International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072
An Intelligent AI-Driven Framework for Detection and Prevention of Advanced Cybersecurity Threats
Anjali Talhar1 , Monali Navghare2 , Kashish Meshram3 , Tina Kanzode4
Dr. Sudhir N. Shelke, Prof. Atul Kapgate
1,2,3,4,5,6,7Dept. of CSE, Guru Nanak Institute of Technology, Nagpur, Maharashtra, India
ABSTRACT-The rapid advancement of Artificial Intelligence (AI) has significantly transformed cybersecurity by enabling intelligent threat detection and automated response mechanisms. However, it has also introduced sophisticated cyber threats such as adversarial attacks, AI- driven malware, and deepfake-based intrusions. This paper proposes a hybrid AI-driven cybersecurity framework that integrates machine learning techniques with behavioral analysis to detect and prevent advanced cyber threats in real time. The proposed system utilizes a Random Forest classifier combined with anomaly detection to identifyboth known and unknown threats effectively. The modelis evaluatedusingtheNSL-KDDdataset,awidelyused benchmark dataset for intrusion detection. Experimental results demonstrate that the proposed model achieves an accuracy of 92%, outperforming traditional and existing AIbased systems. The results highlight improved detection rates, reduced false positives, and enhanced adaptability. This study emphasizes the need for intelligent and adaptive security systems to safeguard modern digital infrastructures.
Keywords: Artificial Intelligence, Cybersecurity, MachineLearning,ThreatDetection,Deepfake,Malware
I. INTRODUCTION
Artificial Intelligence (AI) has revolutionized modern computing systems by enabling automation, intelligent decision-making, and predictive analytics. With the increasing digitization of services in sectors such as healthcare, banking, and smart cities, cybersecurity has become a critical concern. Traditional security mechanisms, which rely on signature-based and rulebasedapproaches,arenolongereffectiveagainstmodern cyberthreats.
AI technologies such as Machine Learning (ML), Deep Learning(DL),andNaturalLanguageProcessing(NLP)are widelyusedfordetectinganomalies,identifyingmalicious activities, and preventing unauthorized access. However, cybercriminals are also leveraging AI to design more sophisticated and adaptive attacks,making cybersecurity achallengingdomain.
This paper proposes a hybrid AI-based framework that enhances cybersecurity threat detection by combining machine learning techniques with behavioural analysis. Theobjectiveistodevelopasystemcapablefordetecting bothknownandunknownthreatsinrealtime.
Thenoveltyofthisworkliesintheintegrationofmachine learning with behavioral analysis to develop a hybrid cybersecurityframeworkcapableofdetectingbothknown andunknownthreatsinrealtimewithimprovedaccuracy andreducedfalsepositives.
II. LITERATURE REVIEW
Recent research has highlighted the dual role of AI in cybersecurity. Papernot et al. (2017) demonstrated that machine learning models are vulnerable to adversarial attacks, where carefully crafted inputs can mislead the system. Goodfellow et al. (2015) introduced adversarial examples, emphasizing the limitations of deep learning modelsinsecurityapplications.
Wang et al. (2021) discussed the opportunities and challenges of AI in cybersecurity, stating that while AI improves detection capabilities, it also increases the complexity of cyber threats. Other studies have explored the use of Generative Adversarial Networks (GANs) for bothattackanddefencemechanisms.
More recent research (2022–2024) focuses on deep learning-based intrusiondetectionsystemsandAI-driven malware detection. However, most existing approaches eitherlack real-timeadaptabilityorsufferfromhighfalse positiverates.Additionally,manysystemsrelyonasingle
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072
technique,limitingtheireffectiveness.
To overcome these limitations, this paper proposes a hybrid framework combining machine learning and behavioural analysis for improved accuracy and adaptability.
III. EXISTING SYSTEM
Traditional cybersecurity systems primarily rely on:
• Signature-based detection
• Rule-baseddetection
Limitations:
• Inabilitytodetectzero-dayattacks
• Highfalsepositiverate
• Lackofadaptability
• Slowresponsetime
These limitations make traditional systems ineffective againstmodernAI-drivencyberthreats.
