Intrusion Detection System Using Machine Learning Project

The detection of malicious activity or disobeying the rules in network traffic, Intrusion Detection Systems (IDS) plays an important role in network security. Drafting a research proposal under machine learning is an integral part of PhD and MS process. Our research proposal presentation will give a good impression as we have skilled writers to finish the paper within the prescribed time. Scholars may not have a prior experience in research field and paper works, our vast experience professional will track you on right path   so that your paper gets published in high reputable journals. By executing the machine learning in Intrusion Detection System, it offers us the dynamic learning, adaptability and permits the system to identify new hazards across the static rule sets.

The procedure for developing IDS (Intrusion Detection Systems) using machine learning is depicted below:

  1. Objective Definition

The main objective is creating an Intrusion Detection System based on machine learning for detecting the unauthorized access or malicious activities by us in network traffic.

  1. Data Collection
  • We use datasets which contains network traffic logs; one of the famous dataset is KDD cup 1999 dataset, such latest datasets like CICIDS 2017.
  • Both normal traffic and different types of attacks must include on a data.
  1. Data Pre-processing
  • Feature Selection: The features are some of not necessary and not informative. The significance of feature helps us in reducing the common feature.
  • Data Cleaning: It controls the missing values and the anomalies.
  • Normalization: The numerical feature is measured on a similar scale.
  • Encoding: The process is for converting the categorical data into numerical format eg. Protocol type.
  1. Model Selection and Development
  • Decision Trees & Random Forests: It offers good understanding for us and tackles the mixed data types.
  • Support Vector Machines: This is highly powerful for high-dimensional data.
  • Neural Networks: If the dataset is huge and the sequential patterns are essential, it utilize in Deep learning models, particularly in Recurrent Neural Networks (RNNs) or 1D Convolutional Neural Networks,
  • Ensemble methods: We enhancing the algorithms which integrate with more weak learners to create strong IDS. For example, XGBoost or AdaBoost .
  1. Training the Model
  • Splitting the Data: The data is separated by us in categories like, training, validation and test set.
  • Class Imbalance: Compared to normal traffic, the attacks are unusual in this process. The techniques like SMOTE or synthetic data generation deploys for controlling the imbalances.
  • Training: On the training set, the model is trained and then using validation set, it must validate.
  1. Model Evaluation
  • For calculating the model performance, we utilize metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are important.
  • It contributes the significant suggestion of wrong negatives (missed attacks) and then the model must contain high capacity of retrieving information.
  1. Optimization and Hyper parameter Tuning
  • The model hyper parameters are modified and adjusted for the excellent performance. The process that we performs in Bayesian or Grid search optimization.
  1. Deployment
  • We observe the real-time traffic through the combination of the trained (IDS) Intrusion Detection System into the network architecture.
  • An alert mechanism is setting up for informing the executives of possible intrusions.
  1. User Interface and Feedback Loop
  • The dashboard is developed by us for network executives to notify the alerts, assess threat levels and provides reviews on wrong accuses on positives and negatives.
  • The gained review helps for frequently refine and retrain our model.
  1. Conclusion and Future Enhancements
  • The result of the project, capability of model and the challenges faced must outline in this method.
  • The advancements in future includes,
  • We combine multiple data sources like system logs.
  • Unsupervised learning or semi-supervised techniques are included for identifying the new types of attack.
  • Usually, update our model with latest threat on intelligence.


  • Feature Engineering: The field knowledge is beneficial for us in powerful engineering features. Such as, the count of unsuccessful login attempts in a short duration which indicates the brute force attack.
  • Continuous Learning: Typically, new techniques are rapidly evolving and it is very important to keep refining our model with fresh data and patterns.

 Not to forget, Machine learning especially improves the IDS (Intrusion Detection System) and makes them more powerful. Integrating with other security measures is very essential, so that it does not depend on it. Let us consider the IDS system based on machine learning is a piece of multi-layered security strategy.

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Intrusion Detection System using Machine Learning Project Topics

Intrusion Detection System Using Machine Learning Thesis Ideas

Entire thesis ideas and topics will be written right from the introduction to conclusion as per scholars’ request by our experts. Thesis topics will be suggested based on your unique research goal. From scratch we prepare your research work. We guarantee that your thesis work will be kept 100% confidential. The topics that we have worked are listed below.

