Recursive Learning of Genetic Algorithm Featuring Incremental Attribute Learning for Higher Dimensional Classification Problems

Zhang, Haofan; Guan, Sheng-Uei; Xu, Mai

doi:10.7763/ijmlc.2012.v2.241

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…In this section, preliminaries and terminologies necessary to understand the proposed approach have been discussed. e incremental genetic algorithm (IGA) simulates the evolving nature of attack payloads by incorporating mutation and crossover operations in the feature set [47,48]. Newly discovered features are added in descending order of their classification accuracy to the existing feature set.…”

Section: Preliminaries and Terminologiesmentioning

confidence: 99%

“…. , x n is the feature set, C is a class identified for payload y, and V i min and V i max are the minimum and maximum bounds of the feature x i , respectively [47,48].…”

Section: Globalmentioning

confidence: 99%

“…These frequent changes made in the web application and security mechanisms incorporate new challenges for testing and removing the bugs [ 43 – 45 ]. The existing techniques in the literature require a complete restructuring of the algorithm, changing the source code [ 46 ] and retraining of the training dataset [ 47 ]. Moreover, with an increase in the size of web applications, usage of code confusion, and dynamic code generation, the various static, dynamic, and hybrid techniques of detection of XSS attacks also become more complicated [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

“…Fitness Function: The fitness of a chromosome is calculated by the percentage of payloads correctly classified by the ruleset of the chromosome [ 47 ]. The global fitness of a chromosome is defined in the whole search space.…”

Section: Preliminaries and Terminologiesmentioning

confidence: 99%

“…A ruleset contains several rules, providing a solution for a classification problem. A rule defining a class is represented as follows:

where R i is a rule label, n is the number of features, x 1 x 2 ,…, x n is the feature set, C is a class identified for payload y , and V i _min and V i _max are the minimum and maximum bounds of the feature x i , respectively [ 47 , 48 ].…”

Section: Preliminaries and Terminologiesmentioning

confidence: 99%

See 4 more Smart Citations

GeneMiner: A Classification Approach for Detection of XSS Attacks on Web Services

Gupta

Singh

Mohapatra

2022

Computational Intelligence and Neuroscience

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According to OWASP 2021, cross-site scripting (XSS) attacks are increasing through specially crafted XML documents. The attacker injects a malicious payload with a new pattern and combination of scripts, functions, and tags that deceits the existing security mechanisms in web services. This paper proposes an approach, GeneMiner, encompassing GeneMiner-E to extract new features and GeneMiner-C for classification of input payloads as malicious and nonmalicious. The proposed approach evolves itself to the changing patterns of attack payloads and identifies adversarial XSS attacks. The experiments have been conducted by collecting data from open source and generating various combinations of scripts, functions, and tags using an incremental genetic algorithm. The experimental results show that the proposed approach effectively detects newly crafted malicious XSS payloads with an accuracy of 98.5%, which is better than the existing classification techniques. The approach learns variations in the existing attack sample space and identifies the new attack payloads with reduced efforts.

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Section: Preliminaries and Terminologiesmentioning

confidence: 99%

“…. , x n is the feature set, C is a class identified for payload y, and V i min and V i max are the minimum and maximum bounds of the feature x i , respectively [47,48].…”

Section: Globalmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Preliminaries and Terminologiesmentioning

confidence: 99%

“…A ruleset contains several rules, providing a solution for a classification problem. A rule defining a class is represented as follows:

Section: Preliminaries and Terminologiesmentioning

confidence: 99%

See 3 more Smart Citations

GeneMiner: A Classification Approach for Detection of XSS Attacks on Web Services

Gupta

Singh

Mohapatra

2022

Computational Intelligence and Neuroscience

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A PSO Based Incremental Hyper-Sphere Partitioning Approach to Classification

Zheng

Guan

Song

2014

International Journal of Applied Evolutionary Computation

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This paper proposes an incremental hyper-sphere partitioning approach for classification problems. Hyper-spheres that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Particle Swarm Optimization (PSO). This new algorithm is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship with a simplified expert rule structure. We solve classification problems through a combination of hyper-sphere partitioning and a Euclidean-distance based partitioning approach. Moreover, an incremental approach combined with output partitioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with seven datasets. The experimental results show that this proposed algorithm outperforms ILEGA (our former research work) and normal GA significantly in the final classification accuracy. In terms of the time complexity, it also gains significant improvement in comparison with ILEGA.

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Incremental Hyper-Sphere Partitioning for Classification

Yang

Guan

Song

et al. 2014

International Journal of Applied Evolutionary Computation

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In this paper, an Incremental Hyper-Sphere Partitioning (IHSP) approach to classification on the basis of Incremental Linear Encoding Genetic Algorithm (ILEGA) is proposed. Hyper-spheres approximating boundaries to a given classification problem, are searched with an incremental approach based on a unique combination of genetic algorithm (GA), output partitioning and pattern reduction. ILEGA is used to cope with the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. Classification problems are solved by a simple and flexible chromosome encoding scheme which is different from that was proposed in Incremental Hyper-plane Partitioning (IHPP) for classification. The algorithm is tested with 7 datasets. The experimental results show that IHSP performs better compared with those classified using hyper-planes and normal GA.

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Recursive Learning of Genetic Algorithm Featuring Incremental Attribute Learning for Higher Dimensional Classification Problems

Cited by 4 publications

References 15 publications

GeneMiner: A Classification Approach for Detection of XSS Attacks on Web Services

GeneMiner: A Classification Approach for Detection of XSS Attacks on Web Services

A PSO Based Incremental Hyper-Sphere Partitioning Approach to Classification

Incremental Hyper-Sphere Partitioning for Classification

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