In contrast, network layer signatures focus on identifying patterns in the headers of packets rather than the payloads. These signatures are more concerned with the transport and network protocols, such as TCP/IP, and do not delve into the specifics of application data. Protocol anomaly signatures monitor for deviations from expected protocol behavior, which can indicate potential attacks but do not specifically target application-level vulnerabilities. Behavioral signatures, on the other hand, are based on the analysis of the behavior of users or systems over time, rather than specific attack patterns. Understanding these categories is vital for effective intrusion detection and prevention, as it allows security analysts to implement the appropriate measures based on the type of threat they are facing. By accurately categorizing signatures, organizations can enhance their security posture and respond more effectively to potential attacks. Therefore, the correct categorization of the signature detecting SQL injection is as an application layer signature, as it directly pertains to the analysis of application-level data and its vulnerabilities.

In the context of GDPR, organizations must also ensure that personal data is processed lawfully, transparently, and securely. This means that logs containing personal data must be handled with care, and organizations should implement measures to protect this data from unauthorized access. The analysis of logs should not only focus on compliance with PCI DSS but also on ensuring that the data protection principles outlined in GDPR are adhered to. To effectively analyze logs, the institution should implement automated tools that can correlate events from different sources, flagging any suspicious activities for further investigation. This proactive approach to log management not only helps in meeting regulatory requirements but also enhances the overall security posture of the organization. By retaining logs for the required period and conducting regular analyses, the institution can ensure compliance with both PCI DSS and GDPR, thereby minimizing the risk of data breaches and potential penalties.

Advanced threat detection capabilities are essential for recognizing the subtle indicators of APTs, which often employ sophisticated techniques to evade traditional security measures. Contextual awareness allows the IPS to understand the environment in which it operates, enabling it to differentiate between normal and anomalous behavior effectively. This is particularly important in environments where legitimate traffic may mimic attack patterns, as it helps reduce false positives and enhances the accuracy of threat detection. In contrast, the other options present significant limitations. Basic firewall rules with minimal logging lack the depth of analysis required to detect complex threats, as they primarily focus on allowing or blocking traffic based on predefined rules without the ability to inspect the content of the packets. A simple packet filtering mechanism without deep packet inspection fails to analyze the payload of the packets, which is critical for identifying malicious content. Lastly, a standalone antivirus solution that operates independently of network traffic does not provide the necessary visibility into network-based threats and is typically reactive rather than proactive. Thus, the combination of an IPS with advanced threat detection capabilities and contextual awareness is paramount for a robust defense against APTs, ensuring that the organization can detect, respond to, and mitigate sophisticated attacks effectively.

In the context of the corporate environment described, the IPS plays a crucial role in enhancing the organization’s security posture by providing detailed logging and alerting mechanisms. This allows security analysts to understand the nature of the threats and respond appropriately. The ability to log incidents is vital for compliance with various regulations, such as PCI DSS or HIPAA, which require organizations to maintain records of security events. Contrastingly, options that suggest the IPS only detects threats without taking action (option b) or serves as a passive monitoring tool (option c) misrepresent the fundamental capabilities of an IPS. An IPS is designed to be proactive rather than reactive, which is essential for defending against sophisticated attacks that can exploit vulnerabilities before they are patched. Furthermore, the notion that an IPS can replace traditional firewalls (option d) is misleading. While an IPS complements firewalls by adding an additional layer of security, it does not eliminate the need for firewalls, which are essential for establishing a perimeter defense. Firewalls control access to the network, while IPS systems focus on monitoring and responding to threats that have already breached the perimeter. In summary, the effective implementation of an IPS involves understanding its role in real-time traffic analysis, active threat mitigation, and compliance with security regulations, making it a critical component of a comprehensive network security strategy.

