INTRUSION DETECTION SYSTEM USING WSN NOVEL DT, RF, AND MLP ALGORITHMS
DOI:
https://doi.org/10.65009/3gndhq65Keywords:
Wireless Sensor Network, Intrusion Detection, Machine Learning, Deep Learning, Energy Efficiency.,,Abstract
The security of computer networks has emerged as a critical issue in the current digital
era, as information is readily shared, and connection is pervasive. Organisations, governments, and
people are all at danger from the enhancing frequency as well as complexity of cyber assaults. The
need for reliable cybersecurity solutions has never been more pressing as criminal actors attempt
to infiltrate sensitive data and exploit vulnerabilities on a constant basis. Network security leaders
and a key line of defence against cyberattacks are intrusion detection systems (IDS). These
advanced tools are designed to continuously scan for any indications of suspect or unauthorised
behaviour while keeping a close eye on system operations and network traffic in real time. IDS are
important for stopping unauthorized access attempts, service outages, and data breaches by quickly
identifying and reacting to possible threats. The field of intrusion detection systems is observed in
this study along with its underlying theories, methodology, and real-world uses. We'll examine how
IDS may assistorganizations in safeguarding their digital assets and maintaining network integrity
by promptly detecting and addressing various cyberattacks. The introduction will provide a general
overview of the growing cybersecurity problems that organisations and people throughout the globe
are facing.
We investigate the components, sensor location, and data collecting of both Network-based
Intrusion DetectionSystems (NIDS) and Host-based IntrusionDetection Systems (HIDS). NIDS
stands for Network-based IntrusionDetection System. HIDS is for Host-based Intrusion Detection
System. In addition, we went further into the complexities of signature-based, AnomalyBased, and
BehaviorBased detection approaches, as well as the incorporation of machine learning approaches
for improved intrusion detection capabilities. The difficulties associated with implementing an IDS
were investigated; these difficulties included evasion methods used by attackers, concerns about
privacy, challenges with scalability, and resource limits.
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