The Reputation-Based Infrastructure Intelligence (RBII) Framework: A New Frontier in Cyber Threat Intelligence

In the rapidly evolving world of cybersecurity, attackers have become more sophisticated, utilizing large-scale internet infrastructure to evade detection. Current cyber threat intelligence (CTI) methodologies, such as the MITRE ATT&CK and Lockheed Martin Cyber Kill Chain frameworks, focus primarily on detecting indicators of compromise (IOCs) like IP addresses, domain names, and malware signatures. While these traditional frameworks offer valuable insights into the tactics, techniques, and procedures (TTPs) of adversaries, they often miss a critical element: infrastructure-level intelligence.

What if, instead of focusing solely on IOCs, we could assess the reputation of the infrastructure—such as BGP (Border Gateway Protocol) Autonomous Systems (ASes)—that attackers use to conduct their operations? Such an approach would allow for broader, more effective detection and mitigation of malicious activities. The Reputation-Based Infrastructure Intelligence (RBII) Framework is a new, forward-looking methodology that does not exist yet, designed to do exactly that. The RBII Framework introduces a novel way to assess and manage the reputation of Autonomous Systems, enabling Security Operations Centers (SOCs) to monitor, block, and contain malicious infrastructure at a much broader and more strategic level.

Why a New Framework Is Needed

Traditional CTI models, while effective in identifying specific indicators such as IP addresses, domains, and malicious files, often fall short in addressing the broader context of the infrastructure that attackers leverage. Attackers have shifted to utilizing entire AS networks to conduct their operations, often leasing space from bulletproof hosting providers or compromising poorly managed ASes to maintain their anonymity and resilience.

While blacklisting individual IP addresses or domains may offer temporary protection, it fails to address the larger problem of compromised infrastructure. The RBII Framework fills this gap by focusing on the reputation of Autonomous Systems, the underlying infrastructure that enables malicious activities on a global scale. By doing so, SOCs can gain a more comprehensive view of the threat landscape, allowing them to detect and mitigate threats earlier in the attack lifecycle.

Why Focus on BGP Autonomous Systems?

1.1 The Role of BGP Autonomous Systems in Internet Infrastructure

Autonomous Systems (ASes) are critical to the global operation of the internet, serving as the backbone for routing traffic across different regions and networks. Each AS is a network or collection of IP prefixes (blocks of IP addresses) that is controlled by a single organization, which can be an internet service provider (ISP), a cloud hosting provider, a large enterprise, or a data center. These ASes are connected to each other through the Border Gateway Protocol (BGP), which is responsible for determining the most efficient path for data to travel from its source to its destination. BGP enables autonomous systems to share routing information with one another, thus forming the basis of global internet routing.

Autonomous Systems play a vital role in maintaining the reliability, scalability, and flexibility of internet communications. Large ISPs, content delivery networks (CDNs), cloud providers, and data centers all depend on ASes to manage and control their internet traffic flow. Each AS is assigned a unique identifier, known as an Autonomous System Number (ASN), which is used to route traffic between ASes across the world.

The BGP protocol, while integral to internet routing, was originally designed without considering modern security challenges. It assumes that all ASes are trusted entities and will advertise legitimate routes. This lack of built-in security features has opened up opportunities for exploitation, as ASes can easily be compromised, misconfigured, or maliciously manipulated. The critical role that ASes play in maintaining global internet traffic makes them an attractive target for adversaries.

Autonomous Systems are particularly important because they sit at the infrastructure level, meaning they have a far-reaching influence over vast swathes of IP space. An attack or compromise on one AS can affect entire networks, services, or geographies, and disruptions at this level can have widespread consequences, including downtime, rerouting of legitimate traffic, and data interception. Given their position in the internet architecture, monitoring AS behavior is crucial for identifying and mitigating threats that leverage the underlying infrastructure.

