This document discusses the development of a network intrusion detection system (NIDS) utilizing machine learning and deep learning to enhance detection capabilities of network traffic for suspicious activities. It explores various machine learning models, such as logistic regression and decision trees, as well as deep learning approaches like convolutional and recurrent neural networks, highlighting their accuracies and effectiveness in intrusion detection. The project aims to advance NIDS integration into a comprehensive website security platform with features like DDoS mitigation and a web application firewall.

1. Network Intrusion Detection System (NIDS)
using Machine Learning and Deep Learning
Guide: Your guide's name, their qualification and the
institution where they work at
BY: YOUR NAME

2. ABSTRACT
• NETWORK INTRUSION DETECTION SYSTEM (NIDS) USING MACHINE LEARNING AND DEEP LEARNING IS
PROPOSED TO ENHANCE DETECTION CAPABILITIES BY CONTINUOUSLY MONITORING AND ANALYZING
NETWORK TRAFFIC BY LEVERAGING ML AND DL ALGORITHMS TO IMPROVE THE ACCURACY AND
EFFICIENCY OF INTRUSION DETECTION.
• THIS ROBUST NIDS CAN DETECT AND CLASSIFY VARIOUS TYPES OF NETWORK INTRUSIONS AND CAN
DETECT AND ALERT NETWORK INTRUSIONS IN REAL TIME, ENABLING PROMPT RESPONSE AND
MITIGATION.
• THE PROCESS IS AUTOMATED, REDUCING THE NEED FOR MANUAL MONITORING AND ANALYSIS.

3. • A NETWORK INTRUSION DETECTION SYSTEM (NIDS) IS A SECURITY TECHNOLOGY THAT MONITORS
NETWORK TRAFFIC FOR SUSPICIOUS ACTIVITIES OR UNAUTHORIZED ACCESS ATTEMPTS.
• NIDS PLAYS A CRUCIAL ROLE IN DETECTING AND PREVENTING NETWORK-BASED ATTACKS, SUCH AS
MALWARE INFECTIONS, DATA BREACHES, AND UNAUTHORIZED ACCESS ATTEMPTS.
• BY ANALYZING NETWORK TRAFFIC, NIDS CAN IDENTIFY PATTERNS AND ANOMALIES THAT INDICATE
POTENTIAL SECURITY THREATS.
INTRODUCTION

4. • FERRAG, MOHAMED AMINE, ET AL., "DEEP LEARNING FOR CYBER SECURITY INTRUSION DETECTION: STATE
OF THE ART, TAXONOMIES, AND OPEN RESEARCH ISSUES", JOURNAL OF INFORMATION SECURITY AND
APPLICATIONS, VOL. 58, 2021
• ETHEM ALPAYDIN, "INTRODUCTION TO MACHINE LEARNING", 4TH EDITION, MIT PRESS, 2020.
• SHONE N., NGOC T. N., PHAI V. D., SHI Q., "A DEEP LEARNING APPROACH TO NETWORK INTRUSION
DETECTION", IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, VOL. 2, ISSUE
1, PG. 41-50, 2018
• VINAYAKUMAR R., SOMAN K.P., POORNACHANDRAN P., "APPLYING CONVOLUTIONAL NEURAL NETWORK
FOR NETWORK INTRUSION DETECTION", INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING,
COMMUNICATIONS AND INFORMATICS (ICACCI), PG. 1222-1228, 2017
REVIEW OF LITERATURE

5. METHODOLOGY
Intrusion Detection
Model Evaluation
Model Training
Feature Selection
Data Collection

6. EXPERIMENTATION - LIBRARIES AND DATASET USED
LIBRARIES:
• NUMPY (NP)
• PANDAS (PD)
• MATPLOTLIB.PYPLOT (PLT)
• SEABORN (SNS)
• TENSORFLOW (TF)
• XGBOOST (XGB)
• SCIKIT-LEARN (LIBRARIES FROM SKLEARN: TREE, NAIVE_BAYES, LINEAR_MODEL, NEIGHBORS,
DECOMPOSITION, SVM, METRICS, ENSEMBLE, PREPROCESSING, MODEL_SELECTION)
• WARNINGS
DATASET:
• THE NSL-KDD DATASET FROM KAGGLE
• HTTPS://WWW.KAGGLE.COM/DATASETS/HASSAN06/NSLKDD.

