International Journal of Computer Science Trends and Technology (IJCST) – Volume 11 Issue 3, May-Jun 2023 ISSN: 2347-8578 www.ijcstjournal.org Page 64 Evaluating Internet of Things Wireless Sensor Network Intrusion Detection System based An Architectural Metrics Scorecard Based Approach Prabhjot Kaur [1] , Rupinder Singh [2] , Rachhpal Singh [3] [1] Department of Computer Science, Khalsa College Amritsar ABSTRACT The anticipated scope, the architecture of IOT WSNIDS, and how they align with the deployment architecture are all compared using IOT WSNIDS architectural metrics. These metrics can be utilized to evaluate an IOT WSNIDS architectural efficiency and to aid in the design of efficient IOT WSNIDS. IOT WSNIDS play a significant role in the security of wireless sensor networks by analyzing wireless-specific traffic, including scanning for external users trying to link to the network through access points. As wireless technology evolves frequently, designing IOT WSNIDS is a challenging work. Architectural metrics can play a significant part in the design of IOT WSNIDS by assessing the sections that are problematic for the architecture of an IOT WSNIDS. We examine a variety of architectural metrics that are pertinent to IOT WSNIDS in this study. The central focus of testing and assessing an IOT WSNIDS is a "scorecard" containing the collection of values. A IOT WSNIDS can be evaluated by giving different architectural metrics related to IOT WSNIDS a score. We use three well-known IOT WSNIDS, Snort, OSSEC, and Bro, as examples of how to use our architectural metrics scorecard-based evaluation technique. Finally, we discuss the outcomes and profound opportunity for further research in this field. Keywords: Architectural Metrics, IOT WSN, Metrics, IDS, and Scorecard. I. INTRODUCTION IOT WSNIDS has ushered in a brand-new, amazing world. Every day, its technology improves, and its popularity rises. The main issue with IOT WSNIDS, however, has been security. For a while, IOT WSNIDS had very scant security, if any, on a wide- open medium. The IOT WSN Intrusion Detection System is a fresh approach to help solve this issue, along with enhanced encryption techniques. A hardware or software program known as an intrusion detection system (IDS) monitors network and/or system activity for malicious behaviour or policy violations and generates reports for a management station (Wikipedia, 2012). This is only done for the wireless network by a wireless IDS. This technology keeps an eye on network traffic for vulnerabilities and alerts staff to take action. "If you cannot measure it, you cannot improve it," Lord Kelvin once stated. This fact also holds true for concerns about wireless network security. This well acknowledged management theory applies to security as well; an activity cannot be managed if it cannot be measured. Metrics can be a useful tool for security providers to assess the efficacy of various security programs components. Metrics have a significant impact on IOT WSNIDS design. Since the field of wireless network security is still in its infancy, it is difficult to define security metrics for this technology. There is still a lack of a common vocabulary and best practices that are well- documented [1]. In order to evaluate intrusion detection systems, which are now popular for IOT WSN in the commercial sector, this article offers an architectural metrics scorecard-based methodology. We outline a testing approach we created to assess IOT WSNIDS by giving scores to different architectural metrics that are relevant to it. The methodology used in this study compares IOT WSNIDS against a set of architectural metrics that are relevant to IOT WSNIDS, rather than one another. Systems with any wireless requirements will be able to customize the evaluation of ID technologies to meet their unique requirements owing to this paper's generalized approach. The evaluation may be expanded to include additional measures such as logistical metrics, performance metrics, quality metrics, etc. since evaluation corresponds to a static set of architectural metrics. This paper's standard comparison strategy also provides us with scientific reproducibility. II. SNORT, OSSEC AND BRO IDS We chose three IOT WSNIDS—Snort, OSSEC, and Bro—as they are among the most well-known and utilize various technologies—in order to illustrate the architectural metrics scorecard based evaluation method to IOT WSNIDS. (a) Snort A formidable open-source intrusion detection and prevention system (IDS and IPS), SNORT offers real-time network traffic analysis and data packet tracking. To find potentially malicious activities, SNORT employs a rule-based language that integrates anomaly, protocol, and signature inspection methods. Network administrators can detect Common Gateway Interface (CGI) assaults, buffer overflows, stealth port scans, and denial-of-service (DoS) and distributed DoS (DDoS) attacks using SNORT. A set of rules developed by SNORT characterize malicious network activity, spot malicious packets, and notify users. SNORT is a piece of open-source software that is available for personal as well as commercial use. Which network traffic should be gathered and what should happen when malicious packets are detected are determined by the SNORT rule RESEARCH ARTICLE OPEN ACCESS