  1. 1. An Efficient Network Intrusion Detection System for Distributed Networks using Machine Learning Technique


Network Intrusion Detection System (NIDS), Distributed Denial of Service (DDoS), Random Forest (RF), Machine Learning, distributed Networks, Accuracy, Support Vector Machine (SVM)

            We propose a Big Data-based Distributed Denial of Service Network Intrusion Detection System and in our study the micro-batch data processing is engaged for traffic feature collection in the network collection module and Random Forest based classification method is utilized in traffic detection module for feature selection. To store a large amount of wary attacks we used Hadoop File System (HDFS).         To suggest a solution we used S park. Our work can be compared with the ML methods like DT, PCARF, NB, SVM and LR to get a high accuracy.

  1. A Machine Learning-Based Intrusion Detection System for IoT Electric Vehicle Charging Stations (EVCSs)


Anomaly detection; cyber security; feature selection; Internet of Things (IoT); intrusion detection system (IDS); security

            Our paper proposes a classifier method to detect malicious traffic in IoT environment by utilizing a ML method. We used a real IoT dataset that can obtained from real IoT traffic. We have been evaluated the multiple classifying methods and by utilizing our proposed method in IoT-based IDS engine that serves electric vehicle charging station that will bring stability and to remove a large number of cyberattacks.

  1. Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey


Adversarial attacks, deep learning

            We review about the recent literature on NIDS, adversarial attacks and network defences to observe the difference in adversarial learning against deep neural network in CV and NIDS. We offer a reader with a better understanding of DL based NIDS, adversarial attacks and defences. At first, we offer a classification of DL-based NIDS and converse the impact of taxonomy on adversarial attacks. Next, we have to evaluate the white-box and black-box adversarial attack on DNNs finally we defence against adversarial features.   

  1. Machine-Learning-Based UAV-Assisted Agricultural Information Security Architecture and Intrusion Detection


Agricultural information security, convolutional neural network (CNN), geographic position information (GPI), intrusion detection, unmanned aerial vehicles (UAVs)

            We aim to ensure the safe operation of agricultural information systems and to assure the data security of intelligent architecture. First the UAV-aided information acquisition system has studied also a double deep $Q$ -network (DDQN) for location deployment based on geography position information (GPI) to fastly optimize the location of UAV. Also CNN and LSTM were combined as CNN-LSTM method to construct IDS for AIoT for agriculture. Our proposed CNN+LSTM gives the best accuracy.

  1. A dependable hybrid machine learning model for network intrusion detection


XGBoost, Feature Importance, Dependability

            We propose a new hybrid method that integrated the ML and DL methods to improve the detection rate while securing dependability. Our proposed method confirms efficient preprocessing by integrating SMOTE for data balancing and XGBoost for feature selection. We compare our methods to different ML and DL method to identify the best method to implement in pipeline. 

  1. Implementation of Intrusion Detection System Using Various Machine Learning Approaches with Ensemble learning


Cyber-Attacks, Hyper parameters, Meta-Heuristic

            We propose a novel approach to enhance the performance IDS in NSL-KDD dataset by utilizing the multiple meta-heuristic and ML methods. The multiple meta-heuristic methods can be used to optimize the hyper-parameter of ML methods like RF, CART, SVM and MLP. Our paper also notes the important capability of metaheuristic methods to optimize the IDS models and the effectiveness of ML based solutions.

  1. Adversarial Attack of ML-based Intrusion Detection System on In-vehicle System using GAN


GAN, In-vehicle networks

            Our study proposes a Generative Adversarial Network (GAN) based method to create adversarial attacks that accomplished by passing ML-based IDS in vehicle network. We contain preprocessing an automotive hacking dataset to train a GAN-based method and evaluate thee generated attack by utilizing accuracy metrics. In addition the t-SNE visualization reveals the effective new adversarial attacks to fortify the security.  

  1. Intrusion Detection in IoT leveraged by Multi-Access Edge Computing using Machine Learning


Multi-Access Edge Computing, Network security

            We concentrate by utilizing ML and DL methods along with feature selection method to detect cyberattack effectively at the edge of IoT by manipulating multi-access edge computing. We use ANOVA and embedded feature selection methods and use different ML methods like DT, RF, LightGBM, ANN, KNN and XGB on UNSW-NB15 dataset. Our classifier can give the best accuracy and LightGBM is the accurate among all.

  1. A machine-learning-based Intrusion detection for IIoT infrastructure


Industrial Internet of Things, Naive Bayes

            Our paper proposes an intrusion detection system for IIoT based on ML methods. To categorize the incoming traffic as normal or malicious by utilizing the methods like DT, RF and NB methods. Our dataset was preprocessed and the features were retrieved by train and test the suggested IDS. The decision tree method can achieve the best accuracy rate. Our proposed IDS are estimated to improve the security of IIoT system and moderate the hazard of cyberattack.


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1. Novel Ideas

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To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

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