Toward Constructing a Balanced Intrusion Detection Dataset

Alsameraee, Amer Abulmajeed Abdulrahman; Ibrahem, Mahmood Khalel

doi:10.54153/sjpas.2020.v2i3.86

Cited by 10 publications

(7 citation statements)

References 8 publications

(11 reference statements)

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“…Creating a self-adaptive parameter control mechanism is an additional option. The precision of the intervals would be higher if the levels of a parameter were split into numerous small intervals but choosing between the intervals would require substantially more calculation [179]. To fully comprehend how minor variations in the values of continuous parameters affect MHs performance, more research is necessary.…”

Section: Discussionmentioning

confidence: 99%

Learnheuristics in routing and scheduling problems: A review

Hussein¹,

Yaseen²,

Rashid³

2023

sjpas

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Combinatorial optimization problems (COPs) are the most important class of optimization problems, with great practical significance. This class is concerned with identifying the best solution from a discrete set of all available options. The transportation (routing) and distribution (scheduling) systems are considered the most challenging optimization examples of the COPs. Given the importance of routing and scheduling problems, many methods have been proposed to address them. These methods can be categorized into traditional (exact and metaheuristics (MHs) methods) and machine learning (ML) methods. ML methods have been proposed to overcome the problems that traditional methods suffer from, especially high computational time and dependence on the knowledge of experts. Recently, ML methods and MHs have been combined to tackle the COPs, and then the learnheuristics term emerged. This combination aims to guide the MHs toward an efficient, effective, and robust search and improve their performance in terms of solution quality. This work reviews the publications in which the collaboration between MHs and ML has been utilized to propose a guideline for the researchers to put forward new algorithms that have a good ability to solve routing and scheduling problems.

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Section: Discussionmentioning

confidence: 99%

Learnheuristics in routing and scheduling problems: A review

Hussein¹,

Yaseen²,

Rashid³

2023

sjpas

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“…The study utilized the CICIDS2017 dataset, which includes benign data and various attack types, such as DoS, DDoS, brute force SSH, brute force FTP, heartbleed, infiltration, and botnet, making it one of the most current datasets available. CICFlowMeter was used to analyze network traffic results [20].…”

Section: Related Studiesmentioning

confidence: 99%

An Improved Intrusion Detection in Wireless Sensor Networks Using Hybrid Multiclass Over-Sampling and Deep Neural Networks

Aliyu. O,

Hashim. I,

Bnjamin

et al. 2023

IJASRE

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With the emergence of new attacks, there is a continual need for innovative approaches that can closely monitor and swiftly adapt to evolving threats. IDSs can be broadly categorized into misuse detection and anomaly detection, each utilizing machine-learning methods. Machine learning algorithms, particularly those relying on datasets like DARPA and KDD Cup 1999,have gained popularity. However, challenges include dataset limitations, overfitting, and the requirement for substantial computational power. This study focuses on the specific problem of class imbalance in Wireless Sensor Networks (WSN) datasets for intrusion detection. Existing techniques, such as oversampling and undersampling, have limitations, and the imbalance poses challenges for accurate intrusion detection model development. The research aims to develop an enhanced intrusion detection model addressing multiclass imbalance through an optimized KNN-SMOTE oversampling technique integrated with a Deep Learning model. The significance lies in its potential to greatly enhance the accuracy of intrusion detection systems, contributing to the security ofcomputer networks. The study's scope involves static dataset analysis and multiclass classification of intrusion attacks. The research questions revolve around the development of a multiclass oversampling technique, a hybrid model, and the performance evaluation of the developed system in comparison to existing IDS.The research proposes a novel OK-SMOTE-DL model combining the potential of the KNN model, firefly algorithm, and the SMOTE model fused with Deep Learning techniques to help create a better model for handling multiclass intrusion datasets that can similarly detect minority classes effectively in wireless sensornetworks

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“…The authors conducted a study to create a balanced dataset. 14 This study proposed BMCD for solving class imbalance problems for large-scale multi-class datasets, where a multi-class dataset for experimental purposes was created from the CICIDS2017 dataset. An experimental setup was implemented to investigate the impact of BMCD on the model's performance, where they measured the model performance with and without BMCD.…”

Section: Introductionmentioning

confidence: 99%

“…The authors conducted a study to create a balanced dataset 14 . This study proposed BMCD for solving class imbalance problems for large‐scale multi‐class datasets, where a multi‐class dataset for experimental purposes was created from the CICIDS2017 dataset.…”

Section: Introductionmentioning

confidence: 99%

AWC‐NIDS: Attack‐wise customized network intrusion detection system using machine learning, concurrency, and distributed systems

Guney¹

2023

Concurrency and Computation

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SummaryWith digitization and modern network applications, information security has gained a tremendous importance. Therefore, accurate and efficient detection systems are crucial for maintaining proactive security in computer networks. Machine learning (ML) has shown great potential as a promising solution since it can teach a machine to distinguish malicious and normal network activities. However, recently proposed methods are suffering from at least one of the following: detection accuracy, false alarm rate, and computational complexity issues. The main reason behind this problem is the complexity of the model in terms of attack types. From the ML perspective, intrusion detection is a classification problem where each attack type is identified by a set of different features, and features are used for classifying network activities. Thus, training an ML algorithm to detect more than one attack type leads to a more complex model; the increasing number of used features contributes positively to the model complexity, and may result in relatively lower detection accuracy or a higher false positive rate. To tackle this problem, this study proposes an attack‐wise customized network intrusion detection system (AWC‐NIDS) based on ML, concurrency, and distributed systems to achieve accurate and efficient network‐wide intrusion detection. Since CICIDS2017 contains many modern attacks, it was used for model development and performance evaluation. The experimental results showed that the proposed methodology achieved high classification performance for all datasets with a small number of features. However, it was observed that the lowest accuracy was achieved for the comprehensive dataset (which contains all attack types); for the single attack‐type datasets, the obtained accuracy was above 99%. This finding proves the concept of attack‐wise customization for intrusion detection and shows the significance of the proposed methodology. In conclusion, this framework is promising for implementing robust and accurate cybersecurity systems for traditional and modern networking.

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Toward Constructing a Balanced Intrusion Detection Dataset

Cited by 10 publications

References 8 publications

Learnheuristics in routing and scheduling problems: A review

Learnheuristics in routing and scheduling problems: A review

An Improved Intrusion Detection in Wireless Sensor Networks Using Hybrid Multiclass Over-Sampling and Deep Neural Networks

AWC‐NIDS: Attack‐wise customized network intrusion detection system using machine learning, concurrency, and distributed systems

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