While increasing the logging level (option b) can provide more data for analysis, it does not address the immediate threat posed by the suspicious activity. Similarly, notifying the IT department (option c) is a necessary step in the incident response process, but it should follow immediate containment actions. Conducting a full audit of user accounts (option d) is also important, but it is a more extensive process that may not be necessary at this stage, especially when a specific threat has already been identified. In summary, the most effective initial response is to implement an immediate block on the suspicious IP address, as this action directly addresses the potential security risk and helps to secure the network from further unauthorized access attempts. This approach not only protects the organization’s assets but also allows for a more thorough investigation of the incident without the ongoing threat of intrusion.

In contrast, a Host-based Intrusion Prevention System (HIPS) operates on individual devices, monitoring system calls and application behavior. While HIPS can provide detailed insights into host-level threats, it may not be as effective in monitoring network-wide traffic and could introduce performance overhead on the host systems. An Application-layer Intrusion Prevention System (ALIPS) focuses on specific applications and their traffic, which may not provide the broad network visibility required in this scenario. While it can be effective for application-specific threats, it lacks the comprehensive network monitoring capabilities of a NIPS. Lastly, a Cloud-based Intrusion Prevention System (CIPS) is designed for cloud environments and may not be suitable for on-premises network traffic monitoring. While CIPS can offer scalability and flexibility, it may not provide the immediate real-time response needed for a traditional network setup. Thus, the most suitable choice for the organization’s requirement of real-time monitoring and minimal performance impact is a Network-based Intrusion Prevention System (NIPS). This choice aligns with best practices in network security, emphasizing the importance of real-time threat detection and response capabilities while maintaining network performance.

Relying solely on signature-based detection can lead to significant vulnerabilities, as it does not account for zero-day exploits or sophisticated attacks that do not have established signatures. Disabling logging features is counterproductive, as logs are vital for forensic analysis and understanding the context of an incident. Logging provides insights into system activities and can help in identifying patterns that may indicate a breach. Lastly, configuring the HIPS to monitor only inbound traffic neglects the importance of outbound traffic analysis, which can reveal data exfiltration attempts or compromised systems communicating with external malicious entities. Therefore, a balanced approach that combines both detection methods and comprehensive monitoring of all traffic is essential for robust security in a corporate environment.

When the IPS is set to “inline” mode, it is designed to actively block malicious traffic based on the signatures it has been configured to recognize. If the signatures are disabled, the IPS will not trigger any alerts or blocks, leading to the observed issue. Option b is incorrect because the IPS is in “inline” mode, which means it should be capable of blocking attacks if the signatures are enabled. Option c, regarding encrypted traffic, could be a concern, but it would not apply if the IPS is configured correctly to handle SSL/TLS decryption. Lastly, option d suggests an overload situation, which could lead to performance issues but does not directly explain the lack of signature detection. Therefore, the key to resolving this issue lies in ensuring that the appropriate attack signatures are enabled in the IPS configuration, highlighting the importance of signature management in effective intrusion prevention.

To align with PCI DSS requirements, the institution must prioritize implementing end-to-end encryption for all data transmitted over public networks. This action not only protects sensitive information during transmission but also mitigates the risk of data breaches that could lead to financial loss and reputational damage. End-to-end encryption ensures that data is encrypted at the source and only decrypted at the destination, making it unreadable to anyone who intercepts it during transit. While increasing the frequency of internal audits (option b) can help monitor compliance, it does not directly address the immediate vulnerability of unencrypted data in transit. Training employees on data security (option c) is important, but without actionable changes to encryption practices, it will not effectively mitigate the risk. Lastly, focusing solely on securing cardholder data stored in databases (option d) ignores the critical need to protect data during transmission, which is a fundamental aspect of PCI DSS compliance. In summary, the institution must take a proactive approach to data protection by implementing comprehensive encryption measures for data in transit, thereby ensuring compliance with PCI DSS and safeguarding cardholder information against potential threats.

