A Countermeasure to SQL Injection Attack for Cloud Environment

Chen, Chien‐Ming; Sun, Xiuyang; Liu, Shuai; Lin, Jerry Chun‐Wei

doi:10.1007/s11277-016-3741-7

Cited by 18 publications

(6 citation statements)

References 26 publications

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“…Using the KDD99 dataset, the authors of [ 45 ] present and evaluate a hierarchical IDS that employs ML and knowledge-based techniques. In [ 46 ], the authors investigated the performance of several ML models, including the RF and gradient-boosting machines, in practical IoT environments. Data-poisoning assaults were simulated by modifying the training data of the datasets using a stochastic algorithm so that the analysis could be carried out.…”

Section: Related Work and Literature Reviewmentioning

confidence: 99%

An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms

Salman

Taher

Hammadi

et al. 2022

Sensors

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Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%.

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Section: Related Work and Literature Reviewmentioning

confidence: 99%

An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms

Salman

Taher

Hammadi

et al. 2022

Sensors

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“…1.6 × 10 +1 9.1 × 10 −5 0.0 × 10 +0 0.0 × 10 +0 9.4 × 10 +2 1.6 × 10 −2 9.4 × 10 +2 1.6 × 10 −2 2.0 × 10 +0 2.9 × 10 −5 f 2-39 7.4 × 10 +1 4.2 × 10 −4 4.8 × 10 +1 6.4 × 10 −4 7.1 × 10 +1 1.2 × 10 −3 7.0 × 10 +1 1.2 × 10 −3 4.8 × 10 +1 7.0 × 10 −4 f 2- 40 3.3 × 10 +3 1.9 × 10 −2 7.7 × 10 +1 1.0 × 10 −3 1.3 × 10 +2 2.3 × 10 −3 1.2 × 10 +2 2.2 × 10 −3 7.2 × 10 +1 1.0 × 10 −3 f 2- 41 3.0 × 10 +3 1.7 × 10 −2 3.3 × 10 +2 4.5 × 10 −3 2.3 × 10 +3 3.9 × 10 −2 2.3 × 10 +3 3.9 × 10 −2 2.0 × 10 +3 3.0 × 10 −2 f 2-42 2.7 × 10 +3 1.5 × 10 −2 2.7 × 10 +3 3.7 × 10 −2 2.7 × 10 +3 4.7 × 10 −2 2.7 × 10 +3 4.7 × 10 −2 2.7 × 10 +3 4.0 × 10 −2 3.6 × 10 +4 4.0 × 10 −1 4.2 × 10 +4 4.4 × 10 −1 3.6 × 10 +4 5.5 × 10 −1 4.3 × 10 +4 7.6 × 10 −1 5.1 × 10 +4 9.1 × 10 −1 f 2-10…”

Section: Featurementioning

confidence: 99%

“…It is essential to address that the KDD99 and UNSW-NB15 datasets do not contain attacks related to the cloud computing, such as the SQL injection. Reference [41] proposed a countermeasure to detect these attacks, specifically in the cloud environment. The method in Reference [41] can be applied to the cloud environment, without the need for an application's source code.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System

et al. 2020

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The significant increase in technology development over the internet makes network security a crucial issue. An intrusion detection system (IDS) shall be introduced to protect the networks from various attacks. Even with the increased amount of works in the IDS research, there is a lack of studies that analyze the available IDS datasets. Therefore, this study presents a comprehensive analysis of the relevance of the features in the KDD99 and UNSW-NB15 datasets. Three methods were employed: a rough-set theory (RST), a back-propagation neural network (BPNN), and a discrete variant of the cuttlefish algorithm (D-CFA). First, the dependency ratio between the features and the classes was calculated, using the RST. Second, each feature in the datasets became an input for the BPNN, to measure their ability for a classification task concerning each class. Third, a feature-selection process was carried out over multiple runs, to indicate the frequency of the selection of each feature. From the result, it indicated that some features in the KDD99 dataset could be used to achieve a classification accuracy above 84%. Moreover, a few features in both datasets were found to give a high contribution to increasing the classification’s performance. These features were present in a combination of features that resulted in high accuracy; the features were also frequently selected during the feature selection process. The findings of this study are anticipated to help the cybersecurity academics in creating a lightweight and accurate IDS model with a smaller number of features for the developing technologies.

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“…e researchers also introduced a cryptographic mechanism to ensure the confidentiality and integrity of the data in the WSN and achieved encouraging results [19]. Literature [20] proposed a detection scheme for SQL injection attacks, which does not require access to the source code of the application, so it can be directly applied to the cloud environment. Literature [21] proposed a certificate-based aggregate signature scheme in WSN, which can resist forgery attacks.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Pan

Fang

Chu

et al. 2021

Security and Communication Networks

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The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

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A Countermeasure to SQL Injection Attack for Cloud Environment

Cited by 18 publications

References 26 publications

An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms

An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms

An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

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