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:
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.
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.
Follow us and stay updated on the latest research works that we have done. We have more than 100+ experts working in our concern to run Intrusion Detection System Using Machine Learning projects effectively. Research proposal topics will be suggested by referring the latest journals based on your interest.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.
We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other
To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)
After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.
Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.
Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)
We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.
We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.
Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.
We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.
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For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.
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Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.
We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources
We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on
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We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.
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We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.
Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
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