1.2 How Attackers Exploit Autonomous Systems

Cybercriminals and nation-state actors have recognized the potential of Autonomous Systems as strategic assets for executing large-scale and long-term cyber campaigns. The architecture of BGP, combined with the complex relationships between ASes, offers multiple avenues for exploitation. Some of the key tactics attackers use include:

• Compromising Autonomous Systems: Attackers target poorly managed or vulnerable ASes, gaining control through various means such as exploiting software vulnerabilities or credential theft. Once an AS is compromised, attackers can use it as a launchpad for their operations, whether it be distributing malware, managing botnets, or coordinating large-scale attacks. The compromised AS allows attackers to obscure their activities, making it harder for defenders to identify the true source of malicious traffic. This tactic is particularly effective in low-reputation ASes, where lax security practices make detection and mitigation more difficult.
• Leasing AS Space from Bulletproof Hosting Providers: Bulletproof hosting providers are notorious for catering to cybercriminals by providing hosting services that ignore abuse complaints, takedown requests, or law enforcement actions. These rogue providers often operate within certain ASes that are intentionally designed to harbor illegal or malicious activities. They may advertise their services directly to criminal enterprises, offering infrastructure for activities such as phishing campaigns, ransomware operations, command-and-control (C2) servers, or black-market platforms. Because bulletproof hosting providers ignore traditional channels for reporting abuse, they are highly effective at enabling cybercriminals to operate with impunity.
• BGP Hijacking: One of the most alarming ways attackers exploit ASes is through BGP hijacking. BGP hijacking occurs when an attacker deliberately manipulates the BGP routing tables to advertise false routes. This causes internet traffic destined for a legitimate network to be rerouted through a compromised or malicious AS. BGP hijacking allows attackers to:
• Route Flapping and Path Manipulation: Attackers can exploit vulnerabilities in BGP to manipulate routing paths or cause route flapping, where routes are frequently updated or withdrawn in a way that destabilizes the network. Such activities can degrade service quality, create network congestion, and cause delays in traffic flow, leading to denial-of-service conditions for users connected to those routes. Attackers might also use this tactic to cover their tracks by frequently changing routes and avoiding persistent detection.

Given these tactics, the importance of monitoring and securing ASes becomes clear. By focusing on the infrastructure layer, security teams can gain valuable insights into how attackers leverage compromised or rogue ASes to further their operations, often undetected by conventional IOC-based detection systems.

1.3 Limitations of Traditional Blacklisting

Traditional blacklisting systems focus on blocking individual IP addresses or domains that have been flagged for malicious activity. However, this approach has several critical limitations in the modern threat landscape:

• Reactive Nature: IP and domain blacklists are inherently reactive. They rely on known indicators of compromise (IOCs) that are discovered after an attack or malicious activity has already occurred. By the time an IP or domain is blacklisted, attackers have likely moved on to new infrastructure. In this cat-and-mouse game, blacklists become quickly outdated, leaving organizations exposed to new or evolving threats.
• Evasion Tactics: Attackers have developed numerous methods to evade IP or domain blacklists. They frequently change IP addresses, use dynamic DNS services, or rotate between different domains. In many cases, attackers will switch infrastructure mid-operation, rendering static blacklists ineffective. Attackers can also leverage techniques like domain generation algorithms (DGAs) to create thousands of new domains, making it impossible for defenders to keep up with all the potential malicious indicators.
• Inability to Address Compromised Infrastructure: Traditional blacklists do not account for the fact that entire Autonomous Systems may be compromised. Blocking individual IP addresses within a compromised AS is akin to treating the symptoms rather than the cause. Attackers can easily move to other IPs within the same AS, rendering the blacklist ineffective. By focusing on the entire AS, security teams can better understand the context of an attack and proactively monitor or block malicious infrastructure as a whole, rather than chasing down individual IPs or domains.

The RBII Framework offers a proactive and strategic alternative to traditional blacklisting. By assigning reputation scores to entire Autonomous Systems based on their behavior, historical patterns, and responsiveness to abuse complaints, RBII allows organizations to assess the risk of an AS before any individual IOCs are even identified. This infrastructure-centric approach offers a broader and more forward-looking defense against cyber threats, enabling security teams to monitor, contain, and block malicious activities at the infrastructure level.

By focusing on the reputation of ASes, the RBII Framework can help organizations preemptively mitigate risks from compromised or malicious infrastructure, significantly improving their ability to detect and respond to threats that would otherwise go unnoticed.

