Authors :
Dr. Pankaj Malik; Parag Jhala; Vedanshi Sharma; Vaishnavi Parsai; Kirti Pandya
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/y2z63w8d
Scribd :
https://tinyurl.com/53c4ez32
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR902
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
With the escalating sophistication of cyber
threats, the need for robust intrusion detection systems
has become paramount in safeguarding information
systems. This research addresses the limitations of
traditional methods by proposing and evaluating
innovative machine learning algorithms for classification
in intrusion detection. The study explores a diverse set of
algorithms designed to enhance accuracy, efficiency, and
adaptability in the dynamic landscape of cybersecurity.
The introduction provides a context for the research,
emphasizing the critical role of intrusion detection in
contemporary cybersecurity. A comprehensive literature
review underscores the shortcomings of existing
methodologies and sets the stage for the introduction of
novel machine learning approaches. The research
methodology outlines the dataset, evaluation metrics, and
the training/testing process, ensuring transparency and
replicability.
The heart of the paper lies in the exploration of
innovative machine learning algorithms. Each algorithm
is introduced, highlighting unique features and
innovations. The experimental results showcase the
performance of these algorithms, with detailed
comparisons against traditional counterparts. The
discussion section interprets the results, emphasizing the
practical implications and potential advancements these
algorithms bring to the field.
Addressing challenges encountered during
implementation, the paper outlines future directions for
research, providing a roadmap for continued innovation.
The conclusion succinctly summarizes key findings,
accentuating the groundbreaking contributions of the
proposed machine learning algorithms to intrusion
detection. This research significantly advances the
discourse on intrusion detection systems, offering a
paradigm shift towards more effective and adaptive
solutions in the face of evolving cyber threats.
With the escalating sophistication of cyber
threats, the need for robust intrusion detection systems
has become paramount in safeguarding information
systems. This research addresses the limitations of
traditional methods by proposing and evaluating
innovative machine learning algorithms for classification
in intrusion detection. The study explores a diverse set of
algorithms designed to enhance accuracy, efficiency, and
adaptability in the dynamic landscape of cybersecurity.
The introduction provides a context for the research,
emphasizing the critical role of intrusion detection in
contemporary cybersecurity. A comprehensive literature
review underscores the shortcomings of existing
methodologies and sets the stage for the introduction of
novel machine learning approaches. The research
methodology outlines the dataset, evaluation metrics, and
the training/testing process, ensuring transparency and
replicability.
The heart of the paper lies in the exploration of
innovative machine learning algorithms. Each algorithm
is introduced, highlighting unique features and
innovations. The experimental results showcase the
performance of these algorithms, with detailed
comparisons against traditional counterparts. The
discussion section interprets the results, emphasizing the
practical implications and potential advancements these
algorithms bring to the field.
Addressing challenges encountered during
implementation, the paper outlines future directions for
research, providing a roadmap for continued innovation.
The conclusion succinctly summarizes key findings,
accentuating the groundbreaking contributions of the
proposed machine learning algorithms to intrusion
detection. This research significantly advances the
discourse on intrusion detection systems, offering a
paradigm shift towards more effective and adaptive
solutions in the face of evolving cyber threats.
