MitM Attack Detection in BLE Networks Using Reconstruction and Classification Machine Learning Techniques

SQL Injection Attack (SQLIA) is a common cyberattack that target web application database. With the ever increasing and varying techniques to exploit web application SQLIA vulnerabilities, there is no a comprehensive method that can solve this kind of attacks. Therefore, these various of attack techniques required to establish many methods against in order to mitigate its threats. However, most of these methods have not yet been evaluated, where it is still just theories and require to implement and measure its performance and set its limitation. Moreover, most of the existing SQL injection countermeasures either used syntax-based detection methods or a list of predefined rules to detect the SQL injection, which is vulnerable in advance and sophisticated type of attacks because attackers create new ways to evade the detection utilizing their pre-knowledge. Although semantic-based features can improve the detection, up to our knowledge, no studies focused on extracting the semantic features from SQL stamens. This paper, investigates a designed model that can improve the efficacy of the SQL injection attack detection using machine learning techniques by extracting the semantic features that can effectively indicate the SQL injection attack. Also, a tenfold approach will be used to evaluate and validate the proposed detection model.

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Ensemble-based Machine Learning Techniques for Attack Detection

10.1109/icrito51393.2021.9596152 ◽

2021 ◽

Author(s):

Seema Sharma ◽

Narendra Singh Yadav

Keyword(s):

Machine Learning ◽

Attack Detection ◽

Machine Learning Techniques ◽

Learning Techniques

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DDoS attack detection using machine learning techniques in cloud computing environments

2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) ◽

10.1109/cloudtech.2017.8284731 ◽

2017 ◽

Cited By ~ 19

Author(s):

Marwane Zekri ◽

Said El Kafhali ◽

Noureddine Aboutabit ◽

Youssef Saadi

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Attack Detection ◽

Machine Learning Techniques ◽

Ddos Attack ◽

Learning Techniques ◽

Computing Environments ◽

Ddos Attack Detection

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A Survey on Cloud Attack Detection using Machine Learning Techniques

International Journal of Computer Applications ◽

10.5120/ijca2020920887 ◽

2020 ◽

Vol 175 (34) ◽

pp. 21-27

Author(s):

Gavini Sreelatha ◽

A. Vinaya Babu ◽

Divya Midhunchakkarvarthy

Keyword(s):

Machine Learning ◽

Attack Detection ◽

Machine Learning Techniques ◽

Learning Techniques

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Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks

10.20944/preprints201901.0311.v1 ◽

2019 ◽

Cited By ~ 2

Author(s):

Mounir Bensalem ◽

Sandeep Kumar Singh ◽

Admela Jukan

Keyword(s):

Machine Learning ◽

Optical Networks ◽

Attack Detection ◽

Machine Learning Techniques ◽

Support Vector ◽

Detection Accuracy ◽

Resource Reallocation ◽

Jamming Attacks ◽

Learning Techniques ◽

Artificial Neural

We study the effectiveness of various machine learning techniques, including artificial neural networks, support vector machine, logistic regression, K-nearest neighbors, decision tree and Naive Bayesian, for detecting and mitigating power jamming attacks in optical networks. Our study shows that artificial neural network is the most accurate in detecting out-of-band power jamming attacks in optical networks. To further mitigating the power jamming attacks, we apply a new resource reallocation scheme that utilizes the statistical information of attack detection accuracy, and propose a resource reallocation algorithm to lower the probability of successful jamming of lightpaths. Simulation results show that higher the accuracy of detection, lower is the likelihood of jamming a lightpath.

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Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

Information ◽

10.3390/info12040154 ◽

2021 ◽

Vol 12 (4) ◽

pp. 154

Author(s):

Ahmed Bahaa ◽

Ahmed Abdelaziz ◽

Abdalla Sayed ◽

Laila Elfangary ◽

Hanan Fahmy

Keyword(s):

Machine Learning ◽

Denial Of Service ◽

Attack Detection ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Precise Analysis ◽

Development Processes ◽

Iot Devices ◽

Public Datasets ◽

The Internet Of Things

In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.

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