Attack Intention Estimation Based on Syntax Analysis and Dynamic Analysis for SQL Injection

Kuroki, Kotomi; Kanemoto, Yo; Aoki, Kazufumi; Noguchi, Yasuhiro; Nishigaki, Masakatsu

doi:10.1109/compsac48688.2020.00-41

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Moreover, the lack of class-imbalance solutions in major machine learning-driven methods like [35] results in reduced and nongeneralizable outcomes [36]. Author [37] applied syntax-structure learning concept towards injection detection; however, lower computational efficiency (accuracy 83.1%) confines it is more limited for NoSQL databases. Noticeably, these approaches cannot be suitable for NoSQL databases due to dynamic data in nature and different data structures [38].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Sreejith

Senthil

2022

BioMed Research International

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The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.

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Section: Related Workmentioning

confidence: 99%

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Sreejith

Senthil

2022

BioMed Research International

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“…One common technique is predicate pushdown, which involves moving filter conditions closer to the data retrieval operations. This reduces the size of intermediate results and minimizes the amount of data processed in subsequent stages (Kuroki et al, 2020). Another method is join reordering, which rearranges the order of join operations to exploit indexes and reduce the number of tuples processed.…”

Section: Query Rewriting Techniquesmentioning

confidence: 99%

Untitled

2024

AJBAIS

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Section: Query Rewriting Techniquesmentioning

confidence: 99%

Advanced Query Optimization in SQL Databases for Real-Time Big Data Analytics

Rahman,

Islam,

Kamruzzaman

et al. 2024

AJBAIS

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This study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments.

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Attack Intention Estimation Based on Syntax Analysis and Dynamic Analysis for SQL Injection

Cited by 9 publications

References 7 publications

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model

Untitled

Advanced Query Optimization in SQL Databases for Real-Time Big Data Analytics

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