Automated Cyber Threat Identification Using Natural Language

Processing

Parumanchala Bhaskar, Kandukuri Ramachari, Shaik Arbas Basha, Kamireddy Vivekananda Reddy,

Bandi Malleswara Reddy and Suravi Ravi Teja

Department of Computer Science and Engineering, Santhiram Engineering College, Nandyal, Andhra Pradesh, India

Keywords: Intelligence on Cyber Threats, Cybersecurity, Natural Language Processing (NLP), Automated Threat

Recognition, Analysis of Threats, Machine Learning, Deep Learning, Information Mining, Extraction of

Information.

Abstract: This abstract paper addresses this challenge through the application of Natural Language Processing (NLP)

to facilitate the automation of cyber threat detection. The suggested system uses modern NLP methods to

interpret large volumes of textual data from sources including cybersecurity reports, social media, forums and

dark web conversations. In an increasingly digital world, cyberthreats are becoming more common and

present a major security and privacy threat. As malevolent behaviour is dynamic, most conventional threat

detection mechanisms tend to lag. The solution aims to enhance the resilience of digital infrastructures against

cyberattacks by improving the precision, effectiveness, and scalability of threat detection. As there are rapid

dynamics of the digital world, the cyberattacks are also growing to be more sophisticated and bigger than

ever. This research will solve this basic problem by creating a mechanised system for cyber threat

identification using some Natural Language Processing (NLP). The solution aims to strengthen the defences

of digital infrastructures against cyberattacks by increasing the precision, effectiveness and scalability of

threat detection.

1 INTRODUCTION

The Problem: We have tons of information about

potential cyber threats. This information is scattered

everywhere – in security logs, reports, social media,

etc. It's too much for humans to handle, and it's hard

to find the real threats quickly. Plus, the threats are

always changing.

The Solution: NLP can help! Think of NLP as

teaching computers to understand human language.

Rather than simply focusing on words, computers

have the ability to grasp the meaning behind the

words.

How NLP Assists: Makes Sense of the Data: NLP

can analyse all that intricate threat data (logs, reports,

posts) and decipher it. It’s akin to having a super-fast

reader who understands what it is reading. NLP is a

branch of artificial intelligence that focuses on

analysing, comprehending, and producing the

languages that people naturally use to facilitate

computer interactions using human natural languages

instead of computer languages. Natural language

processing allows computers to interact in a manner

akin to human speech.

Discovers Patterns: NLP is capable of uncovering

hidden patterns and clues that might suggest a cyber-

attack is happening or is about to happen. It has the

ability to recognize connections that humans might

overlook. NLP allows computers to comprehend and

process large amounts of text data to identify cyber

threats more efficiently and swiftly than humans can

do alone. It’s like giving cybersecurity experts a

powerful assistant that can read and understand

everything!

2 LITERATURE REVIEW

Web applications in the current era play an extremely

crucial part in personal life as well as in the progress

of any nation. Web applications have experienced an

extremely fast evolution during the recent years and

their acceptance is increasing at a quicker pace than

was anticipated some years ago. In these days,

614

Bhaskar, P., Ramachari, K., Basha, S. A., Reddy, K. V., Reddy, B. M. and Teja, S. R.

Automated Cyber Threat Identiﬁcation Using Natural Language Processing.

DOI: 10.5220/0013887300004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

614-619

ISBN: 978-989-758-777-1

trillions of transactions are made online with the help

of various Web applications. Although these

applications are accessed by hundreds of users, in

most instances the security level is low, and hence

they are prone to get compromised. In the majority of

the scenarios, a user must be authenticated before any

communication is made with the backend database.

An arbitrary user must not be granted access to the

system without evidence of valid credentials.

Nevertheless, a crafted injection provides access to

unauthorized users. This is primarily achieved

through SQL Injection input. Despite the emergence

of various methods to avoid SQL injection, it

continues to be a threatening issue for Web

applications. In this paper, we have provided an

in-depth survey on various SQL Injection

vulnerabilities, attacks, and prevention methods.

Besides discussing our results from the research, we

also write down future prospects and potential

evolution of countermeasures for SQL Injection

attacks.

The Internet is Important but RiskWe rely on the

internet for everything, but it's also full of dangers

like cyberattacks.

Threat Intelligence Helps: To fight these attacks,

we use "threat intelligence". This is like gathering

clues about upcoming attacks, including details

regarding the attacker’s methods (“signatures”). This

prepares us. Where We Obtain Clues: We gather

these clues from different places:

Formal Sources: Authorized organizations that

share threat information in a methodical, organized

way (like a formal report).

