AI DRIVEN DETECTION OF INJECTION ATTACKS IN API’S USING BIDIRECTIONAL RECURRENT NEURAL NETWORKS
Dr A. Yashwanth Reddy, Sri Harsha Dixit, SV Swathi, N Shashank, KV Mahendra Prashanth · Journal of Science Engineering Technology and Management Sciences · 2025
Injection attacks in APIs are a significant security concern, where malicious actors exploit vulnerabilities by inserting harmful code into API requests.These attacks can lead to unauthorized access, data breaches, and system compromises, ultimately affecting the integrity and confidentiality of applications.Historically, the detection of injection attacks in APIs has evolved alongside the increasing reliance on APIs in modern software development.Early detection methods primarily involved manual code reviews and pattern matching, which were often insufficient due to the complexity and variety of injection techniques.As APIs became more prevalent, the demand for automated and sophisticated detection mechanisms grew.Traditional systems for mitigating injection attacks have relied on input validation, parameterized queries, and the use of Web Application Firewalls (WAFs).These methods aim to prevent malicious inputs from being processed by the system.However, they frequently fall short in detecting complex or novel attack patterns, leaving systems vulnerable.The motivation for developing advanced detection systems arises from the limitations of traditional approaches.The increasing sophistication of attackers and the critical role of APIs in modern applications necessitate more robust and intelligent security measures.This research is driven by the need to enhance detection capabilities, reduce false positives, and adapt to emerging threats in real time.A core problem with traditional systems is their reactive nature and limited adaptability.They often require manual updates to recognize new attack vectors and struggle to detect obfuscated or zero-day attacks.Additionally, their reliance on predefined rules results in high false positive rates, where legitimate traffic is mistakenly flagged as malicious.To address these challenges, the proposed system leverages Bidirectional Recurrent Neural Networks (BRNNs) to detect injection attacks in APIs.BRNNs process input sequences in both forward and backward directions, capturing contextual information more effectively than unidirectional models.This approach enables the system to identify complex patterns associated with injection attacks, thereby improving detection accuracy and adaptability to evolving threats.