7. EXPERIMENTATION - ML MODELS
USED
LOGISTIC REGRESSION:
• LOGISTIC REGRESSION IS A POPULAR ML ALGORITHM WHICH CAN BE USED IN NIDS FOR BINARY
CLASSIFICATION TASKS, SUCH AS DETECTING MALICIOUS TRAFFIC OR IDENTIFYING LEGITIMATE TRAFFIC.
• TRAINING ACCURACY LOGISTIC REGRESSION 87.97443861198488 TEST ACCURACY LOGISTIC REGRESSION 87.62452867632466
K-NEAREST NEIGHBORS (KNN):
• KNN IS A NON-PARAMETRIC ML ALGORITHM THAT CAN BE USED FOR BOTH CLASSIFICATION AND
REGRESSION TASKS. IT WORKS BY FINDING THE K NEAREST NEIGHBORS TO A GIVEN DATA POINT AND
USING THEIR LABELS TO MAKE A PREDICTION.
• TRAINING ACCURACY KNEIGHBORSCLASSIFIER 99.05236313841452 TEST ACCURACY KNEIGHBORSCLASSIFIER 98.93629688430245
NAIVE BAYES:
• NAIVE BAYES IS A PROBABILISTIC ALGORITHM THAT CAN BE USED FOR BOTH CLASSIFICATION AND
REGRESSION TASKS. IT WORKS BY CALCULATING THE PROBABILITY OF A GIVEN DATA POINT BELONGING
TO A PARTICULAR CLASS BASED ON THE PROBABILITIES OF ITS FEATURES.
• TRAINING ACCURACY GAUSSIANNB 91.80269307480874 TEST ACCURACY GAUSSIANNB 91.60547727723754
SUPPORT VECTOR MACHINES (SVM):
• SVM IS A POWERFUL ALGORITHM THAT CAN BE USED FOR BOTH CLASSIFICATION AND REGRESSION
TASKS. IT WORKS BY FINDING THE HYPERPLANE THAT BEST SEPARATES THE DATA INTO DIFFERENT
CLASSES.
• TRAINING ACCURACY LINEAR SVC(LBASEDIMPL) 97.40416960219098 TEST ACCURACY LINEAR SVC(LBASEDIMPL) 97.3169279618972

8. EXPERIMENTATION - ML MODELS
USED
DECISION TREE CLASSIFIER (DTC):
• DTC IS A TREE-BASED ML ALGORITHM THAT CAN BE USED FOR BOTH CLASSIFICATION AND REGRESSION
TASKS. IT WORKS BY RECURSIVELY SPLITTING THE DATA INTO SMALLER SUBSETS BASED ON THE MOST
IMPORTANT FEATURES.
• TRAINING ACCURACY DECISIONTREECLASSIFIER 99.99404626055548 TEST ACCURACY
DECISIONTREECLASSIFIER 99.86505258979956
XGB REGRESSOR:
• XGB Regressor is a tree-based ml algorithm that can be used for both classification and regression tasks. It
works by recursively splitting the data into smaller subsets based on the most important features and using a
gradient boosting algorithm to improve the accuracy.
PRINCIPAL COMPONENT ANALYSIS (PCA):
• PCA IS A DIMENSIONALITY REDUCTION ALGORITHM THAT CAN BE USED TO REDUCE THE NUMBER OF
FEATURES IN A DATASET WHILE PRESERVING THE MOST IMPORTANT INFORMATION. IT WORKS BY
FINDING THE DIRECTIONS OF MAXIMUM VARIANCE IN THE DATA AND PROJECTING THE DATA ONTO
THOSE DIRECTIONS.

9. EXPERIMENTATION - DL MODELS
USED
CONVOLUTIONAL NEURAL NETWORKS (CNNS):
• CNNS ARE COMMONLY USED IN NIDS FOR THEIR ABILITY TO PROCESS LARGE AMOUNTS OF DATA
EFFICIENTLY.
• THEY ARE PARTICULARLY EFFECTIVE IN ANALYZING NETWORK TRAFFIC DATA AND IDENTIFYING
PATTERNS AND ANOMALIES.
• CNNS CONSIST OF MULTIPLE LAYERS OF CONVOLUTIONAL AND POOLING OPERATIONS, FOLLOWED BY
FULLY CONNECTED LAYERS FOR CLASSIFICATION.
• VAL_ACCURACY: 0.9771
RECURRENT NEURAL NETWORKS (RNNS):
• RNNS ARE WELL-SUITED FOR NIDS AS THEY CAN CAPTURE TEMPORAL DEPENDENCIES IN NETWORK
TRAFFIC.
• THEY ARE CAPABLE OF PROCESSING SEQUENTIAL DATA AND ARE OFTEN USED FOR TASKS LIKE
INTRUSION DETECTION AND ANOMALY DETECTION.
• RNNS UTILIZE RECURRENT CONNECTIONS TO MAINTAIN MEMORY OF PAST INPUTS, ALLOWING THEM TO
MAKE PREDICTIONS BASED ON PREVIOUS DATA.

