'Generate Food Recipes from the Fridge.
+
+In today's fast-paced world, many individuals have encountered the predicament
of having to prepare a meal when faced with limited ingredients. Furthermore,
tailoring a dish to one's specific dietary needs and personal preferences can
be challenging. Let's tackle this by leveraging ChatGPT's capabilities along
with the knowledge graph framework, empowering users to access top-tier
insights with the simplest of inputs.
+
+
+
+
+
+Figure 3. Information extraction process using ChatGPT from an image of the
ingredients in the fridge
+
+The above two models are used for extracting food recipes and information from
the images of the ingredients in the fridge. As a first prompt, using object
detection algorithms such as R-CNN or YOLO models, the ingredients present in
the fridge are extracted. As for the second input value, it pertains to the
user's unique preferences. To ensure accurate prompts, predefined options such
as select boxes or radio buttons are employed.
+
+In this workflow, both the ingredient list and the user's preferences are
integrated into the ChatGPT prompt using a standardized format. Subsequently,
GPT employs this data to craft a text output encompassing potential dishes,
ingredients, recipes, and nutritional insights tailored to the user's specific
traits, including factors like appetite, age, and time zone. This meticulous
prompt engineering, which leverages both images and user characteristics,
yields high-quality information to [...]
+
+##### Knowledge management using Graphs
+
+When you input ingredients and user preferences into ChatGPT, the generated
output comprises a 'dish,' 'recipe,' available ingredients, and additional
ingredients suitable for the user. In Figure 4. below, by entering items like
cabbage, tomato, and seaweed in your pantry, along with dinner preferences,
dietary requirements, and age, GPT generates results related to 'cooking' and
'nutritional ingredients' as illustrated. This information is easily visualized
through graphs and can be ext [...]
+
+
+
+Figure 4. Utilization of information extracted by ChatGPT using a graph (a
circle with red border is information that can be obtained through ChatGPT, red
dotted arrow indicates additional information that is recommended from
information through ChatGPT)
+
+Given that actual GPT information often spans over 70 lines of text,
comprehending the desired details and their relationships can be challenging.
Utilizing a graphical representation, you can observe that 'cabbage salad' was
suggested as a recommended dish, along with 'salt and pepper' as essential
cooking ingredients. Furthermore, dietary insights like 'dietary fiber' and
'digestion' were provided for users in the 'elderly' age group.
+
+By leveraging these complex information interconnections, users can extract
knowledge from GPT-generated data and discern common patterns among essential
information elements.
+
+This graph model can further be advanced into meal planning recommendations
using the ingredients or nutrients in the food information. For example, by
clicking on the 'cabbage, and 'tomato' nodes of an ingredient and putting them
back into the GPT input prompt, it returns a dish that can be made with those
ingredients, such as 'tomato-cabbage stir-fry'. If one clicks on the 'dietary
fiber' and 'digestion' nodes in the nutrition component, 'older' and 'diet' are
the most relevant charact [...]
+
+This way, knowledge management using graphs enables users to secure visibility
and identity and explore relationships between information to identify
causality and relevance. A simple graph-type UI/UX also helps improve the
productivity of ChatGPT by clicking on the appropriate node providing a data
environment in a simple form that allows other food recommendations without
having to separate prompt engineering.
+
+##### Bringing the utilization of ChatGPT to the next level
+
+ChatGPT holds significant promise for seamless integration with other models
and efficient knowledge management. With a broader data spectrum and
user-friendly graph-based knowledge management, it unlocks new insights and
expands effortlessly across various domains. In particular, Apache AGE makes it
easy to search and extract various knowledge by storing data in a graph. Being
an extension to PostgreSQL, AGE allows a hybrid approach to databases combining
the capabilities of both relati [...]
+
+<!--EndFragment-->
\ No newline at end of file
diff --git
a/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md
b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md
new file mode 100644
index 00000000..b268fada
--- /dev/null
+++
b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md
@@ -0,0 +1,72 @@
+---
+templateKey: blog-post
+title: Combating cyber attack with Apache AGE's link analysis.
+date: 2024-04-23T01:28:20.437Z
+description: |-
+ Combating cyber attack with Apache AGE's link
+ analysis.
+featuredpost: true
+featuredimage: /img/logo.png
+---
+<!--StartFragment-->
+
+##### Cybersecurity Background
+
+In today's world, the internet has transformed how we connect, work, and
access information, bringing unparalleled convenience and connectivity. This
digital revolution, however, has its challenges, especially in cybersecurity.
As we depend more on technology, the need to protect sensitive data and
counteract cyber threats becomes increasingly critical.
+
+Cybersecurity is about safeguarding our computer systems, networks, and data
from theft, damage, or unauthorized access. A key part of this field is network
intrusion detection. It's like a digital watchdog, constantly monitoring
network traffic for any signs of suspicious activities, such as hacking
attempts or unauthorized entries. The aim is to catch and address these threats
early, preventing any harm to our networks or data.
+
+In essence, cybersecurity and network intrusion detection are vital in our
tech-driven world, ensuring our digital safety and resilience against
ever-evolving cyber threats.
+
+##### Data Overview
+
+In the realm of cybersecurity, graph analysis plays a crucial role. To
illustrate this, we use the CIDDS-001 dataset, specifically designed for
network intrusion detection. This dataset serves as a benchmark for evaluating
how different intrusion detection systems perform in identifying network
attacks. It simulates a small business environment, complete with clients and
servers, like email and web servers. The behaviors on these clients are
mimicked using Python scripts, and the dataset [...]
