Cyber Security Machine Learning Projects

Using machine learning, the several cybersecurity tasks are performed to improve the identification abilities, automate processes and counter advanced hazards. Get to know about our recent work in area of cyber security we have completed more than 8000+ projects. All the necessary resources are available and we have huge team of experts to guide you in your project work. So, share with us all your details we will guide you further. Some cyber security topics in machine learning project ideas are listed below,

1. Phishing Website Detection:

• Objective: Depend on URL features, website content and domain registration details, identify the phishing websites and it is considered as our main goal.
• Data: Legal data and phishing URLs are gathered.
• Techniques: This incorporates tools like Random Forest, Gradient Boosting, or Neural Networks.

2. Network Intrusion Detection:

• Objective: In network traffic, errors or malicious activities are recognized by us.
• Data: Make use of datasets like NSL-KDD or CICIDS2017 that includes labeled network traffic.
• Techniques: Deploying this process, execute anomaly detection such as Isolation Forest, One-Class SVM (Support Vector Machine), or LSTM (Long Short-Term Memory)-based sequence analysis.

3. Malware Classification:

• Objective: Depending on attributes, we categorize the files or performable as benign (harmless) or malicious.
• Data: Features are derived from the malware instances like byte sequences, API calls and opcode sequences.
• Techniques: Utilize CNN (Convolutional Neural Networks) on byte sequences or RNNs (Recurrent Neural Networks) for performing opcode sequences.

4. Spam Email Detection:

• Objective: Our emails are organized into legal and junk e
• Data: Datasets occupied in this like Enron Spam Dataset.
• Techniques: NLP (Natural Language Processing) method is executed and some of the models like TF-IDF associated with Naive Bayes or transformers like BERT.

5. User Behaviour Analytics:

• Objective: We identify the errors in user conduct that points out the possible interior assaults or accommodate accounts.
• Data: The log data of user proceedings is assembled.
• Techniques: Sequence modeling is established like LSTMs or clustering for behavioural profiling.

6. DDoS Attack Detection:

• Objective: The (DDoS) Distributed Denial of Service attacks in network traffic are identified by us.
• Data: Network traffic datasets with labeled DDoS activities are the involved data’s.
• Techniques: For detecting unusual traffic patterns, consider time series analysis or deep learning models.

7. Domain Generation Algorithm (DGA) Detection:

• Objective: Our main focus is to identify the fields which are created by malware for orders and manage communication (C2).
• Data: Gather the recognized DGA-generated domains and legal domains.
• Techniques: Take advantage of LSTM or transformer based models for sequence classification.

8. Password Strength Prediction :

• Objective: Through this, we forecast the potency of passwords and possibly how it extended for defending against brute-force assaults.
• Data: The revealed passwords are acquired.
• Techniques: Feature engineering like password length, types of characters and classification models is employed in this method.

9. Secure Code Analysis:

• Objective: It mechanically detects the weakness or defects in source code.
• Data: This collects the data of Source code repositories with labeled vulnerabilities (mentioning weakness).
• Techniques: The tools carried out by us like NLP techniques for observing the code patterns and detecting the errors.

10. Digital Forensics:

• Objective: We program this method for gathering and observing the digital proof after a cybersecurity event.
• Data: It stores the data’s like, Disk images, logs and memory dumps.
• Techniques: Apply feature extraction and machine learning models to categorize and examine the proof.

While performing these types of projects, it is significant to recollect the moral suggestions particularly when working with real data. Constantly anonymize sensitive data, gain required allowances and respect privacy measures.