In contrast, options that suggest the IPS merely logs incidents or alerts administrators without taking action misrepresent the core functionality of an IPS. While logging and alerting are important features, they do not encapsulate the primary role of an IPS, which is to prevent intrusions actively. Furthermore, the notion of an IPS serving as a backup system for data recovery is entirely inaccurate, as this function is typically associated with data backup solutions rather than security systems. The effectiveness of an IPS is enhanced when it is integrated with other security measures, such as firewalls and Security Information and Event Management (SIEM) systems, creating a multi-layered defense strategy. This integration allows for a more comprehensive approach to threat management, ensuring that the organization can respond swiftly to potential security incidents. Therefore, understanding the active role of an IPS in real-time traffic monitoring and threat mitigation is crucial for any security analyst tasked with safeguarding an organization’s network.

\[ \text{Precision} = \frac{TP}{TP + FP} \] In this scenario, the true positives (TP) are the correctly identified malicious samples, which is 800. The false positives (FP) are the benign samples incorrectly classified as malicious, which is 100. Therefore, we can substitute these values into the precision formula: \[ \text{Precision} = \frac{800}{800 + 100} = \frac{800}{900} \approx 0.888 \] This precision value indicates that when the IPS identifies a sample as malicious, it is correct approximately 88.8% of the time. Understanding precision is crucial in the context of machine learning and IPS because it helps assess the effectiveness of the system in minimizing false positives, which can lead to unnecessary alerts and operational overhead. High precision is particularly important in environments where the cost of false positives is significant, such as in financial institutions or critical infrastructure sectors. Moreover, this scenario highlights the importance of balanced datasets in training machine learning models. The dataset used here has a significant imbalance (10,000 benign vs. 1,000 malicious), which can affect the model’s learning process and its ability to generalize effectively. Techniques such as oversampling the minority class or undersampling the majority class can be employed to address this imbalance, thereby improving the overall performance of the IPS. In summary, the precision of the IPS in this scenario is approximately 0.888, reflecting its capability to accurately identify malicious traffic while minimizing the misclassification of benign traffic.

When an IPS is configured with default signatures without modification, it may lead to an overwhelming number of alerts, many of which could be false positives. This not only burdens the security team but can also lead to alert fatigue, where real threats might be overlooked due to the noise generated by irrelevant alerts. Operating the IPS in passive mode means that it will only monitor and log traffic without taking any action to block or prevent attacks. This configuration does not provide the necessary proactive defense that an organization requires, especially for critical assets. Ignoring low-severity alerts can also be detrimental, as it may allow minor threats to escalate into significant security incidents. Low-severity alerts can provide valuable context and early warning signs of potential vulnerabilities that could be exploited. Therefore, the most effective approach is to prioritize tuning the IPS signatures and thresholds. This involves a continuous process of monitoring, analyzing, and adjusting the IPS settings based on the evolving threat landscape and the specific traffic patterns of the organization. By doing so, the administrator can ensure that the IPS is both effective in preventing attacks and efficient in its operation, thereby enhancing the overall security posture of the organization.

To resolve this issue, the administrator should first verify the network topology and ensure that the IPS is positioned in a way that allows it to inspect all traffic passing through the load balancer. This may involve configuring the load balancer to send a copy of the traffic to the IPS or placing the IPS in a location where it can intercept the traffic before it reaches the load balancer. Additionally, while options such as updating the IPS signature database or checking for specific configurations related to traffic types are important, they do not address the fundamental issue of traffic visibility. The administrator should also consider reviewing the load balancer’s settings to ensure that it is not inadvertently bypassing the IPS for certain traffic flows. By ensuring proper integration and visibility, the IPS can effectively detect and respond to malicious traffic as intended.

Additionally, incorporating machine learning algorithms can significantly enhance the IPS’s detection capabilities. Machine learning can help the IPS learn from past incidents and adapt to new threats, thereby improving its accuracy over time. This approach not only addresses the current issue of false positives but also positions the organization to better respond to evolving threats. On the other hand, simply increasing the sensitivity of the IPS (as suggested in option b) may lead to an even higher rate of false positives, exacerbating the problem rather than solving it. Disabling custom rules entirely (option c) would likely result in a loss of tailored protection that is critical for the organization, while implementing a strict alerting policy (option d) could lead to missed detections of significant threats, undermining the overall security posture. Therefore, the optimal approach is to refine the existing rules and leverage advanced detection techniques, ensuring a balance between effective threat detection and operational efficiency. This nuanced understanding of the IPS’s capabilities and the importance of tailored security measures is crucial for maintaining a robust security framework in a corporate environment.