+
+
+
+##### Figure1. Graph Modeling
+
+Graph analysis in cybersecurity involves transforming relational data into a
graph format, consisting of nodes (points) and edges (connections). There are
two main types of graphs:
+
+1. **Homogeneous Graphs:** These have nodes and edges of a single type,
similar to what you'd find in social networks with user nodes connected by
'follow' edges.
+2. **Heterogeneous Graphs:** These contain multiple types of nodes and edges,
like in e-commerce networks where interactions between users and items are
tracked through different types of connections such as 'buy,' 'cart,' and
'view.'
+
+Using the [CIDDS-001
dataset](https://www.hs-coburg.de/forschung/forschungsprojekte-oeffentlich/informationstechnologie/cidds-coburg-intrusion-detection-data-sets.html)
, we create a graph for network flow analysis. This includes nodes
representing start and end points in the network, and edges of various types
like 'benign,' 'dos,' 'portscan,' 'pingscan,' and 'bruteforce.' A key feature
of this graph is the detailed information attached to each node and edge, such
as ports, packet sizes [...]
+
+##### Cypher Query with Analysis Scenario
+
+In this section, we explore how the PostgreSQL extension [Apache
AGE's](https://age.apache.org/) graph analysis capabilities, particularly
through Cypher queries, can be leveraged in cybersecurity. We'll look at three
different scenarios, each demonstrating the power and flexibility of Cypher
queries in analyzing network data.
+
+
+
+##### Figure2. Graph Query with Condition
+
+Figure 2 showcases a Cypher query that focuses on identifying network packets
larger than a certain size. This is a prime example of the 'condition' function
in Apache AGE, which is crucial for pinpointing specific scenarios. Here, the
query filters out packets larger than 10 units, helping analysts spot potential
anomalies or threats in network traffic. This function isn't limited to just
packet size; it can be tailored to various criteria, making it a versatile tool
for cybersecurity a [...]
+
+
+
+##### Figure3. Multi-path Search with Union Query
+
+In Figure 3, we see a union query in [Apache AGE](https://age.apache.org/),
demonstrating its ability to handle complex, multi-path searches. This is
particularly useful in scenarios where you need to analyze data across
different types of connections, like 'eg_portscan' and 'eg_pingscan.' [Apache
AGE](https://age.apache.org/) also enhances this analysis with visual aids,
like representing the weight of packets through the thickness of edges in the
graph, making it easier to interpret th [...]
+
+
+
+##### Figure4. Bi-directional Path Search
+
+The final example, illustrated in Figure 4, is a bi-directional path search.
This Cypher query uncovers one-to-many relationships between nodes, providing a
detailed view of network interactions and potential security incidents. It's
particularly adept at revealing complex patterns and relationships that might
be missed with traditional analysis methods. By tracing paths involving
different types of nodes and connections, this query helps in identifying
multi-step attack patterns and pot [...]
+
+In summary, these scenarios highlight the strength of [Apache
AGE](https://age.apache.org/) in cybersecurity. Through its advanced graph
analysis capabilities, [Apache AGE](https://age.apache.org/) enables
cybersecurity professionals to uncover deep insights into network activities,
detect threats more effectively, and enhance the overall security of network
infrastructures.
+
+##### Conclusion
+
+Throughout this discussion, we've delved into the essentials of cybersecurity,
examined the CIDDS-001 dataset for cyber intrusion analysis, and explored the
dynamic use of Cypher queries in data analysis. The journey underscores the
immense value of graph analysis in cybersecurity, offering profound insights
and robust tools to combat digital threats.
+
+[Apache AGE](https://age.apache.org/) stands out in this landscape,
particularly for its user-friendly approach to graph analysis. The use of
Cypher queries, known for their power and intuitiveness, simplifies complex
data analysis. This language enables cybersecurity experts to craft detailed
queries, uncovering complex attack patterns and suspicious network activities.
Such flexibility is crucial in enhancing threat detection and response
capabilities.
+
+Graph visualization emerges as a key player in making intricate cybersecurity
data more understandable and actionable. By graphically representing network
nodes, edges, and their attributes, it becomes easier for analysts to decode
complex structures, spot anomalies, and respond swiftly to potential threats.
This visual approach aids in proactive security measures, allowing teams to
trace unusual activities, map out attack routes, and pinpoint potential sources
of cyber threats, thereby [...]
+
+Another significant aspect of [Apache AGE's](https://age.apache.org/)graph
analysis is the labeled property graph model. This approach enriches nodes and
edges with detailed metadata, capturing essential information about cyber
incidents. Such rich data representation not only aids in precise threat
categorization but also supports thorough post-incident analysis. This helps
organizations learn from past experiences, enhancing their defensive strategies
against future cyber threats.
+
+In conclusion, [Apache AGE](https://age.apache.org/) presents a powerful suite
of tools for cybersecurity professionals, significantly bolstering their
ability to detect, investigate, and neutralize cyber threats. The combination
of easy-to-use Cypher queries and effective graph visualization positions
[Apache AGE](https://age.apache.org/) as an indispensable asset in contemporary
cybersecurity operations. By adopting graph analysis, organizations can
effectively stay ahead of cyber adve [...]
+
+<!--EndFragment-->
\ No newline at end of file
diff --git a/src/templates/blog-post.js b/src/templates/blog-post.js
index 9fca3d8f..b7b653ca 100644
--- a/src/templates/blog-post.js
+++ b/src/templates/blog-post.js
@@ -5,7 +5,7 @@ import { Helmet } from "react-helmet";
import { graphql, Link, navigate } from "gatsby"; // 'navigate'를 임포트합니다.
import Layout from "../components/Layout";
import Content, { HTMLContent } from "../components/Content";
-import style from "../components/styles/blog-post.scss"
+import style from "../components/styles/Blog-post.scss"
// eslint-disable-next-line
export const BlogPostTemplate = ({