Challenges Facing SOCs in Detecting Malicious Infrastructure

2.1 The Increasing Complexity of Cyber Threats

The cybersecurity landscape has undergone a significant transformation in recent years, with cyber threats becoming more intricate, persistent, and difficult to detect. Security Operations Centers (SOCs) are constantly bombarded with new attack techniques that leverage large-scale infrastructure rather than relying solely on isolated indicators of compromise (IOCs). While traditional detection methods are adept at identifying specific malicious files, IP addresses, or domains, they often fail to address the broader and more sophisticated strategies employed by modern attackers.

One of the key challenges facing SOCs is the shift in attacker tactics. No longer confined to small-scale, individual attacks, today’s adversaries are exploiting entire Autonomous Systems (ASes) and global internet infrastructure to maintain persistence, scale their operations, and evade detection. This infrastructure-level exploitation enables attackers to move fluidly across vast networks, often obscuring their malicious activities within legitimate traffic. Techniques such as BGP hijacking, AS route manipulation, and the use of bulletproof hosting services allow attackers to orchestrate large-scale, distributed campaigns while remaining hidden from traditional security measures that focus on endpoint detection.

Moreover, the use of advanced evasion techniques like fast-flux domains, dynamic DNS, and the rapid cycling of IP addresses further complicates the detection process. These methods make it increasingly difficult for SOCs to track malicious infrastructure, as attackers can pivot between compromised assets or infrastructures rapidly, bypassing static blacklists and reactive security measures. SOC analysts are left in a constant race to catch up with threat actors who are always a step ahead in their ability to adapt and scale their operations globally.

The sheer volume of data that SOCs must process compounds the complexity of the threat landscape. On a daily basis, SOCs must analyze millions of security events, alerts, and logs, often involving traffic patterns across a multitude of ASes. This overwhelming data flow requires continuous updates to blocklists, detection rules, and correlation engines—efforts that are both resource-intensive and prone to human error. As a result, SOCs frequently struggle to distinguish between legitimate and malicious infrastructure traffic. This operational challenge leaves organizations vulnerable to infrastructure-based attacks that slip through the cracks of traditional detection systems.

2.2 The Problem of False Positives

False positives are one of the most pressing issues that SOCs encounter. These occur when legitimate traffic or benign infrastructure is incorrectly flagged as malicious, triggering alarms that divert valuable resources and time. In modern cybersecurity environments, the prevalence of false positives has escalated to the point where they significantly hinder operational efficiency, leading to "alert fatigue" among SOC analysts. As analysts are forced to sift through countless false alarms, the likelihood of missing critical, genuinely malicious activities increases, putting organizations at heightened risk.

The root cause of the false positives problem lies in the limitations of traditional detection systems, which often rely on static IOCs—such as specific IP addresses, domain names, or file hashes—to identify potential threats. These IOCs are inherently transient, as attackers frequently change their infrastructure and tactics to evade detection. For example, an IP address associated with a phishing campaign today might be reassigned to a legitimate service tomorrow, leading to unnecessary blocking of legitimate traffic. This volatility creates a scenario in which blacklists become outdated almost as soon as they are generated, causing SOCs to block benign activities mistakenly and disrupt normal business operations.

The impact of false positives extends beyond mere inconvenience. In environments where the stakes are high—such as in financial institutions, healthcare, or critical infrastructure sectors—blocking legitimate services due to false positives can lead to severe consequences. This includes lost revenue, damaged reputations, and even legal liabilities in the case of disrupted essential services. Moreover, frequent false positives can undermine trust in the SOC’s capabilities, as other departments within an organization may begin to view cybersecurity as a bottleneck to business operations rather than a critical enabler.

Additionally, SOCs are often burdened by the operational cost of false positives. Analysts are forced to spend a significant portion of their time investigating false alarms, which reduces the time available to focus on real threats. This inefficiency can lead to burnout among SOC teams, who are already stretched thin by the growing volume of alerts. The opportunity cost is significant, as the time wasted on false positives could have been better spent on analyzing more serious threats, performing proactive threat hunting, or fine-tuning detection models.

