Metaheuristic Algorithms-based Feature Selection Approach for Intrusion Detection

Almomani, Ammar; Alweshah, Mohammed; Khalayleh, Saleh Al; Al-Refai, Mohammed; Qashi, Riyadh

doi:10.1201/9780429504044-8

Cited by 16 publications

(20 citation statements)

References 1 publication

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“…Some of the popular algorithms in this category are: Particle Swarm Optimization, Ant Colony Optimization, Honey Bee Swarm Optimization, Monkey Optimization, Cuckoo Search, Bat Algorithm, Bees Algorithm, Firefly Algorithm, Social Spider Algorithm, and so forth. 11 Physics-based algorithms operate according to the principles and rules of physics observed in the universe. Some of the well-known techniques in this category are: Simulated Annealing, Multi-Verse Optimizer, Gravitational Search, Tabu Search, Galaxy Based Search, Nuclear Reaction Optimization, Equilibrium Optimizer, Wind-Driven Optimization, etc.…”

Section: Advantages Of Metaheuristic Algorithmsmentioning

confidence: 99%

“…A number of academics have contended that ML approaches are more adept at managing novel botnet variations than traditional techniques. 11 They are more able to identify new botnet threats because of their capacity to learn from patterns and extrapolate from available data. Additionally, ML can effectively be utilized for encrypted botnet identification by evaluating flow-based data without the need for deep packet inspection.…”

Section: Introductionmentioning

confidence: 99%

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Reviewing various feature selection techniques in machine learning‐based botnet detection

Baruah,

Borah,

Deka

2024

Concurrency and Computation

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SummaryMachine learning approaches are widely used for the detection and classification of emerging botnet variations due to their ability to yield more precise results compared to traditional methods. The relevancy of the features plays a major role in these detection algorithms' effectiveness. As such, the most distinctive characteristics must be extracted from a high‐dimensional dataset that is used to classify botnets. Nevertheless, we discovered that the majority of earlier studies lacked proper analysis and paid little attention to the various feature selection techniques. The main goal of this work is to investigate and assess the advantages and disadvantages of the different feature selection techniques used for botnet detection. Studies show that feature selection is a very efficient way to decrease the amount of storage and processing power required while simultaneously increasing classification accuracy. As a consequence, its application in many other fields has grown. The field of feature selection is recognized for its non‐deterministic polynomial‐time hardness; to mitigate this hardness, metaheuristic techniques have been applied. Metaheuristic algorithms are exceptionally good at performing a global search. In order to choose feature subsets optimally in the field of botnet detection, we additionally prioritize the use of metaheuristic methods. This study offers a more thorough insight of the feature selection strategies that are primarily employed by machine learning‐based botnet detection models. It also offers insights into how better feature selection approaches might be applied to strengthen botnet detection mechanisms. Additionally, it will help in understanding the limitations of existing approaches and identifying areas for improvement.

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Section: Advantages Of Metaheuristic Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reviewing various feature selection techniques in machine learning‐based botnet detection

Baruah,

Borah,

Deka

2024

Concurrency and Computation

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“…Feature selection has been used in a wide range of problems, including image processing, sentiment analysis, intrusion detection, and language identification as well as many other domains [61][62][63][64][65][66][67]. However, one of the challenges that still needs be overcome in respect of the use of FS process is its use in the field of medical diagnosis, which is the focus of this research.…”

Section: Related Workmentioning

confidence: 99%

Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis

Alweshah

Alkhalaileh

Al-Betar

et al. 2022

Knowledge-Based Systems

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“…Therefore, selecting a smaller subset of predictors through nding the most-related predictors is crucial because it reduces the amount of time required for data collection and computing while also avoiding over tting in prediction models [13]. Feature selection methods were developed to lter out obsolete and redundant predictors in order to obtain the smallest and most successful subset of predictors, which decreases computation time in addition to increasing prediction accuracy [14] Despite some developments, the current FS methods for large feature sets are still insu cient [15]. They are technically feasible, but far from optimal, or they are optimum or nearly optimum but can not handle the computational complexity of FS problems of realistic, or real-world size [4].…”

Section: Introductionmentioning

confidence: 99%

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Alweshah

Jebril

Alkhalaileh

et al. 2022

Preprint

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Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which causes a number of problems that adversely affect diagnostic predictive accuracy. Work on FS has grown extensively many fields due to increased demand for methods that can reduce the dimensionality of data by choosing the best subset of features according to specific criteria in order to maximize prediction accuracy and minimize irrelevant features. In recent times, metaheuristics have been preferred over conventional optimization methods for solving FS problems in order to try to obtain an almost optimal solution in a finite time. Metaheuristics are general-purpose algorithms that can be used to solve almost any optimization problem because they generate “appropriate” solutions in a reasonable amount of time, which is especially useful when seeking to solve complicated problems. Many popular implementations have shown the utility of metaheuristics in different ways by contrasting their performance on well-known problems with that of other algorithms or applications .There are many metaheuristic algorithms in the literature such as those based on swarm intelligence, including particle swarm optimizations and ant colony optimization. The major objective of this research is to provide an increased degree of accuracy to resolve FS problems by conducting different experiments using a metaheuristic algorithm, namely the heap-based optimizer algorithm (HBO). The HBO is used with a k-nearest neighbor classifier in a wrapper to improve the FS process. The performance of the proposed method is evaluated and compared against seven approaches in the literature that are applied on nine high-dimensional data sets that contain, a low number of samples and multiple classes. The findings reveal that the HBO decreases the number of features for classification tasks, and is able to achieve high accuracy in two data sets as compared to the other approaches, the BHBO achieved the best convergence speed as compared to the competing methods. It is therefore concluded that the proposed HBO method can be used to optimize the FS process, whether in terms of classification accuracy or selection size.

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Metaheuristic Algorithms-based Feature Selection Approach for Intrusion Detection

Cited by 16 publications

References 1 publication

Reviewing various feature selection techniques in machine learning‐based botnet detection

Reviewing various feature selection techniques in machine learning‐based botnet detection

Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

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