Data Science and Cybersecurity Project Ideas for Final Year College Students

Jul 18, 2025

Data science and cybersecurity are the two recently evolving areas whose merging serves to solve some of the most vital current issues in the digitised world. Project-based studies for college final-year students in this area offer exciting opportunities to learn while addressing some real-life problems, as well as showcasing their work for potential employers. This blog will explore a few innovative project ideas when data science meets security: project ideas for students who already have some knowledge of programming, statistics, and some computer security fundamentals. The entry will be a short overview of the idea, tools involved, and expected outcomes to help steer students in their contributions.

Why Combine Data Science and Cybersecurity?

A data scientist derives insight from variable complexities in data using statistical methods, machine learning, and data visualisation. It is the technology that safeguards the information systems and network and protects data from different attacks, such as malware, phishing, or unauthorised access. Combining it allows you to proactively detect threats, identify anomalies, and generate predictive analytics that can then be implemented to strengthen the security posture. Projects in this area allow students to utilise algorithms on security data sets, analyse patterns, and arrive at solutions to cope with emerging threats. Such relevance aids both academically and professionally.

Below are six project ideas that can be accomplished within a final-year timeframe while providing flexibility for creativity and technical depth.

1. Intrusion Detection System Using Machine Learning:

Overview:

The main concern is to determine unauthorised network traffic through an intrusion detection system. Denial of Service (DoS) attacks generally affect network traffic and pave the way for unauthorised access. It is an effort toward building a machine learning intrusion detection system that classifies network traffic as benign or malicious using datasets like NSL-KDD or CICIDS2017.

Approach:

Data Collection: A publicly available dataset should be used to collect labelled network traffic data (NSL-KDD, for example).
Preprocessing: This phase cleans the dataset, performs missing value handling, and normalises features such as packet size or protocol type.
Model Development: Develop classification models, including Random Forest, Support Vector Machine, or even a basic Neural Network for detecting intrusions.
Evaluation: Metrics used in the determination of performance include accuracy, precision, recall, and F1-score.
Visualisation: To display the real-time detection results, dashboards can be created through Tableau or Matplotlib.

Tools:

Python (Pandas, Scikit-learn, TensorFlow)
Jupyter Notebook for experimentation
Wireshark for capturing live network traffic (optional)
Tableau or Seaborn for visualisation

Expected Outcomes:

A functional IDS model that accurately detects intrusions with an average accuracy on the test data of almost 85-90%. Students will be exposed to the concepts of feature engineering, tuning of models, and analysis of network traffic.

Challenges:

Working on imbalanced datasets that might haveigher under-representation of malicious traffic.
One aspect of it is to make models that can be relied upon for real-time performance.

2. Phishing Website Detection with Natural Language Processing:

Overview:

Phishing sites act like genuine ones in order to pilfer sensitive information, such as login credentials. This project utilises natural language processing (NLP) in analysing website content and URLs to classify a website as either phishing or legitimate.

Approach:

Collect Data: Collect data from the website or use public datasets such as PhishTank or UCI Phishing Websites.
Feature Extraction: Extract features from URL features such as length or presence of suspicious characters, and then features from webpage text, such as keywords such as "login" or "urgent".
NLP Pipeline: Use libraries such as NLTK or SpaCy for text preprocessing and then methods like TF-IDF or even word embeddings such as BERT.
Model Training: Create a model training to classify phishing sites with a classifier like logistic regression or XGBoost.
Browser Extension (Optional): Develop a Chrome extension that alerts users about probable phishing websites in real-time.

Tools:

Python (NLTK, SpaCy, Scikit-learn, BeautifulSoup)
Selenium for web scraping
Hugging Face Transformers for advanced NLP
Flask for creating a web-based demo

Expected Outcomes:

It is a model that can detect phishing websites with high accuracy (>90%). By keeping this in mind, students will learn NLP techniques, web scraping, and all features that can be derived from unstructured data.