The RBII Framework as a Solution

The Reputation-Based Infrastructure Intelligence (RBII) Framework addresses these challenges by shifting the focus from volatile, easily manipulated indicators to a more holistic, infrastructure-level approach. By assessing the reputation of entire Autonomous Systems (ASes) rather than relying solely on individual IOCs, the RBII Framework enables SOCs to gain deeper insights into the infrastructure that attackers use to launch their operations. This infrastructure-centric perspective offers several advantages:

1. Reduced False Positives: The RBII Framework evaluates the reputation of ASes based on a combination of historical behavior, responsiveness to abuse complaints, and real-time traffic patterns. This contextual analysis allows SOCs to differentiate between benign ASes and those that are compromised or involved in malicious activities. By filtering out legitimate infrastructure, RBII minimizes the noise from false positives, allowing analysts to focus on high-priority threats.
2. Infrastructure-Level Intelligence: By monitoring ASes and assessing their overall reputation, the RBII Framework enables SOCs to take a proactive approach to threat detection. Rather than waiting for specific IOCs to appear, SOCs can preemptively monitor high-risk ASes and contain threats before they escalate. This method not only improves detection accuracy but also reduces the burden on SOCs to constantly update blocklists and rules for rapidly changing indicators.
3. Broader Context and Visibility: The RBII Framework provides wider visibility into how malicious actors operate within the global infrastructure. Instead of focusing on isolated events, SOCs gain a broader understanding of how attackers leverage entire networks, allowing them to detect coordinated attacks that might span multiple ASes or regions.
4. Improved Efficiency: By reducing false positives and providing more accurate intelligence, the RBII Framework frees up SOC analysts to focus on genuine threats. This improves overall SOC efficiency, reduces analyst fatigue, and ensures that critical incidents receive the attention they deserve.

In conclusion, the growing complexity of modern cyber threats and the prevalence of false positives have made it clear that SOCs need a more comprehensive and infrastructure-focused approach to threat detection. The RBII Framework offers a solution that addresses these challenges by evaluating infrastructure-level reputation, enabling SOCs to detect, monitor, and respond to threats more effectively and with fewer false alarms. Through its innovative approach, RBII provides the actionable intelligence SOCs need to stay ahead of increasingly sophisticated adversaries.

The Reputation-Based Infrastructure Intelligence (RBII) Framework: A New Approach

The Reputation-Based Infrastructure Intelligence (RBII) Framework represents a transformative shift in how organizations approach cyber threat intelligence. Unlike traditional frameworks that rely primarily on individual indicators of compromise (IOCs) such as IP addresses or domains, RBII evaluates the reputation of entire Autonomous Systems (ASes) by analyzing their behavior, historical patterns, and associations with known malicious activity. This infrastructure-focused methodology allows Security Operations Centers (SOCs) to move beyond reactive threat detection toward a more proactive, infrastructure-level defense strategy. By assessing the trustworthiness and activity of entire AS networks, RBII enables more comprehensive and strategic threat mitigation, empowering SOCs to identify and neutralize threats at an earlier stage, before they can cause widespread harm.

RBII introduces a new dimension to CTI by treating the internet's backbone infrastructure—BGP ASes—as a critical element in the threat landscape. This shift allows SOCs to monitor the behavior and reputation of these ASes in real-time, providing an infrastructure-wide view of potential malicious activities. Instead of chasing ever-changing IOCs, RBII focuses on understanding the underlying infrastructure that attackers depend on, enabling a more robust and sustainable approach to threat intelligence and response.

Core Components of the RBII Framework

3.1 Reputation Scoring for Autonomous Systems

At the core of the RBII Framework is its reputation-based scoring system, which evaluates entire ASes based on their historical behavior, response to threats, and connections to malicious activities. This AS-level assessment allows SOCs to make more informed, strategic decisions about how to handle traffic from these networks, balancing the need for proactive defense with the goal of minimizing false positives and operational disruptions.