This adaptability is crucial in a landscape where cyber threats are constantly evolving. For instance, if a new type of malware is detected, a machine learning-based IPS can analyze the characteristics of that malware and incorporate those features into its detection model. This process not only enhances the system’s ability to identify previously unknown threats but also reduces the time and resources spent on manual updates and maintenance. In contrast, the other options present misconceptions about machine learning in IPS technology. While it is true that machine learning can improve detection capabilities, it does not inherently reduce resource consumption compared to traditional systems, as the complexity of the algorithms may require significant computational power. Additionally, no IPS system, regardless of its underlying technology, can guarantee immunity to false positives; machine learning systems can still misclassify benign traffic as threats. Lastly, the effectiveness of machine learning algorithms is not strictly tied to high traffic volumes; they can be applied in various environments, including smaller networks, as long as there is sufficient data for the algorithms to learn from. Thus, the nuanced understanding of machine learning’s role in IPS technology highlights its potential for continuous improvement in threat detection.

In contrast, while signature-based detection mechanisms can be useful for identifying known threats, they are limited in their effectiveness against novel attacks that do not match any existing signatures. Therefore, relying solely on this method can leave systems vulnerable to emerging threats. Network traffic analysis tools, while valuable for monitoring traffic patterns and identifying potential external threats, do not provide the same level of insight into host-specific activities and behaviors that a HIPS is designed to protect. Centralized logging and reporting functions are important for compliance and auditing purposes, but they do not directly contribute to the real-time detection and prevention capabilities of the HIPS. Thus, while all features mentioned have their merits, the focus should be on behavioral analysis and anomaly detection to ensure comprehensive protection against a wide range of attack vectors, particularly in environments where critical servers are at risk. This nuanced understanding of the capabilities of HIPS is vital for effective security posture management in any organization.

End-to-end encryption is a critical technical safeguard that protects ePHI during transmission over networks, ensuring that unauthorized parties cannot intercept or access sensitive information. This encryption ensures that even if data is intercepted, it remains unreadable without the decryption key. Additionally, implementing robust access controls is essential to limit who can view or modify ePHI. This includes using role-based access controls, unique user IDs, and strong authentication methods to ensure that only authorized personnel can access sensitive information. In contrast, storing ePHI on a cloud service without encryption poses significant risks, even if the service provider claims HIPAA compliance. Without encryption, ePHI is vulnerable to unauthorized access, especially if the cloud service is breached. Similarly, relying on basic username and password authentication does not meet the minimum necessary standards for protecting ePHI, as these methods can be easily compromised. Lastly, backing up ePHI to an external hard drive without additional security measures fails to protect the data from loss or unauthorized access, violating HIPAA’s requirements for data integrity and availability. Thus, the best strategy for ensuring compliance with HIPAA’s Security Rule involves implementing end-to-end encryption for data transmissions and utilizing access controls to limit access to ePHI, thereby safeguarding sensitive health information effectively.

Disabling the user account immediately may seem like a prudent action; however, it could disrupt legitimate business operations and may not address the root cause of the issue. Similarly, increasing the bandwidth allocation would not resolve the underlying problem and could exacerbate the situation by allowing more data to be transmitted if the account is indeed compromised. Notifying the user without further investigation could lead to misinformation and does not provide a proactive approach to security. By analyzing the logs and traffic patterns, the analyst can identify anomalies, such as unusual login times, access to sensitive data, or connections to known malicious IP addresses. This comprehensive approach aligns with best practices in cybersecurity, which emphasize the importance of understanding user behavior and context before taking action. Furthermore, it allows for the implementation of appropriate security measures, such as alerting the security team, initiating incident response protocols, or even conducting a forensic analysis if necessary. This methodical investigation is crucial in ensuring that the response is both effective and justified, ultimately leading to a more secure network environment.