Challenges:

Getting a sufficient and updated dataset.
Balancing for false positives specific to the lagging of suitable pages.

3. Malware Classification Using Deep Learning:

Overview:

Malware classification involves identifying whether a file or program is malicious. This project uses deep learning to analyse static or dynamic features of executable files to classify them as benign or malicious.

Approach

Data Collection: Use datasets like Microsoft Malware Classification Challenge (Kaggle) or VirusShare.
Feature Extraction: With a plethora of available tools, extract static features (file headers, byte sequences, etc.) or dynamic features (API calls, memory usage, etc.), e.g., using PEfile.
Model Development: Either implement a convolutional network (CNN) or a recurrent network (RNN) to classify malware using the extracted features.
Evaluation: Use confusion matrices and ROC curves to assess model performance.
Visualisation: Plot feature importance or malware distribution using Matplotlib.

Tools

Python (TensorFlow, Keras, PEfile)
Cuckoo Sandbox for dynamic analysis
Matplotlib or Seaborn for visualisation
Kaggle for dataset access

Expected Outcomes

A deep learning model with >85% accuracy in classifying malware. Students will gain expertise in deep learning architectures and malware analysis techniques.

Challenges

Processing large binary files efficiently.
Ensuring model generalisability across diverse malware families.

4. Anomaly Detection in User Behaviour for Insider Threat Detection

Overview

Insider threats materialise when authorised users exploit access rights to inflict damage on an organisation. This project employs an anomaly-based detection model that identifies odd behaviours of users in server logs, like login time irregularities or file access patterns.

Approach

Data Collection: Use synthetic datasets like the CERT Insider Threat Dataset or generate logs using simulated user activity.
Preprocessing: Parse logs to extract features like login frequency, file access counts, or session duration.
Anomaly Detection: Apply unsupervised algorithms like Isolation Forest, Autoencoders, or One-Class SVM to detect outliers.
Alert System: Develop a simple dashboard to flag anomalies in real-time.
Evaluation: Validate anomalies against labelled data or manual inspection.

Tools

Python (Pandas, Scikit-learn, PyTorch)
ELK Stack (Elasticsearch, Logstash, Kibana) for log analysis.
Dash or Streamlit for dashboard creation

Expected Outcomes

A system that flags anomalous use behaviour with low false positives. Students will learn about unsupervised learning and log analysis, which are critical for cybersecurity operations.

Challenges

Defining “normal” behaviour in diverse user environments.
Scaling the system for large log volumes.

5. Predictive Analytics for Vulnerability Assessment

Overview

Vulnerability assessment identifies weaknesses in systems that attackers could exploit. This project uses historical vulnerability data to predict which systems or software are most likely to be targeted.

Approach

Data Collection: Use datasets from NIST’s National Vulnerability Database (NVD) or CVE Details.
Feature Engineering: Extract features like vulnerability severity (CVSS scores), affected software, or patch availability.
Model Development: Train a regression model (e.g., gradient boosting) to predict vulnerability exploitation likelihood or a clustering model to group similar vulnerabilities.
Visualisation: Create heatmaps or trend graphs to highlight high-risk vulnerabilities.
Reporting: Generate automated reports summarising predictions.

Tools

Python (Scikit-learn, XGBoost, Pandas)
SQL for querying vulnerability databases
Power BI or Plotly for interactive visualisations

Expected Outcomes

A predictive model that prioritises vulnerabilities for patching, improving system security. Students will gain skills in predictive modelling and vulnerability management.

Challenges

Handling incomplete or noisy vulnerability data.
Interpreting model predictions for actionable insights.

6. Sentiment Analysis of Cybersecurity Threat Intelligence Feeds

Overview

Intelligence feeds provide updates regarding the recent cyber threat. This project applies sentiment analysis to assess the criticality or urgency of such threats from social media or RSS feeds.

