Software As Service Attack Detection and Prevention using Manipulated QR Code

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Manushree Sahay, Sandeep Vanjale


A sort of software service delivery paradigm known as "software as a service" (SaaS) includes a wide variety of commercial possibilities and problems. Despite being drawn to SaaS by its advantages, users and service providers are reluctant to incorporate their businesses into it because of security concerns. This article emphasizes the usefulness and adaptability of SaaS in a variety of situations, such as software defined networking, cloud computing, mobile cloud computing, and the Internet of Things. The examination of SaaS security issues, including data security, application security, and SaaS deployment security, is then started. Potential solutions or strategies that may be used in conjunction with one another are then offered for a secure SaaS platform. The SQL injection attack is the SaaS application's most dangerous vulnerability. This might result in sensitive and important data loss. (e.g., financial, personal). Through these kinds of assaults, attackers might steal sensitive information that is crucial to a business or organization, which has a detrimental effect on both physical (like data) and intangible (like reputation) assets. This research aims to investigate the viability of using machine learning techniques for application-level SQL injection detection. Various dangerous and benign payloads were utilized to train the classifiers employed in the testing methodologies. They detect if a payload includes malicious code when given one. This study aims to identify harmful activities in a Software as a Service (SaaS) environment based on the cloud. The anti-phishing advice for this technique, which is known as a secure QR code, includes a thorough analysis of the most current research on the usability and security of QR codes. The most important use cases and accompanying attack paths were identified. To do this, we conducted a comprehensive literature study. Social engineering, or phishing, is the fraud that exploits QR codes as an attack vector most often covered in the media. The usage of QR codes on smartphones has spread from auto production plants.

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