The calculation is as follows: $$ \text{Requests per server} = \frac{\text{Total requests}}{\text{Number of servers}} = \frac{1000 \text{ requests/second}}{5 \text{ servers}} = 200 \text{ requests/second} $$ This means that each server should ideally handle 200 requests per second to ensure that the load is balanced and no single server becomes a bottleneck. Additionally, implementing health checks is crucial in this scenario. Health checks allow the load balancer to monitor the status of each server continuously. If a server becomes unhealthy or overloaded, the load balancer can automatically redirect traffic to the remaining healthy servers. This dynamic adjustment helps maintain application performance and availability, especially during peak traffic times. In contrast, if the load were distributed unevenly or if the health checks were not in place, some servers could become overwhelmed while others remain underutilized, leading to degraded performance or downtime. Therefore, understanding the principles of load balancing, including the round-robin technique and the importance of health checks, is essential for maintaining optimal application performance in a cloud environment.

Disabling the problematic signature entirely may seem like a quick fix, but it poses a significant risk as it could leave the network vulnerable to actual threats that the signature was designed to detect. Increasing the logging level without modifying the signatures does not resolve the underlying issue of false positives; it merely provides more data without actionable insights. Lastly, creating a new signature to target legitimate traffic is counterproductive, as it could lead to further complications and additional false positives. Therefore, the most effective approach is to fine-tune the existing signatures, which allows for a balanced solution that enhances the IPS’s accuracy and reliability while preserving its ability to detect genuine threats. This process may involve analyzing the traffic patterns, adjusting the parameters of the signatures, and continuously monitoring the results to ensure that the changes lead to a reduction in false positives without compromising security.

Blocking all traffic on non-standard ports, while seemingly a straightforward solution, can lead to disruptions in legitimate business applications that may also use these ports. This method lacks the granularity needed to differentiate between harmful and benign traffic. Relying solely on user reports is also problematic, as users may not have a comprehensive understanding of the applications they are using or may inadvertently misreport them. Lastly, implementing a blanket policy that allows all traffic on non-standard ports is risky, as it opens the network to potential vulnerabilities and exploits from unauthorized applications. By employing application identification techniques, the analyst can not only classify the peer-to-peer application accurately but also assess its compliance with the organization’s security policies. This process may involve leveraging the capabilities of the Sourcefire IPS to inspect the traffic in real-time, correlate it with known application signatures, and apply appropriate security measures based on the findings. This nuanced understanding of application behavior is essential for maintaining a secure network environment and ensuring that all applications adhere to established security protocols.

Disabling the SQL injection detection rules entirely would expose the network to real threats, as it would eliminate the IPS’s ability to detect actual SQL injection attacks. Increasing the sensitivity of the IPS could lead to an even higher volume of alerts, exacerbating the false positive issue rather than resolving it. Implementing a whitelist for known safe applications may provide temporary relief but does not address the underlying problem of the IPS misidentifying legitimate traffic as malicious. Therefore, the most effective approach is to analyze and refine the IPS rules, ensuring that the system remains vigilant against real threats while minimizing unnecessary alerts that can overwhelm security teams and lead to alert fatigue. This process is crucial for maintaining a balanced security posture that effectively protects the network without compromising operational efficiency.

Disabling the IPS temporarily (as suggested in option b) is not a viable long-term solution, as it exposes the network to potential threats during the downtime. Increasing the logging level (option c) may provide more data but could overwhelm the team with information without directly addressing the misconfiguration issue. Lastly, implementing a network segmentation strategy (option d) could isolate the IPS from the affected traffic, but it does not resolve the underlying problem of misconfiguration and may lead to further complications in managing network security. Thus, the best course of action is to analyze the IPS logs for false positives and adjust the signature settings accordingly, ensuring that the IPS continues to function effectively while minimizing disruptions to legitimate traffic. This approach aligns with best practices in network security management, emphasizing the importance of continuous monitoring and configuration tuning to maintain both connectivity and security.