Approach

Data Collection: Aggregate threat intelligence from Twitter (using Tweepy) or RSS feeds from security blogs.
Preprocessing: Clean text data, remove noise (e.g., hashtags, URLs), and tokenise content.
Sentiment Analysis: Use pre-trained models like VADER or fine-tune a BERT model to classify sentiment (e.g., neutral, urgent, critical).
Correlation Analysis: Link sentiment scores to threat severity or incident reports.
Dashboard: Build a web app to display sentiment trends and threat alerts.

Tools

Python (Tweepy, TextBlob, Hugging Face)
Flask or Django for web app development
D3.js or Chart.js for visualisations

Expected Outcomes

A dashboard that visualises sentiment trends in threat intelligence, aiding prioritisation of response efforts. Students will learn text mining and web development.

Challenges

Filtering irrelevant or noisy social media data.
Mapping sentiment to actionable cybersecurity insights.

7. Tips for Success

Start Small: Begin with a simple dataset and model, then scale complexity as you gain confidence.
Leverage Open-Source Tools: Use free resources like Kaggle, GitHub, or Google Colab to reduce costs.
Document Your Process: Maintain a project report detailing your methodology, challenges, and results for academic evaluation.
Collaborate: Work in teams to divide tasks like data preprocessing, modelling, and visualisation.
Stay Ethical: Ensure datasets are used responsibly, and avoid accessing live systems without permission.

8. Cyberbullying Detection on Social Media Platforms

Overview:

Employ deep learning algorithms to detect language depicting cyberbullying on platforms like Twitter. It will help improve online safety.

Approach:

Dataset: Kaggle Cyberbullying Datasets, Twitter API
Preprocessing: Remove hashtags, mentions, emojis
Model: LSTM, CNN or BERT
Dashboard: Monitor bullying content in real time
Sentiment classification (optional)

Tools:

Python, TensorFlow, Keras, Hugging Face, Streamlit

Challenges:

Identification of sarcasm and indirect bullying
Adapting to evolving slang
Ethical issues surrounding the user data and privacy

9. Password Strength Classification Using Machine Learning

Overview:

Predict password strength using machine learning, helping users create secure passwords beyond just rule-based checks.

Approach:

Dataset: RockYou, public password leaks
Feature Engineering: Length, digits, symbols, repetition
Model: Random Forest, Neural Networks
Evaluation: Classification report, suggestions for improvement
Optional: GUI for real-time password testing

Tools:

Python, Scikit-learn, Regex, Tkinter

Challenges:

Access to large, diverse password datasets
Privacy issues when analyzing real passwords
Balancing complexity with usability for non-technical users

10. Ransomware Detection Based on File Behaviour

Overview:

Detection of ransomware activity through the identification of abnormal file activity like mass encryption or renaming in real-time.

Approach:

Dataset: Simulated logs or Cuckoo Sandbox
Feature Extraction: File changes per minute, entropy scores
Model: Isolation Forest, Decision Tree
Alert System: Warn users of ransomware-like actions
Visualisation: Live monitoring charts

Tools:

Python, Cuckoo Sandbox, Scikit-learn, Streamlit

Challenges:

Generating realistic ransomware behavior for training
Real-time monitoring without system lag
Minimizing false alerts on legitimate mass file actions

11. Network Traffic Analyser for IoT Devices

Overview:

Monitor and analyze network traffic from IoT devices to identify potential threats or unusual communication patterns.

Approach:

Dataset: UNSW-NB15 or custom traffic capture
Feature Extraction: Protocol type, traffic frequency, port analysis
Model: K-Means, One-Class SVM, DBSCAN
Dashboard: Real-time visualization of device behavior.

Tools:

Python, Wireshark, Scapy, Scikit-learn, Dash

Challenges:

Limited labeled IoT traffic datasets
Differentiating legitimate spikes from threats
Dealing with encrypted traffic

12. DNS Anomaly Detection for Covert Tunneling

Overview:

Identification of DNS tunneling attacks by spotting patterns within DNS requests which look like covert channels for data exfiltration.

