Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks

Abstract

To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding malicious domains by passively analyzing DNS data. The proposed model builds a real-time, accurate, middleweight, and fast classifier by combining a genetic algorithm for selecting DNS data features with a two-step quantum ant colony optimization (QABC) algorithm for classification. The modified two-step QABC classifier uses K-means instead of random initialization to place food sources. In order to overcome ABCs poor exploitation abilities and its convergence speed, this paper utilizes the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach (K-mean and QABC) to deal with the large size of uniform resource locator (URL) data is one of the main contributions of this paper. The major point is that blacklists, heavyweight classifiers (those that use more features), and lightweight classifiers (those that use fewer features and consume the features from the browser) may all be improved with the use of the suggested machine learning method. The results showed that the suggested model could work with more than 96.6% accuracy for more than 10 million query–answer pairs.

Phisheild Website Detection System for Safeguarding Online Security

“…This paper addresses two primary objectives: identifying optimal classifiers for detecting phishing among twenty-four options and determining the feature identificationtechniques for malicious url datasets. [12]Through evaluation of two datasets using eight metrics, the study concludes that J-48 classifiers & FilteredClassifier, exhibit superior performance in detection, while the InfoGainAttributeEval process stands as the most effective feature detection method among the considered [10] [18].…”

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning

“…The advancement of optimization techniques, driven by an expansion in scientific knowledge, has led to notable breakthroughs in various fields, including cybersecurity [ 3 ]. Artificial intelligence (AI) plays a pivotal role in this evolution, especially through the development of bio-inspired optimization algorithms.…”

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

“…They encompass various branches of engineering, including mechanical engineering (automotive [ 20 , 21 ], aerospace [ 22 ], fluid dynamics [ 23 ], thermal engineering [ 24 ], automation [ 25 ], robotics [ 26 ], mechatronics [ 27 ], MEMS [ 28 , 29 ], etc. ), electrical engineering [ 30 ] (including power engineering [ 31 ], electronics [ 32 ], microelectronics [ 33 ] and nanoelectronics [ 33 ], control engineering [ 34 ], renewable energy [ 35 ], biomedical engineering [ 36 ], telecommunications [ 36 ], signal processing [ 37 ]), geometrical optics [ 38 ], photonics [ 39 ], nanophotonics and nanoplasmonics [ 40 ], image processing [ 41 ] including pattern recognition [ 42 ], computing [ 30 ], [ 43 ], networking (computer networks [ 44 ] including Internet and Intranet [ 45 ], social networks [ 46 ], networks on a chip [ 47 ], optical networks [ 48 ], cellular (mobile) networks [ 49 ], wireless sensor networks [ 50 ], Internet of things [ 51 ], etc. ), data clustering and mining [ 52 ], civil engineering [ 53 , 54 ], architectural design [ 55 ], urban engineering [ 56 ], smart cities [ 57 ], traffic control and engineering [ 58 ], biomedicine and healthcare […”