Zero-Day Vulnerabilities: Unveiling the Threat Landscape in Network Security

zaib, Raheela

doi:10.58496/mjcs/2022/007

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…This proliferation increases the number of potential entry points for cyber threats, necessitating thorough examination and development of robust mitigation strategies to protect against these emerging vulnerabilities. The increasing reliance on mobile devices for sensitive transactions [11], including banking, shopping, and various online services, has helped in a new era of convenience and accessibility. Managing finances, making purchases, and accessing confidential information on the go with the tap of a screen is convenient.…”

Section: User Behaviormentioning

confidence: 99%

Enhancing Mobile Security through Comprehensive Penetration Testing

Roshanaei

2024

JIS

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In today's era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists' toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.

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Section: User Behaviormentioning

confidence: 99%

Enhancing Mobile Security through Comprehensive Penetration Testing

Roshanaei

2024

JIS

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“…Based on the analysis and study of references [21,22], this paper comprehensively examines various intrusion detection techniques, providing valuable references for the author's work. It demonstrates that in the current Internet environment, on the one hand, the increasing popularity of the Internet has led to a rise in the number and complexity of network attacks, making emerging threats critical [23]. On the other hand, various challenges and opportunities arise from applying AutoML in intrusion detection [24].…”

Section: Introductionmentioning

confidence: 99%

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

Zhang,

Wang

2023

Applied Sciences

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In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method’s potential and performance in network intrusion detection.

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Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

Chen,

Guo,

Zhang

et al. 2023

Symmetry

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Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.

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Zero-Day Vulnerabilities: Unveiling the Threat Landscape in Network Security

Cited by 4 publications

References 21 publications

Enhancing Mobile Security through Comprehensive Penetration Testing

Enhancing Mobile Security through Comprehensive Penetration Testing

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

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