Approach:

Dataset: Simulated PCAPs, DNS logs
Feature Engineering: Domain length, query timing, entropy
Model: Isolation Forest, Autoencoder, Rule-based filter
Visualisation: Query frequency trends, domain patterns

Tools:

Python, PyShark, Scikit-learn, Wireshark

Challenges:

Generating realistic DNS tunneling traffic
High false positive rate due to similar benign behavior
Need for packet-level traffic analysis

13. Vulnerability Pattern Mining from CVE Data

Overview:

Analyze and predict vulnerability trends using past data from CVE/NVD databases to assist patch prioritization.

Approach:

Dataset: NVD API, CVE Details
Feature Engineering: CVSS score, vendor, product, attack vector
Model: K-Means, DBSCAN for performing clustering
Visualisation: Heatmaps, trend graphs, dashboards
Reporting: Automatic summary of critical vulnerability clusters

Tools:

Python, Pandas, Scikit-learn, Plotly, Power BI

Challenges:

There could be missing or incomplete fields in the dataset
Interpreting technical metadata into meaningful insights
Keeping data up-to-date as new vulnerabilities are added

14. Encrypted Traffic Analysis Using Machine Learning

Overview:

Many attackers now use HTTPS or VPNs to hide malicious activity. This project builds a machine learning system to detect threats based only on encrypted traffic metadata (packet size, timing, flow patterns).

Approach:

Collect encrypted traffic flows using tools like Wireshark or CIC-IDS datasets.
Extract features like flow duration, packet inter-arrival time, burst size.
Train supervised models like Random Forest or LSTM for classification.
Evaluate on test data using confusion matrix and ROC curves.

Tools:

Python (Scikit-learn, Pandas, Matplotlib)
Wireshark for traffic capture
Jupyter Notebook

Expected Outcomes:

A system that identifies malware communication with >85% accuracy without decrypting any payload.

Challenges:

Identifying patterns in metadata without content.
Avoiding high false positives.
Ensuring real-time scalability.

15. AI-Powered Email Threat Detection with Explainable NLP

Overview:

This project uses transformer-based NLP models like BERT to detect phishing or social engineering in email bodies and headers. Explainability tools like SHAP highlight risky content for users.

Approach:

Collect phishing emails from sources like SpamAssassin or PhishTank.
Clean and tokenize email content using NLTK or SpaCy.
Train a BERT model and fine-tune it on the dataset.
Use SHAP or LIME to highlight suspicious parts of the email.

Tools:

Python (Hugging Face Transformers, NLTK, SHAP)
Flask for web app deployment

Expected Outcomes:

An accurate phishing detection system with visual feedback on why the email is flagged.

Challenges:

Balancing accuracy and false positives.
Generalizing to new phishing techniques.
Explaining AI predictions clearly.

16. Graph Neural Network for Malware Detection

Overview:

Malware behavior can be modeled as graphs — such as system calls or binary control flow. This project uses Graph Neural Networks (GNNs) to classify malware families.

Approach:

Extract graphs from executables using PEfile or Radare2.
Represent malware behavior as nodes (API calls) and edges (call sequence).
Train a GNN to classify these graphs into malware/benign.
Use t-SNE to visualize high-dimensional embeddings.

Tools:

Python (PyTorch Geometric, NetworkX)
PEfile for binary analysis
Matplotlib for visualization

Expected Outcomes:

High-accuracy classification of advanced malware using graph-based models.

Challenges:

Extracting meaningful graphs from obfuscated binaries.
Requires deep understanding of GNN architecture.
Model size and training time.

17. Zero-Day Vulnerability Forecasting Using Predictive Analytics

Overview:

Use data from CVE and NVD databases to predict which software vulnerabilities might be exploited in the near future based on severity, vendor, patch history, etc.

