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1. Base paper Title: Comparative Analysis of Intrusion Detection Systems and Machine
Learning-Based Model Analysis Through Decision Tree
Modified Title: Comparative Study of Machine Learning-Based Model Analysis Using
Decision Trees and Intrusion Detection Systems
Abstract
Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a
comprehensive review of intrusion detection techniques, and commonly used datasets for
evaluation. It discusses evasion techniques employed by attackers and the challenges in
combating them to enhance network security. Researchers strive to improve IDS by accurately
detecting intruders, reducing false positives, and identifying new threats. Machine learning
(ML) and deep learning (DL) techniques are adopted in IDS systems, showing potential in
efficiently detecting intruders across networks. The paper explores the latest trends and
advancements in ML and DL-based network intrusion detection systems (NIDS), including
methodology, evaluation metrics, and dataset selection. It emphasizes research obstacles and
proposes a future research model to address weaknesses in the methodologies. The decision
tree, known for its speed and user friendliness, is proposed as a model for detecting result
anomalies, combining findings from a comparative survey. This research aims to provide
insights into building an effective decision tree-based detection framework.
Existing System
Designing intrusion detection systems (IDSs) is greatly challenged by the development
of malicious software, commonly called malware. The biggest problem in detecting unknown
and disguised malware is that the attackers use various methods to avoid the detection of their
activities by the IDS. Consequently, the complexity level of malicious attacks has increased.
Zero-day attacks have greatly affected countries such as Australia and the US [96]. 21st century
is seeing more zeroday attacks each year [249], which was higher in volume and intensity than
previous years, according to the 2017 Symantec Internet Security Threat Report [225]. The
number of data records lost or stolen by hackers has risen to over fourteen billion since 2013
[239], according to the Data Breach Statistics of 2023. Previously, fraudsters targeted bank
customers to steal credit cards or bank accounts. But now, the latest malware attacks banks

2. directly, attempting to steal sensitive information in one attack. Thus, the detection of zero-day
attacks has gained the utmost importance. The Australian Cyber Security Centre analyzed the
complexity of attackers’ methods in 2017 [19]. As a result, developing effective intrusion
detection systems (IDSs) has become essential to detect new and advanced forms of malware.
An IDS aims to identify various malware types replacing the traditional firewall quickly.
Researchers have implemented several ML- and DL-based techniques over the past decade to
improve NIDS’s ability to identify malicious activities. The tremendous growth in network
traffic and ensuing security risks in parallel create difficulties for NIDS systems to identify
hostile intrusions effectively. The key idea is to provide up-to-date information on recent ML-
and DL-based NIDS to provide a baseline for new researchers exploring this important domain.
Several methodologies employed in signature-based and anomaly-based procedures, such as
SIDS and AIDS, are explained. The paper focused on the challenges associated with various
anomaly-based intrusion detection methods and their evaluation processes. It then provides
recommendations for the most suitable methods based on the type of intrusion. The discussion
highlights how these issues are relevant to the network intrusion detection system research
community and how they compare to prior surveys in the field [140], [185]. There is a
requirement for a more recent analysis, as earlier surveys on intrusion detection have not
thoroughly reviewed dataset issues, evasion techniques, and various attack forms. This work
provides a revised classification of the field of intrusion detection, which enhances previous
classifications [26], [140].
Drawback in Existing System
 Limited Expressiveness:
Issue: Decision trees may not be expressive enough to capture complex relationships
in the data. They tend to create simple, axis-aligned decision boundaries.
Impact on IDS: In the context of intrusion detection, where the patterns of attacks can
be intricate and evolving, decision trees may struggle to represent these complex
relationships accurately.
 Instability:
Issue: Decision trees are sensitive to variations in the training data, and small changes
can result in different tree structures.

3. Impact on IDS: The instability of decision trees can make them less robust in the
context of intrusion detection, where a consistent and reliable model is crucial for
accurate identification of malicious activities.
 Difficulty Handling Continuous Variables:
Issue: Decision trees naturally work with discrete features and may not handle
continuous variables well without additional preprocessing.
Impact on IDS: In intrusion detection, features such as network traffic flow rates or
packet sizes are often continuous. Failure to handle these variables appropriately may
lead to loss of information and reduced detection accuracy.
 Limited Interpretable Depth:
Issue: Decision trees with sufficient depth to capture complex relationships may
become challenging to interpret.
Impact on IDS: Interpretability is crucial in the context of security applications. If
security professionals cannot understand and trust the decisions made by the model, it
may hinder the adoption of the IDS in real-world scenarios.
Proposed System
 Dataset Selection:
Choose relevant and representative datasets for training, validation, and testing.
Consider using benchmark datasets for intrusion detection to ensure comparability with
other studies. Include a mix of normal and anomalous network traffic data.
 Model Selection:
Implement decision tree-based models for intrusion detection. Consider variations
like traditional decision trees, Random Forests, or Gradient Boosted Trees. Optionally,
compare decision tree models with other machine learning algorithms commonly used
in intrusion detection.

4.  Performance Evaluation:
Assess the performance of the proposed IDS using decision tree models based on
various metrics, including:
Accuracy, Precision, Recall, F1 Score
Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC)
Confusion Matrix
False Positive Rate and False Negative Rate
 Future Work and Recommendations:
Suggest areas for improvement and future research based on the findings. Provide
recommendations for enhancing the proposed IDS, including potential modifications to
decision tree models or exploration of alternative algorithms.
Algorithm
 Interpretability:
Decision trees are inherently interpretable, as the rules for classification are
represented in a tree structure. Assess the ease of interpretation for security analysts
and stakeholders. This is particularly important in cyber security, where understanding
the reasoning behind a decision is crucial for trust and action ability.
 Robustness to Adversarial Attacks:
Assess the robustness of decision tree-based models against adversarial attacks.
Intrusion detection systems are vulnerable to attacks aimed at deceiving the model.
Evaluate whether decision trees are resilient or if additional measures such as ensemble
methods or adversarial training are needed to enhance robustness.
 Feature Importance and Selection:
Explore the ability of decision trees to automatically rank features based on their
importance in intrusion detection. Consider whether the model's built-in feature
selection capabilities align with the domain expertise or if additional feature
engineering is required.

5. Advantages
 Interpretability:
Advantage: Decision trees are inherently interpretable. The rules for classification are
represented in a tree structure, making it easy for security analysts and stakeholders to
understand how the model makes decisions. This transparency is crucial in cyber
security for explaining and trusting the decision-making process.
 Automatic Feature Selection:
Advantage: Decision trees perform automatic feature selection by choosing the most
discriminative features at each node. This can be advantageous in intrusion detection,
where the identification of relevant features is crucial for accurate detection of
anomalies or attacks.
 Visualization:
Advantage: Decision trees can be visualized graphically, providing an intuitive
representation of the decision-making process. Visualization tools can aid in the
analysis of the model's behavior, making it easier to identify patterns and potential areas
of improvement.
 Robustness to Irrelevant Features:
Advantage: Decision trees tend to be robust to irrelevant features in the dataset. The
algorithm can ignore irrelevant attributes during the tree-building process, which can
be beneficial in intrusion detection scenarios where not all features may be equally
informative.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB

6. Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm