EST. 2026

The Archive

Computer Science · REF. TA-18050

Determination of Classification Accuracy in Intrusion Detection Systems Using Feature Selection Techniques in Selected Benchmark Data Sets

Abstract

This study investigates the subject matter outlined in the title above through a structured research design appropriate to its academic level. Using primary and/or secondary data collection methods, the research examines the underlying variables, tests relevant hypotheses, and presents findings with implications for practice and policy. This is placeholder abstract text generated for catalogue preview purposes; the full document contains a complete, topic-specific abstract, literature review, methodology, data analysis, and conclusion.

Chapter One — 1.1 Background to the Study

Research interest in feature selection techniques has grown steadily in recent years, driven by its demonstrated relevance to intrusion detection systems in both laboratory and field settings.

Much of the existing literature on feature selection techniques draws on data and conditions that differ from the local context in which intrusion detection systems is typically studied or produced, limiting the direct applicability of prior findings to classification accuracy.

1.2 Statement of the Problem

There is currently limited empirical evidence on how feature selection techniques affects classification accuracy in intrusion detection systems, making it difficult for researchers and practitioners to draw reliable, context-appropriate conclusions. This study addresses that gap through a structured investigation.

1.3 Objectives of the Study

  1. To determine the effect of feature selection techniques on classification accuracy of intrusion detection systems.
  2. To evaluate the extent to which feature selection techniques influences classification accuracy.
  3. To identify the conditions under which feature selection techniques has the greatest effect on classification accuracy.
  4. To recommend practices based on the observed relationship between feature selection techniques and classification accuracy.

1.4 Research Questions

  1. What is the effect of feature selection techniques on classification accuracy of intrusion detection systems?
  2. To what extent does feature selection techniques influence classification accuracy?
  3. Under what conditions does feature selection techniques have the greatest effect on classification accuracy?
  4. What practices can be recommended based on this relationship?

1.5 Significance of the Study

This study is significant to researchers and practitioners working with intrusion detection systems, offering evidence on how feature selection techniques relates to classification accuracy. It also contributes to the broader literature in computer science by documenting findings specific to the conditions under which the study was conducted.

1.6 Scope of the Study

The study is limited to examining Feature Selection Techniques and its relationship with classification accuracy in intrusion detection systems, reflecting a clearly defined scope of analysis; conclusions are drawn strictly from the conditions and samples used in the study.

Chapters Two through Five, references and appendices are available for a one-time fee of ₦75,000.

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