Key Factors in Reputation Scoring:

• Proliferation of Malicious Activity: This factor assesses how frequently the AS has been involved in malicious campaigns. SOCs can identify ASes that have consistently hosted malicious activity, such as malware, phishing, botnets, or DDoS attacks. ASes associated with repeated abuse are flagged as high-risk, allowing for preemptive measures to be taken against them.
• Duration and Persistence of Malicious Behavior: Some ASes show long-term involvement in malicious activities, while others might only exhibit short-lived malicious behavior. ASes with persistent malicious activities are given higher risk scores, signaling to SOCs that these networks are likely to continue supporting cybercriminals.
• Response to Abuse Complaints: The speed and effectiveness of an AS's response to abuse reports are critical in determining its reputation. If an AS is non-compliant or slow to react to takedown requests from ISPs, threat intelligence providers, or law enforcement, it signals a higher risk level. These ASes are ranked lower, as their reluctance to cooperate makes them more attractive to cybercriminals.
• Geopolitical Considerations: The geographical location of an AS influences its reputation. ASes located in regions with weak cybersecurity enforcement or minimal cooperation with international law enforcement tend to have lower reputational scores. SOCs need to be particularly cautious when dealing with ASes in jurisdictions where cybercrime is more likely to go unpunished.

Scoring Mechanism:

• Real-Time Updates: The RBII Framework continuously updates the reputation scores of ASes based on new threat intelligence, abuse reports, and observed behaviors. This dynamic scoring mechanism ensures that SOCs always have the most current and accurate intelligence available, allowing for timely and effective threat responses.
• Historical Decay: Malicious behavior is not weighed indefinitely. The RBII Framework applies a decay factor to historical incidents, allowing ASes that demonstrate long-term improvement or remediation efforts to regain their reputation. This feature helps reduce false positives and ensures that legitimate ASes are not permanently penalized for past mistakes.

Reputation scoring enables SOCs to anticipate risk rather than simply react to incidents as they occur, providing a more strategic, long-term perspective on threat intelligence.

3.2 Dynamic Behavioral Analysis for Threat Detection

A key innovation of the RBII Framework is its reliance on dynamic behavioral analysis rather than static IOCs. By monitoring and analyzing the behavior of ASes over time, SOCs can detect anomalies and suspicious activities that might indicate potential threats, even when traditional IOCs have not yet emerged. This behavioral intelligence provides early warning signals, allowing for proactive threat detection.

Key Behavioral Indicators:

• Anomalous Traffic Patterns: Sudden or unexplained spikes in traffic volume originating from an AS can indicate the presence of a botnet or DDoS attack. Likewise, unusual traffic flows or deviations from normal patterns may suggest malicious activity such as data exfiltration or covert communication between infected machines.
• BGP Route Hijacking Detection: Monitoring for real-time BGP route changes is critical in detecting BGP hijacking events, where traffic is maliciously rerouted through compromised ASes. Such hijacks can facilitate man-in-the-middle attacks, traffic manipulation, or mass data interception. Early detection allows SOCs to intervene before attackers can leverage the hijacked traffic for nefarious purposes.
• Geographic Movement and Flux: If an AS frequently alters its infrastructure, such as leasing IP space in new regions or frequently modifying its routing paths, this could signal malicious infrastructure migration—a common tactic used by attackers to evade detection. Monitoring these changes enables SOCs to identify shifting threat infrastructure and act accordingly.

By continuously monitoring these and other behavioral indicators, the RBII Framework provides SOCs with an early detection system that flags potential threats before they become widespread.

3.3 Machine Learning for Predictive AS Risk Modeling

One of the most innovative aspects of the RBII Framework is its application of machine learning (ML) to predict which ASes are likely to be compromised in the future. By training ML models on large datasets of historical threat intelligence, including previous AS compromises, abuse reports, and BGP hijacking incidents, RBII can identify patterns that signal future risk.

Machine Learning Model Inputs:

• AS Age and Ownership: Newer ASes, or ASes that frequently change ownership, are more prone to compromise. Attackers often exploit new or unstable networks to establish infrastructure quickly without drawing attention. Machine learning models take these variables into account to flag ASes that may be more vulnerable.
• Volume of IP Space: ASes that suddenly expand their IP space without clear operational needs are flagged as suspicious. Such behavior can indicate that an AS is preparing to support large-scale malicious campaigns, such as DDoS attacks or botnet deployments.
• Temporary or “Burner” ASes: Some attackers create short-lived ASes that are used temporarily for malicious operations, then quickly abandoned. Machine learning models are trained to detect these abnormal churn patterns, which often precede malicious activities.

