# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 2 Research Gaps and Challenges  
The study by [10] primarily focuses on the security vulnerabilities associated with the Advanced Message Queuing Protocol (AMQP), identifying a range of attacks, including replay and Masquerade attacks, Modification attacks, and Denial-of-Service (DoS) attacks.  
However, this research lacks a practical component, as no datasets or lab-based experiments were developed to simulate or analyze these attacks. Instead, the analysis relies on a comprehensive review of existing literature and prior research. While the study provides valuable insights into the security challenges of AMQP, the absence of experimental validation and novel dataset generation limits its practical applicability.  
In [11], the research explores various types of attacks across multiple IoT communication protocols, such as Cross-Site Scripting, Malicious Code Injection, Cinderella Attacks, and Big Data Handling vulnerabilities. Despite the broad scope, the work does not offer protocol-specific analysis or experimental validation. Additionally, no datasets or lab tests were developed to simulate these attacks. The study's focus on application-layer protocols is insightful but lacks specificity, as attacks like Denial of Service, Phishing, and Sniffing are not analyzed within the context of individual protocols. This omission hinders the ability to monitor network traffic or identify unique attack signatures.  
The work by [12] examines various attacks on IoT application protocols, highlighting issues such as Malicious Code Injection, Denial-of-Service (DoS), Phishing, and Sniffing attacks. However, similar to the aforementioned studies, no datasets or lab-based experiments were developed to conduct these attacks. While the study addresses critical security concerns, including Man-in-the-Middle (MitM) and DoS attacks that exploit privileged access, it does not provide an in-depth exploration of attacks specific to AMQP. This gap reduces the study's relevance to AMQP-focused security challenges.  
The research by [13] addresses the security of IoT devices across different layers of the IoT architecture. The study identifies attacks such as Access Control Attacks, Backdoor Attacks, and Keylogger Attacks. However, the work relies on a comparative analysis of existing literature and does not involve the development of datasets or lab tests to validate findings. Although the research highlights significant security challenges faced by IoT devices, the lack of experimental validation and novel dataset creation limits its contribution to practical security solutions.  
Finally, the study by [14] investigates privacy and security in data exchange within IoT networks, identifying attacks such as Malicious Code Injection, Node Tampering, Remote Configuration Exploits, and Web Application Scanning. While the research is comprehensive, it lacks practical experimentation, as no datasets or lab tests were developed to simulate these attacks. Without analyzing network traffic traces or identifying unique attack features, the findings remain largely theoretical, limiting their applicability to real-world IoT security scenarios.  
Common limitations across the reviewed works include the following:  
* Lack of Experimental Validation: None of the studies developed lab-based experiments or datasets to validate their findings.
* Absence of Protocol-Specific Focus: Few studies specifically address AMQP vulnerabilities or analyze attacks within the context of individual protocols.
* Theoretical Approaches: Much of the research is based on literature reviews and lacks practical implementation or novel dataset creation to enhance its applicability.  
This study seeks to address these limitations by developing a practical, AMQP-specific approach that includes lab-based experimentation, dataset generation, and the identification of unique attack signatures to enhance the security of IoT systems.