Informal Sources: More casual sources, such as

news articles, blogs, or discussions.

Organized Clues are Ideal: When the clues are

structured (“organized”), security tools can more

readily understand them and take automated

measures to keep us protected.

In summary: We collect signs of cyberattacks

from multiple sources. The more organized these

pieces of information are, the more effectively we can

shield ourselves. However, the slide indicates that

there remains a significant amount of unstructured,

chaotic information that is challenging to utilize, and

that’s where the new danger arises.

3 EXISTING RESEARCH

Current research has examined multiple aspects of

NLP-based cyber threat detection, including:

Automated phishing identification using machine

learning techniques.

Real-time threat surveillance on social media

utilizing NLP strategies.

Automated extraction of threat intelligence from

security documents. Implementation of message

queuing and stream processing for handling large data

volumes. Studies have also explored the use of NLP

models in cloud environments to achieve scalability

and efficiency.

Drawback in Existing System:

Contextual Noise: A lot of natural language

depends on context and has ambiguity. Potentially,

the same name or term can have different meanings

based on context, creating difficulties in prediction

and makeup of evolving cyber threats.

Domain-Specific Models: Most of NLP models

do not generalize well across domains.

sectors, or languages. A model which might have

trained up to a specific data category may differ

completely in another scenario.

Extractable knowledge and Trust: Natural

Language Processing models are often described as

black-boxes, and these are not straightforward for us

humans to interpret This ambiguity can erode trust

and limit broad use.

Adversarial Attacks: Similar underlying to

images, NLP models can fall victim to adversarial

layouts where malicious actors intentionally shape

input data to fool the model.

4 PROPOSED SYSTEM

The framework coordinates three fundamental

elements: first, the identification of cyber threats and

their classification; second, the profiling of these

identified threats, distinguishing their motives and

goals through a sophisticated machine learning

architecture; and third, the issuance of alerts based on

the danger posed by the identified threats. A

significant innovation in our work lies in our

approach to define these emerging threats, providing

contextual understanding of their motives. This

improved layer of understanding not only enhances

threat detection but also offers avenues for effective

countermeasures. In our experimental research, the

profiling stage achieved an impressive F1 score of

77%, demonstrating a strong ability to identify and

understand identified threats. " "This Paper leads the

forefront of proactive cybersecurity strategies, aiming

to equip defenders with a sophisticated system

capable of performing early threat detection and

advanced threat characterization. By utilizing a rich

source of event data and advanced machine learning

techniques, the framework not only identifies threats

Automated Cyber Threat Identiﬁcation Using Natural Language Processing

615

but also delves deeper into their motives, providing

valuable insights for proactive defence strategies

against rapidly evolving cyber threats.

The Problem: The rising quantity and complexity

of cyber threats necessitate automated and scalable

solutions for early detection and mitigation.

Traditional security systems that rely on manual

analysis are ineffective and susceptible to human

errors. Natural Language Processing (NLP) offers a

powerful method to automate the identification of

cyber threats from text data, enabling real-time

analysis and proactive defence.

The Solution: Researchers created a system to

combat them. It operates in three stages: Identify

Threats: Recognizes and tags the threats to

cyberspace.

Profile Threats: Establishes the intentions of the

attacker using machine learning (a form of AI). This

uncovers what the attackers intend to achieve.

Generate Alerts: Based on the threat's severity,

alerts are generated by the system.

Main Idea: The core of this system is how it

discerns what the attackers are trying to accomplish.

Understanding the attackers' goals allows defenders

to be better prepared and react effectively.

Advantages of Proposed System:

Real-time Threat Detection: NLP can process and

analyse large volumes of unstructured data quickly.

The sooner the threat is identified, the more

opportunities organizations have to act to counter it

before it can do damage.

Adaptability to Emerging Threats: NLP models

can be continuously trained and updated to stay in

line with evolving cyber threats. The system can

remain relevant, compare and contrast its past

decisions with new data & retraining the models at

regular intervals to detect new threats.

Enhanced Situational Awareness: NLP systems

increase situational awareness by processing and

interpreting natural language. Organizations gain

insight into the evolving threat landscape,

adversarial techniques, and can take proper actions

with regard to cybersecurity.

Cost-Effectiveness: Reduces manual labour by

using NLP-automated threat detection that works on

large volumes of text data. This economical approach

enables organizations to optimize resource allocation

and devote efforts to strategic cybersecurity

initiatives.

