A distributed, reаl-time mоnitоring system fоr а criticаl infrastructure network leverages MQTT for data dissemination between sensor nodes, edge computing devices, and a central control server. While MQTT's lightweight nature and publish/subscribe architecture offer advantages for resource-constrained environments, they also introduce unique security and privacy challenges within the CPS. Analyze and discuss the inherent vulnerabilities of this MQTT-based CPS, focusing on the following: Elaborate on the specific properties of CPS (e.g., real-time constraints, physical coupling, resource limitations, potential for cascading failures) and how they influence the attack surfaces associated with MQTT communication. Discuss how integrating physical processes with cyber components exacerbates the impact of vulnerabilities in the MQTT protocol, considering the potential for direct physical harm and indirect disruptions to critical services. Discuss how the publish/subscribe nature of MQTT might create unique privacy issues, such as traffic analysis or the inference of sensitive information from message patterns, and how the CPS environment impacts this. Observation 1: Use the taxonomy provided by STRIDE to discourse about the attack surfaces. Observation 2: Do not use vague or generic answers. For example, when you analyze the MQTT, explain the protocol's real issues and vulnerabilities. Rubric CPS Properties and Attack Surfaces (10 Points) Good (6-10 Points): - Provides a precise and detailed analysis of relevant CPS properties (real-time constraints, physical coupling, resource limitations, potential for cascading failures) and their impact on MQTT attack surfaces. - Effectively applies the STRIDE taxonomy (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) to identify specific attack surfaces related to MQTT in the CPS context. - Demonstrates a deep understanding of how specific CPS properties (e.g., tight coupling between cyber and physical) amplify specific STRIDE threats (e.g., tampering of sensor data leading to physical damage). - Provides concrete examples of specific attack vectors related to the specific CPS being described. Average (3-5 Points): - Identifies some relevant CPS properties and their general impact on MQTT security. - Attempts to use the STRIDE taxonomy but may lack depth or accuracy in application. - Provides a general discussion of attack surfaces but may not fully connect them to specific CPS properties. - Provides some examples, but they may be generic or lack detail. Poor (0-2 Points): - Fails to identify or accurately describe relevant CPS properties. - Demonstrates a limited understanding or misapplication of the STRIDE taxonomy. - Provides vague or inaccurate descriptions of attack surfaces. - Relies on generic statements without concrete examples. Physical Impact of Cyber Vulnerabilities (10 Points) Good (5-10 Points): - Provides a detailed and nuanced discussion of how vulnerabilities in the MQTT protocol can lead to direct physical harm and indirect disruptions to critical services. - Demonstrates a clear understanding of the causal relationships between cyberattacks and physical consequences. - Provides specific examples of how exploiting MQTT vulnerabilities (e.g., unauthorized control commands, manipulated sensor data) can lead to physical damage or service disruptions. - Demonstrates understanding of the time constraints related to the CPS, and how that impacts the physical world. Average (2-4 Points): · Provides a general discussion of the potential physical impact of cyber vulnerabilities. · May lack specific examples or detailed explanations of causal relationships. · Provides some understanding of the physical impact, but it may be superficial. Poor (0-1 Points): · Fails to adequately address the physical impact of cyber vulnerabilities. · Provides vague or inaccurate statements. · Demonstrates a lack of understanding of the connection between cyber and physical systems. Privacy Implications of MQTT and CPS Impact (10 Points) Good (5-10 Points): · Provides a thorough analysis of the privacy implications of MQTT in a CPS context, focusing on traffic analysis and the inference of sensitive information from message patterns. · Demonstrates a deep understanding of how the publish/subscribe nature of MQTT creates unique privacy challenges. · Explains how the CPS environment (e.g., sensor deployment, data aggregation) amplifies these privacy risks. · Provides specific examples of how an adversary could infer private information from MQTT traffic within the context of the defined CPS. · Discusses the tradeoffs between privacy and security within the CPS. Average (2-4 Points): · Identifies some privacy implications of MQTT but may lack depth or detail. · Provides a general discussion of traffic analysis and information inference. · May not fully connect privacy risks to the specific characteristics of the CPS environment. · Provides limited examples. Poor (0-1 Points): · Fails to adequately address the privacy implications of MQTT. · Provides vague or inaccurate statements. · Demonstrates a lack of understanding of privacy principles in a CPS context.
A mаrket reseаrcher is investigаting the relatiоnship between age grоup (18-30, 31-45, 46-60) and preference fоr three different smartphone brands (Brand X, Brand Y, Brand Z). They conduct an appropriate statistical test, which shows a significant association (p < 0.05). To further analyze the data, they decide to perform multiple pairwise comparisons. Explain why you would use a Bonferroni correction in this scenario. Calculate how many pairwise comparisons will be made in total. Show your work. If the researcher wants to maintain an overall significance level of α = 0.05 for the entire set of comparisons, calculate the adjusted significance level for each individual comparison using the Bonferroni correction. Show your work. Given the following p-values for some of the pairwise comparisons, determine which would be considered statistically significant after applying the Bonferroni correction: 18-30 vs. 31-45 for Brand X: p = 0.010 18-30 vs. 46-60 for Brand Y: p = 0.003 31-45 vs. 46-60 for Brand Z: p = 0.008