Countermeasure to Structured Query Language Injection Attack for Web Applications using Hybrid Logistic Regression Technique

Abstract

The new generation of security threats has been promoted by real-time applications, where several users develop new ways to communicate on the internet via web applications. Structured Query Language injection Attacks (SQLiAs) is one of the major threats to web application security. Here, unauthorised users usually gain access to the database via web applications. Despite the giant strides made in the detection and prevention of SQLiAs by several researchers, an ideal approach is still far from over as most existing techniques still require improvement, especially in the area of addressing the weak characterisation of input vectors which often leads to low prediction accuracy. To deal with this concern, this paper put forward a hybrid optimised Logistic Regression (LR) model with Improved Term Frequency Inverse Document-Frequency (ITFIDF-LR). To show the effectiveness of the proposed approach, attack datasets is used and evaluated using selected performance metrics, i.e., accuracy, recall, specificity and False Positive Rate. The experimental results via simulation when compared with the benchmarked techniques, achieved performance record of 0.99781 for accuracy, recall and F1-score as well as 0.99782, 0.99409 and 0.00591 for precision, specificity and False Positive Rate (FPR) respectively. This is an indication that the proposed approach is efficient and when deployed is capable of detecting SQLiA on web applications.

Detecting Structured Query Language Injections in Web Microservices Using Machine Learning

“…Table 2 provides further details on the criteria implemented by various authors. [17] 0.9904 0.9898 0.9903 0.991 Artificial Neural Network (ANN) [13,18] 0.9893 0.9870 0.9913 0.99 AdaBoost (AB) [17,21] 0.9808 0.9559 0.9592 0.9561 Decision Tree (DT) [16,18,22,23] 0.9668 0.9315 0.88955 0.9164 Random Forest (RF) [18,22,23] 0.9634 0.9247 0.8947 0.9149 Support Vector Machine (SVM) [18,22,23] 0.9546 0.9706 0.9085 0.9395 Logistic Regression (LR) [4] 0.9503 0.9737 0.9089 0.9653 Naive Bayes (NB) [18,24] 0.9074 0.8966 0.7985 0.9010 KNN (K-Nearest Neighbors) [21] 0.8920 0.9143 0.8931 0.8853 Furthermore, the choice of these algorithms is justified for the following reasons. The decision tree (DT) algorithm is simple to interpret and allows for the identification of characteristics relevant to the detection of SQL injections.…”

“…Preventing these types of attacks has become a priority for organizations and software development industries. Early and accurate detection is crucial to avoid harmful effects [4,5]. Historically, preventing these types of attacks involved validating data entry and examining it for special characters associated with common attacks.…”

Secure Health Information System with Blockchain Technology

“…There are several attempts by malicious users to compromise the security of medical records. Cyberattacks such as SQL injection [16], which targets the data aggregated on the database over time are very common. To this effect, the use of various disruptive technologies such as the blockchain, artificial intelligence, and internet of things for protecting medical records from human errors and cyberattacks is discussed in [17].…”

An Empirical Study on Anomaly Detection Using Density-based and Representative-based Clustering Algorithms

“…Anomaly detection is the process of finding data patterns (outcomes, values, or observations) that deviate from the rest of the other observations or outcomes. Anomaly detection is heavily used in solving real-world problems in many application domains like medicine, cybersecurity [1], fraud detection [2], networking, transportation, and military surveillance for enemy activities, but not limited to only these fields, as anomaly detection is classified under deep learning which is applicable in all fields such as in mathematics and statistics [3]. These deviating outcomes or observations are referred to as anomalies (outliers, deviants, discordant observations, exceptions, surprises, or abnormalities) in different application domains [4].…”