Approach:

Scrape data from NIST’s NVD and CVEDetails.
Perform feature engineering on severity, attack vector, patch delay.
Train regression and classification models to predict “exploitability score”.
Create dashboards to highlight risky software products.

Tools:

Python (XGBoost, Pandas, Seaborn)
SQL for querying vulnerability databases
Plotly or Power BI for dashboards

Expected Outcomes:

A model that forecasts risk level of new vulnerabilities and prioritizes patching.

Challenges:

Incomplete or delayed data entries.
Correlating prediction with real-world exploits.
Building explainable models.

18. Cyber Threat Intelligence Analysis from Dark Web Forums

Overview:

Collect and analyze cybersecurity threats being discussed in hacker forums using NLP. Detect keywords like “0day,” “exploit,” or “credentials” to alert analysts.

Approach:

Use web scraping (Tor + BeautifulSoup) to gather forum content.
Clean and analyze text using NLP techniques (TF-IDF, LDA).
Train classifier to tag posts as “relevant” or “irrelevant.”
Visualize topic trends over time.

Tools:

Python (Scrapy, BeautifulSoup, NLTK)
Tor browser or proxies for access
WordCloud, Matplotlib

Expected Outcomes:

A dashboard showing trending cyber threats being discussed in real time.

Challenges:

Accessing the dark web safely and ethically.
Filtering irrelevant or spam posts.
Language and slang variations.

19. Insider Threat Detection Using Behavioral Biometrics

Overview:

This project uses behavioral biometrics like typing speed, mouse movement or login time to detect insider threats or unusual activity from authorized users.

Approach:

Collect biometric behavior logs from multiple users (simulated or real).
Extract features like average typing interval, click frequency, off-hours logins.
Train unsupervised models (One-Class SVM, Isolation Forest) to flag anomalies.
Integrate real-time alert dashboard.

Tools:

Python (PyCaret, Scikit-learn, Streamlit)
Pynput for user interaction logging

Expected Outcomes:

A lightweight agent that flags suspicious user behavior based on patterns.

Challenges:

Defining “normal” per user.
Avoiding privacy violations.
Limited datasets for biometric behavior.

20. IoT Device Fingerprinting Using Machine Learning

Overview:

Identify and classify unknown IoT devices on a network using their traffic patterns and behavior rather than MAC addresses.

Approach:

Capture network traffic from various IoT devices.
Extract features like inter-packet intervals, TCP flags, device wake times.
Train a model (KNN or SVM) to recognize and label devices.
Flag unknown or spoofed devices.

Tools:

Python (Scikit-learn, Pandas)
Wireshark or tcpdump
Jupyter Notebook

Expected Outcomes:

A tool that detects unauthorized or spoofed IoT devices accurately.

Challenges:

Device behavior may change over time.
Dataset collection from multiple devices.
MAC spoofing evasion.

21. AI-Powered Log Anomaly Detection Engine

Overview:

Developing a real-time log analysis system that is based on AI. This system is for the identification of anomalies in system logs (SSH, DB access, application logs).

Approach:

Ingest log data using ELK Stack (Elastic, Logstash, Kibana).
Clean and parse data using regex and Python.
Train Autoencoders or LSTM models for anomaly detection.
Display anomalies with timestamps and severity in dashboard.

Tools:

Python (TensorFlow, PyTorch)
ELK Stack
Kibana/Streamlit for visualization

Expected Outcomes:

A smart anomaly detection system with low false positives, usable in SOCs.

Challenges:

Parsing varied log formats.
Modeling temporal behavior.
Large log volume processing.

Conclusion

These project ideas offer final-year students a chance to explore the synergy of data science and cybersecurity while addressing real-world challenges. Whether detecting intrusions, classifying malware, or predicting vulnerabilities, each project builds technical skills and demonstrates practical applications. By choosing a project aligned with their interests and skill levels, students can create compelling portfolios that stand out in the job market. Start exploring these ideas, experiment with tools, and contribute to a safer digital world!