A survey on the application of deep learning for code injection detection

Abstract Code injection is one of the top cyber security attack vectors in the modern world. To overcome the limitations of conventional signature-based detection techniques, and to complement them when appropriate, multiple machine learning approaches have been proposed. While analysing these approaches, the surveys focus predominantly on the general intrusion detection, which can be further applied to specific vulnerabilities. In addition, among the machine learning steps, data preprocessing, being highly critical in the data analysis process, appears to be the least researched in the context of Network Intrusion Detection, namely in code injection. The goal of this survey is to fill in the gap through analysing and classifying the existing machine learning techniques applied to the code injection attack detection, with special attention to Deep Learning. Our analysis reveals that the way the input data is preprocessed considerably impacts the performance and attack detection rate. The proposed full preprocessing cycle demonstrates how various machine-learning-based approaches for detection of code injection attacks take advantage of different input data preprocessing techniques. The most used machine learning methods and preprocessing stages have been also identified

CODDLE: Code-Injection Detection With Deep Learning

Code Injection attacks such as SQL Injection and Cross-Site Scripting (XSS) are among the major threats for today's web applications and systems. This paper proposes CODDLE, a deep learning-based intrusion detection systems against web-based code injection attacks. CODDLE's main novelty consists in adopting a Convolutional Deep Neural Network and in improving its effectiveness via a tailored pre-processing stage which encodes SQL/XSS-related symbols into type/value pairs. Numerical experiments performed on real-world datasets for both SQL and XSS attacks show that, with an identical training and with a same neural network shape, CODDLE's type/value encoding improves the detection rate from a baseline of about 75% up to 95% accuracy, 99% precision, and a 92% recall value

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Very High Cycle Fatigue Behavior of Additively Manufactured 316L Stainless Steel

The present paper is focused on an experimental study of the damage-to-failure mechanism of additively manufactured 316L stainless steel specimens subjected to very high cycle fatigue (VHCF) loading. Ultrasonic axial tension-compression tests were carried out on specimens for up to 109 cycles, and fracture surface analysis was performed. A fine granular area (FGA) surrounding internal defects was observed and formed a “fish-eye” fracture type. Nonmetallic inclusions and the lack of fusion within the fracture surfaces that were observed with SEM were assumed to be sources of damage initiation and growth of the FGAs. The characteristic diameter of the FGAs was ≈500 μm on the fracture surface and were induced by nonmetallic inclusions; this characteristic diameter was the same as that for the fracture surface induced by a lack of fusion. Fracture surfaces corresponding to the high cycle fatigue (HCF) regime were discussed as well to emphasize damage features related to the VHCF regime