5 METHODOLOGY

Data Collection and Preprocessing

A pipeline will be built to harvest text data around

the web/post-processed. Text cleaning, tokenization,

normalization, etc., will be automated in data

preprocessing. TF-IDF, word embeddings are some

of the techniques that will be used for feature

extraction. Manh power from message queueing

technologies will be used to process data streams.

Development and Deployment of NLP Models:

“NLP models (for Machine Learning models and

Deep Learning models) will be created for the

identification and classification of threats. The

models will be tuned to latency and throughput for

real-time processing. Your models will be deployed

at scale, such as in a cloud environment or

containerized environment. "

Real-Time Threat Detection: A real-time threat

detection system will be implemented, utilizing

stream processing technologies. " The system will

continuously analyse incoming data streams and

trigger alerts for detected threats.

Evaluation Metrics: Accuracy, precision, recall,

F1-score, latency, throughput, and scalability

metrics.

6 ARCHITECTURES

Figure 1 illustrates the proposed architecture for

cyber threat detection utilizing Natural Language

Processing (NLP) techniques.

Figure 1: Architecture of cyber threat detection using NLP.

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 Application File (App.py):

The process begins with an application file, denoted

as App.py. This represents the user- initiated program

or script designed to interact with the cyber threat

platform.

 File Execution via Jupiter/Anaconda3:

The App.py file is executed using Jupiter Notebook

or Anaconda3, which are powerful environments for

Python development and data science.

 Local Host Address Generation:

Upon

execution,

the

application

generates

local

host

address,

specifically

https://127.0.0.5000.

 Address Copying:

The generated local host address

(https://127.0.0.5000) is copied for subsequent

access.

 Chrome Website Access

The copied address is subsequently used to launch

the service via the Chrome web browser.

This clarifies the use of a standard web browser for

engaging with the local service. The choice of

Chrome indicates suitability and ease for the user

 Search Address Input:

The copied address is pasted into the browser's

search bar or address field.

 Main Website of Cyber Threat Platform:

The browser navigates to the main website of the

cyber threat platform, based on the provided address.

 Platform Interaction:

The platform offers various interactive options,

including "Home," "About," "Sign-up," and "Sign-

in."

7 EXPECTED OUTCOMES

This Paper aims to:

 Develop an automated framework for real-

time cyber threat identification.

 Enhance the efficiency and effectiveness of

threat detection through automation.

 Provide a scalable and adaptable solution

for large-scale data analysis.

 Improve the speed of response to cyber

threats.

 Future research will focus on:

 Integrating automated threat response

capabilities.

 Improving the explainability of real-time

threat detection models.

 Implementing adaptive learning techniques

for continuous model improvement.

 Building a report to future screening of

identified threats.

8 CONCLUSIONS

We researched a range of machine learning

algorithms Naive Bayes, SVM, KNN, Random

Forest, Bagging, Boosting, Neural Networks, and

Voting Classifier in this research that are specifically

well-suited for various classification and prediction

tasks. These algorithms find

important applications across a range of fields

from text classification and image recognition to

anomaly detection and ensemble learning. What

makes them work is their capability to manage

complex data relationships, respond to differing

datasets, and make accurate predictions. In the future,

machine learning algorithms for applications such as

cybersecurity, health diagnostics, and autonomous

systems have promising prospects. Developments in

deep learning and reinforcement learning are poised

to make algorithms even better, allowing more

advanced applications in real-world situations.

Additionally, combining these algorithms with new

technologies like edge computing and quantum

computing could provide new paths for processing

speed and accuracy.

9 FUTURE SCOPE

Future work may include optimizing such algorithms

to run in real-time, enhancing explainability via

model explainability methods and exploring how

these can be combined with other new technologies

such as blockchain for enhanced security and

transparency in data-driven systems. More research

work will also be directed towards improving the

scalability of algorithms, adversarial robustness and

addressing the ethical considerations in the

deployment of AI systems in various domains.

10 RESULTS

The Figure 2 below represents the classification of

Cyber Threat Types using the proposed model

Automated Cyber Threat Identiﬁcation Using Natural Language Processing

617

Figure 2: Class distribution of cyber threat types.

The below Figures 3 and 4 represents mostly used

words and most frequently used words in the given

dataset.

Figure 3: Most commonly used words.

Figure 4: Most frequent words used in texts.

The below Figures 5,6 and 7 shows the Process of NLP

is Started, Cyber Threat Not Detected and Cyber Threat

Detected.

Figure 5: Process of NLP is started.

Figure 6: Cyber threat not detected.

Figure 7: Cyber threat detected.

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