Cyber Hacking Breaches Prediction and Detection Using Machine Learning

IEEE BASE PAPER TITLE:

Cyber Hacking Breaches Prediction and Detection Using Machine Learning

(or)

OUR PROPOSED PROJECT TITLE:

Enhancing Cybersecurity: Predicting and Detecting Cyber Hacking Breaches Using Machine Learning and Random Forest Classifier

IEEE BASE PAPER ABSTRACT:

Cyber hacking breaches prediction is one of the emerging technologies and it has been a quite challenging task to recognize breaches detection and prediction using computer algorithms. Making malware detection more responsive, scalable, and efficient than traditional systems that call for human involvement is the main goal of applying machine learning for breaches detection and prediction. Various types of cyber hacking attacks any of them will harm a person’s information and financial reputation. Data from governmental and non-profit organizations, such as user and company information, may be compromised, posing a risk to their finances and reputation. The information can be collected from websites that can trigger cyberattack. Organizations like the healthcare industry are able to contain sensitive data that needs to be kept discreet and safe. Identity theft, fraud, and other losses may be caused by data breaches. The findings indicate that 70% of breaches affect numerous organizations, including the healthcare industry. The analysis displays the likelihood of a data breach. Due to increased usage of computer applications, the security for host and network is leading to the risk of data breaches. Machine learning methods can be used to find these assaults. By research, machine learning models are utilized to protect the website from security flaws. The dataset can be obtained from the Privacy Rights Clearinghouse. Data breaches can be decreased by educating staff on the use of modern security measures. This can aid in understanding the attacks knowledge and data security. The machine learning models like Random Forest, Decision Tree, k-means and Multilayer Perceptron are used to predict the data breaches.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Random Forest Classifier.

OUR PROPOSED PROJECT ABSTRACT:

In an era where cybersecurity threats have become increasingly sophisticated, the need for robust prediction and detection systems to safeguard against cyber hacking breaches is paramount. This project presents a novel approach to address this concern, employing Machine Learning techniques, specifically the Random Forest Classifier, to predict and detect potential cyber hacking breaches.

Implemented in Python, the proposed system utilizes a carefully curated dataset of 5457 URLs, encompassing 87 extracted features. Crucially, the dataset maintains a balanced composition, precisely divided between 50% phishing and 50% legitimate URLs. The project’s primary focus lies in accurately identifying cyber threats while minimizing false positives. Through rigorous training and evaluation, the achieved results demonstrate the system’s remarkable performance.

The Random Forest Classifier attains a commendable training accuracy of 99%, ensuring its ability to discern patterns and distinguish between legitimate and malicious URLs. The model also showcases a robust test accuracy of 91%, further validating its reliability in real-world scenarios.

In conclusion, this project stands as a pioneering effort in the realm of cyber hacking breach prediction and detection, harnessing the power of Machine Learning and the Random Forest Classifier to offer enhanced security measures. The remarkable accuracy achieved serves as a testament to its effectiveness, empowering organizations to fortify their cybersecurity defenses against potential cyber threats and attacks.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 6 GB

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10 / 11.
• Coding Language : Python 3.10.9.
• Web Framework : Flask.
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

K Pujitha; Gorla Nandini; K V Teja Sree; Banda Nandini; Dhodla Radhika, “Cyber Hacking Breaches Prediction and Detection Using Machine Learning”, 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), IEEE Conference, 2023.

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