Intelligent Intrusion Detection System

Information and communications technology (ICT) systems and networks handle various sensitive user data that are prone by various attacks from both internal and external intruders. These attacks can be manual and machine generated, diverse and are gradually advancing in obfuscations resulting in und

2025-06-28 16:27:59 - Adil Khan

Project Title

Intelligent Intrusion Detection System

Project Area of Specialization Cyber SecurityProject Summary

Information and communications technology (ICT) systems and networks handle various sensitive user data that are prone by various attacks from both internal and external intruders. These attacks can be manual and machine generated, diverse and are gradually advancing in obfuscations resulting in undetected data breaches. Malicious cyberattacks pose serious security issues that demand the need for a novel, flexible and more intellegent intrusion detection system (IDS). An IDS is a proactive intrusion detection tool used to detect and classify intrusions, attacks, or violations of the security policies automatically at network-level and host-level infrastructure in a timely manner.

Project Objectives

By combining both (Network Intrusion Detection System) NIDS and (Host Intrusion Detection System) HIDS collaboratively, an effective deep learning approach is proposed by modeling a deep-neural network (DNN) to detect cyberattacks proactively. The efficacy of various classical machine learning algorithms and DNNs are evaluated on various NIDS and HIDS datasets in identifying whether network traffic behavior is either normal or abnormal due to an attack that can be classified into corresponding attack categories. 

Project Implementation Method

We use deep learning algorithms and machine learning in this project.

We can also use python libraries.

Software methodology:

Agile Development Methodology:

           The main focus of this methodology is the project/product itself. That is why, it presupposes various constant alterations based on users and customers feedback, as well as internal changes related to the work of engineers. Agile software development methodology is free of rigid frameworks on the one hand. While, on the other hand, the working process is divided into short time boxes, thus offering the real results and feedbacks truly fast.

Benefits:

Problems are fixed at the early stage, so the quality of the final product is often top-notch.

Drawbacks: 

It is easy to get off track with all the constant changes and amendments aimed at improving the product.

Tools and technologies:

Python

PyCharm

Machine Learning

Deep Neural Networks

Benefits of the Project

It improves security in networks.

Although IDS is typically a passive system, some active IDS can, along with detection and generating alerts, block IP addresses or shut down access to restricted resources when an anomaly is detected.

Technical Details of Final Deliverable

In this part of deep learning, a model is developed as an optimization 

algorithm for first-order gradient-based optimization of a stochastic objective 

function to obtain maximum accuracy of classification ratios for intrusion detection 

system. This optimizer works on adaptive estimation of low-order instants. Use this 

method to activate a model developed as a pre-implementation process. Raw data is 

initially collected from CTU-13 dataset in the PCAP format and subsequently 

transformed to select the relevant network flow parameters.

Final Deliverable of the Project Software SystemCore Industry SecurityOther Industries IT , Telecommunication Core Technology Internet of Things (IoT)Other Technologies OthersSustainable Development Goals Responsible Consumption and ProductionRequired Resources
Item Name Type No. of Units Per Unit Cost (in Rs) Total (in Rs)
Total in (Rs) 75000
Pycharm paid tools Equipment12000020000
Machine learning tools Equipment11000010000
Other deep learning tools Equipment13500035000
Stationary and papers etc Miscellaneous 11000010000

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