10. EXPERIMENTATION - ENSEMBLE TECHNIQUES
USED
BOOSTING
• COMBINES MULTIPLE WEAK CLASSIFIERS TO CREATE A STRONG CLASSIFIER.
• EACH WEAK CLASSIFIER FOCUSES ON DIFFERENT ASPECTS OF NETWORK TRAFFIC TO IMPROVE
ACCURACY.
• EXAMPLES: ADABOOST, GRADIENT BOOSTING
BAGGING:
• CREATES AN ENSEMBLE OF CLASSIFIERS BY TRAINING EACH CLASSIFIER ON A DIFFERENT SUBSET OF THE
TRAINING DATA.
• EACH CLASSIFIER INDEPENDENTLY PREDICTS THE CLASS LABEL, AND THE FINAL PREDICTION IS MADE BY
MAJORITY VOTING OR AVERAGING.
• EXAMPLES: RANDOM FOREST, EXTRA TREES
• TRAINING ACCURACY RANDOMFORESTCLASSIFIER 99.99404626055548 TEST ACCURACY RANDOMFORESTCLASSIFIER 99.8809287557055
STACKING:
• COMBINES MULTIPLE CLASSIFIERS BY TRAINING A META-CLASSIFIER ON THE PREDICTIONS OF THE BASE
CLASSIFIERS.
• EXAMPLES: STACKED GENERALIZATION, SUPER LEARNER

11. RESULTS

12. THIS PROJECT DEMONSTRATES THE FEASIBILITY OF DEVELOPING A ROBUST NETWORK INTRUSION
DETECTION SYSTEM (NIDS) USING MACHINE LEARNING TECHNIQUES. THE PROMISING RESULTS ACROSS
VARIOUS MODELS HIGHLIGHT THE POTENTIAL OF SUCH A SYSTEM TO SAFEGUARD WEBSITES AGAINST
MALICIOUS TRAFFIC.
FUTURE WORK:
• CLOUDFLARE COMPETITOR DEVELOPMENT: THE NEXT SIGNIFICANT STEP IS TO INTEGRATE THIS NIDS INTO
A COMPREHENSIVE WEBSITE SECURITY PLATFORM. THIS PLATFORM WILL OFFER END-TO-END
PROTECTION MIRRORING, AND ULTIMATELY SURPASSING, THE CAPABILITIES OF SERVICES LIKE
CLOUDFLARE. KEY FEATURES SHOULD INCLUDE:
⚬ DDOS MITIGATION: DEVELOP ROBUST MECHANISMS TO DETECT AND MITIGATE LARGE-SCALE
DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS.
⚬ WEB APPLICATION FIREWALL (WAF): IMPLEMENT A WAF CAPABLE OF FILTERING COMMON WEB
ATTACKS SUCH AS SQL INJECTION AND CROSS-SITE SCRIPTING (XSS).
⚬ CONTENT DELIVERY NETWORK (CDN): BUILD A GEOGRAPHICALLY DISTRIBUTED CDN
INFRASTRUCTURE TO ACCELERATE WEBSITE LOADING TIMES AND ENHANCE RESILIENCE.
• DEPLOYMENT AND SCALABILITY: FOCUS ON STRATEGIES FOR SEAMLESS DEPLOYMENT OF THE NIDS
DIRECTLY ON WEBSITES. THIS WILL REQUIRE:
⚬ LIGHTWEIGHT DESIGN: OPTIMIZE THE MODEL FOR MINIMAL RESOURCE CONSUMPTION TO ENSURE IT
DOESN'T HINDER WEBSITE PERFORMANCE.
⚬ CONTAINERIZATION: EXPLORE CONTAINERIZATION TECHNOLOGIES (LIKE DOCKER) TO PACKAGE THE
NIDS AND ITS DEPENDENCIES FOR EASY DEPLOYMENT ACROSS VARIOUS ENVIRONMENTS.
⚬ CLOUD INTEGRATION: DESIGN THE PLATFORM WITH CLOUD-NATIVE TECHNOLOGIES IN MIND TO
ENABLE SCALABILITY AND HIGH AVAILABILITY.
CONCLUSION AND FUTURE WORK

13. ANY QUESTIONS?