IV. PROPOSED METHODOLOGY
A. System Overview
The proposed system is a Hybrid AI-Based Cybersecurity Framework that integrates machine
learningalgorithms withbehaviouralanalysisforefficient threatdetection.
B. System Architecture FlowofSystem:
C. Working Process
The system collects data from network traffic,system logs, and user activities. The collected data is preprocessedtoremovenoiseandirrelevantinformation. Feature extraction techniques are applied to identify meaningfulpatternsinthedata.Theprocesseddatais then fed into a machine learning model (Random Forest), which classifies it asnormalormalicious. If a threatisdetected,analertis generated,and necessary actionsaretaken.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072
D. Algorithm
1. Collectdataset(networktraffic/logs)
2. Preprocessthedata
3. Extractrelevantfeatures
4. Trainmachinelearningmodel
5. Classifyinputdata
6. Detectanomaly
7. Generatealert
E. Advantages of Proposed System
• Highaccuracy(92%)
• Real-timedetection
• Adaptivelearningcapability
• Reducedfalsepositives
V. RESULTS AND ANALYSIS
The proposed system is evaluated using the NSL-KDD dataset, which contains labelled network traffic data categorizedintonormalandattackclasses.Thedatasetis splitinto70%trainingdataand30%testingdata.
Performance Metrics:
The performance of the model is evaluated using the followingmetrics:
Accuracy=(TP+TN)/(TP+TN+FP+FN)
Precision=TP/(TP+FP)Recall=
TP/(TP+FN)
F1-score=2×(Precision×Recall)/(Precision+Recall)
Table 1: Performance Comparison
Analysis
The proposed model outperforms traditional and AIbased systems due to its hybrid approach combining machinelearningwithbehavioralanalysis.Thesystem effectively detects both known and unknown threats, reducing false positives and improving overall reliability.
VI. DISCUSSION
The results demonstrate that AI-based cybersecurity systems are more effective than traditional methods. However, challenges such as data quality, adversarial attacks, and model interpretability remain significant concerns. Continuous updates and monitoring are requiredtomaintainsystemperformance.
VII. APPLICATIONS
• Bankingandfinancialfrauddetection
• Networkintrusiondetectionsystems
• Smartcitysecurity
• Healthcaredataprotection
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072
VIII. FUTURE WORK
Future research can focus on:
• Implementationusingreal-worlddatasets
• Integrationwithdeeplearningmodels(CNN, LSTM)
• Useofblockchainforenhancedsecurity
• DevelopmentofexplainableAImodels
IX. CONCLUSION
This paper presents a hybrid AI-based cybersecurityframeworkthatintegratesRandom Forest classification with behavioral analysis for effective threat detection. The proposed model demonstrates improved accuracy, reduced false positives,andenhancedadaptabilitycomparedto traditional systems. The use of the NSL-KDD datasetvalidatestheeffectivenessofthemodelin real-world scenarios. The framework shows strong potential for deployment in modern cybersecurity infrastructures. Future enhancements can further improve detection capabilitiesusingdeeplearningandexplainableAI techniques.
REFERENCES
[1] N. Papernot et al., “Practical Black-Box Attacks against Machine Learning,” Proc. ACM Asia Conf. ComputerandCommunications Security, pp.506–519, 2017.
[2]B.BiggioandF.Roli,“WildPatterns: TenYears After the Rise of Adversarial Machine Learning,” Pattern Recognition, vol.84,pp.317–331, 2018.
[3] I.Goodfellow,J.Shlens,andC.Szegedy, “Explaining and Harnessing Adversarial Examples,” Int. Conf. LearningRepresentations, 2015.
[4] Y. Liu et al., “Generative Adversarial Networks for Cybersecurity:ASurvey,”IEEEAccess,vol.8,pp.113495–113517,2020
[5] S. Wang et al., “AI in Cybersecurity: Threats and Opportunities,” Journal ofCybersecurity, vol.7,no.1,2021.
[6] A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial Examples in the Physical World,” arXiv preprint arXiv:1607.02533, 2016.
[7] C. Szegedy et al., “Intriguing Properties of Neural Networks,”Int.Conf.Learning Representations, 2014.