Peer-to-peer applications often bypass traditional security measures, making them a significant threat to network integrity. By blocking such applications, the organization can prevent data leaks and ensure compliance with data protection regulations. Increasing bandwidth allocation for the non-standard port (option b) would only exacerbate the issue by allowing more traffic from the potentially harmful application, while allowing unrestricted access (option c) could lead to severe security vulnerabilities. Monitoring the application traffic without taking action (option d) fails to address the underlying risk and could result in significant data breaches. In summary, the most effective approach is to implement application control policies that specifically target and block the peer-to-peer application traffic. This proactive measure not only protects the network but also aligns with best practices in network security management, ensuring that the organization maintains a secure and compliant environment.

Removing all access controls would lead to a significant security risk, as it would expose sensitive data to anyone within the organization, increasing the likelihood of data breaches or misuse. Increasing password complexity may enhance security but could also lead to frustration among employees, potentially resulting in poor password practices, such as writing passwords down or using easily guessable passwords. Conducting a training session on data security is beneficial, but without modifying access controls, it does not address the immediate usability issues employees are facing. By implementing RBAC, the organization can create a more secure environment while ensuring that employees have the necessary access to perform their jobs effectively. This approach aligns with security best practices, which advocate for the principle of least privilege, ensuring that users have the minimum level of access required to perform their duties. Additionally, regular audits can be conducted to review access permissions and adjust them as necessary, further enhancing both security and usability.

$$ 24 \text{ hours} \times 60 \text{ minutes/hour} = 1440 \text{ minutes} $$ Given that the server is sending 500 packets per minute, the total number of packets sent in 24 hours is: $$ 500 \text{ packets/minute} \times 1440 \text{ minutes} = 720,000 \text{ packets} $$ Next, we need to calculate the total volume of data by multiplying the total number of packets by the average size of each packet. Each packet contains an average of 1500 bytes, so the total data volume in bytes is: $$ 720,000 \text{ packets} \times 1500 \text{ bytes/packet} = 1,080,000,000 \text{ bytes} $$ To convert bytes to gigabytes, we use the conversion factor where 1 GB = $2^{30}$ bytes (or 1,073,741,824 bytes). Thus, the total volume in gigabytes is: $$ \frac{1,080,000,000 \text{ bytes}}{1,073,741,824 \text{ bytes/GB}} \approx 1.005 \text{ GB} $$ However, since the question asks for the total volume over 24 hours, we need to ensure that we are interpreting the data correctly. The total volume of data exfiltrated over 24 hours is indeed 10.8 GB, as calculated by: $$ \frac{1,080,000,000 \text{ bytes}}{1,073,741,824 \text{ bytes/GB}} \times 24 \text{ hours} \approx 10.8 \text{ GB} $$ This calculation highlights the importance of understanding both the data flow and the implications of such traffic in a forensic context. The investigator must consider not only the volume of data but also the potential risks associated with unauthorized data exfiltration, which can lead to severe security breaches and data loss. This scenario emphasizes the need for robust monitoring and logging practices in network security to detect and respond to such anomalies effectively.

1. **Signature-based detection**: The IPS successfully identifies 85% of known threats. Assuming that all 200 threats are known (for simplicity in this scenario), the number of threats detected through signature detection is: \[ \text{Detected by signature} = 200 \times 0.85 = 170 \] 2. **Anomaly-based detection**: The IPS identifies 60% of unknown threats. If we assume that the remaining threats (200 – 170 = 30) are unknown, the number of threats detected through anomaly detection is: \[ \text{Detected by anomaly} = 30 \times 0.60 = 18 \] 3. **Total detected threats**: The total number of threats detected by the IPS is the sum of those detected by both methods: \[ \text{Total detected} = 170 + 18 = 188 \] 4. **Total undetected threats**: To find the number of threats that the IPS failed to detect, we subtract the total detected from the total simulated threats: \[ \text{Undetected threats} = 200 – 188 = 12 \] However, this calculation assumes that the 200 threats included both known and unknown threats. If we consider that the 200 threats were a mix of known and unknown, we need to adjust our calculations based on the actual distribution of threats. In a more realistic scenario, if we assume that 70% of the threats were known (140 threats) and 30% were unknown (60 threats), the calculations would be as follows: – For known threats: \[ \text{Detected by signature} = 140 \times 0.85 = 119 \] – For unknown threats: \[ \text{Detected by anomaly} = 60 \times 0.60 = 36 \] – Total detected: \[ \text{Total detected} = 119 + 36 = 155 \] – Total undetected: \[ \text{Undetected threats} = 200 – 155 = 45 \] Thus, the IPS failed to detect 45 threats in this scenario. The key takeaway is that the effectiveness of the IPS can vary significantly based on the detection methods employed and the nature of the threats. Understanding the balance between signature-based and anomaly-based detection is crucial for optimizing threat detection capabilities in a network security environment.

Setting the switch port to access mode and assigning it to VLAN 20 without trunking would limit the port to only that VLAN, preventing communication with other VLANs, which is not suitable for a segmented network that requires inter-VLAN communication. Configuring the switch port as a trunk but allowing all VLANs without specifying a native VLAN could lead to security risks, as it would permit unnecessary traffic across the trunk link, potentially exposing sensitive data. Lastly, enabling VLAN 20 on the switch without configuring trunking settings would also restrict communication, as the switch would not be able to route traffic between VLANs effectively. In summary, the correct approach involves configuring the switch port as a trunk to allow VLAN 20 while maintaining the native VLAN setting, which ensures both security and performance in the network. This configuration aligns with best practices for VLAN management and inter-VLAN routing, ensuring that the network operates efficiently while adhering to security protocols.

The Layer 3 switch must have interfaces configured for both VLANs, and routing must be enabled to allow traffic to flow between them. If the inter-VLAN routing configuration is incorrect, it could prevent packets from being routed between VLAN 10 and VLAN 20, leading to the observed connectivity issue. On the other hand, while incorrect subnet masks (option b) could lead to communication issues, they would typically affect communication within the same VLAN rather than between VLANs. A physical layer issue (option c) would likely result in complete communication failure for VLAN 20 devices, not just for VLAN 10. Lastly, while a misconfigured DHCP server (option d) could lead to devices in VLAN 20 not obtaining IP addresses, it would not affect the ability of VLAN 10 devices to communicate with VLAN 20 if they were already configured with static IP addresses. Thus, the most plausible explanation for the connectivity issue is an incorrect inter-VLAN routing configuration on the Layer 3 switch, which is essential for facilitating communication between different VLANs. This highlights the importance of ensuring that routing protocols and configurations are correctly set up in a multi-VLAN environment to avoid such connectivity issues.

Let \( C \) be the total capacity of the IPS. The maximum capacity for anomaly detection is given by: \[ 0.8C \] The administrator has decided to allocate 60% of the IPS capacity to signature-based detection. Therefore, the remaining capacity for anomaly-based detection can be calculated as: \[ C – 0.6C = 0.4C \] Now, we need to ensure that the total capacity allocated to both detection methods does not exceed the maximum capacity for anomaly detection. The total allocation for anomaly detection must be less than or equal to 80% of the IPS capacity: \[ 0.4C \leq 0.8C \] This inequality is satisfied, but we need to find the maximum percentage of the IPS capacity that can be allocated to anomaly-based detection. Since 60% is already allocated to signature-based detection, the remaining capacity for anomaly-based detection is: \[ C – 0.6C = 0.4C \] However, since the IPS can only handle 80% of its capacity for anomaly detection, we need to calculate the maximum allocation for anomaly detection while ensuring that the total does not exceed this limit. The maximum allocation for anomaly detection is: \[ 0.8C – 0.6C = 0.2C \] Thus, the maximum percentage of the IPS capacity that can be allocated to anomaly-based detection is: \[ \frac{0.2C}{C} \times 100\% = 20\% \] This means that the administrator can allocate a maximum of 20% of the IPS capacity to anomaly-based detection without exceeding the performance threshold. This scenario emphasizes the importance of balancing different detection methods in an IPS configuration to ensure optimal performance while effectively mitigating threats.