By incorporating machine learning into the RBII Framework, SOCs can proactively identify high-risk ASes and apply increased scrutiny or preemptive mitigation measures before malicious activities take place. This predictive capability gives SOCs the ability to focus resources on the most likely future threats, making their operations more efficient and effective.

3.4 Collaborative Threat Intelligence Sharing

The RBII Framework is designed to operate within a global, collaborative threat intelligence ecosystem, where SOCs, ISPs, cloud providers, internet registries (e.g., RIPE NCC, ARIN), and threat intelligence vendors work together to share data on AS behavior and infrastructure-level threats.

Benefits of Collaboration:

• Real-Time Data Sharing: By sharing data on compromised ASes, BGP hijacks, and abuse reports in near real time, organizations can accelerate the detection and containment of malicious infrastructure. This collective intelligence provides a broader and more comprehensive view of the global threat landscape.
• Global Situational Awareness: SOCs gain global visibility into emerging threats by collaborating with industry peers and threat intelligence providers. This shared perspective allows organizations to identify patterns and trends that may not be apparent from a local or regional perspective.
• Automated Sharing and APIs: The RBII Framework supports automated data sharing through APIs, allowing SOCs to seamlessly ingest and distribute AS reputation data across their threat intelligence platforms. This ensures that intelligence is always up to date, actionable, and integrated into the SOC's broader detection and response ecosystem.

3.5 Granular Incident Response Capabilities

Once an AS has been flagged as suspicious or malicious, the RBII Framework empowers SOCs to implement granular, context-aware responses that minimize disruption while ensuring effective threat containment. Instead of automatically blocking an entire AS—which could disrupt legitimate traffic—RBII provides more nuanced response options.

Incident Response Options:

• Traffic Throttling: SOCs can limit the bandwidth or traffic volume from suspicious ASes, preventing large-scale attacks while allowing legitimate users to continue accessing services. This technique minimizes disruption while reducing the attack surface.
• Quarantine Zones: Traffic from high-risk ASes can be redirected to sandboxed environments for deeper analysis and inspection before it is allowed to interact with critical systems. This approach provides an added layer of defense, ensuring that malicious traffic is detected before it can cause harm.
• Conditional Access: SOCs can allow selective access to benign IP ranges within a suspicious AS while blocking known malicious subnets. This ensures that legitimate users are not impacted, while the threat from malicious entities is effectively neutralized.

These granular response capabilities enable SOCs to mitigate threats in a way that is both effective and efficient, ensuring minimal disruption to legitimate operations while containing and neutralizing the threat.

By leveraging reputation scoring, behavioral analysis, machine learning, and collaborative intelligence sharing, the RBII Framework equips SOCs with a comprehensive toolkit for detecting and mitigating infrastructure-based threats at scale.

Implementing the RBII Framework: A Comprehensive Blueprint for SOCs

The successful implementation of the Reputation-Based Infrastructure Intelligence (RBII) Framework within a Security Operations Center (SOC) demands a structured, phased approach. Given the complexity of integrating infrastructure-level intelligence into existing cybersecurity operations, careful attention must be paid to data sourcing, system integration, and automation. The RBII Framework must function cohesively with existing tools and processes to provide SOCs with real-time, actionable intelligence, empowering them to proactively mitigate threats at the AS level.

Below is a detailed step-by-step guide that outlines how SOCs can implement the RBII Framework:

Step 1: Data Collection and Aggregation

1.1 BGP Routing Data Integration BGP routing data forms the backbone of the RBII Framework. Collecting real-time routing data from multiple reliable sources is essential for building a comprehensive and dynamic understanding of Autonomous Systems (ASes). Integrating data from services such as BGPmon, RouteViews, CAIDA (Center for Applied Internet Data Analysis), and various internet registries (e.g., RIPE NCC, ARIN, APNIC) provides continuous insights into AS behavior, routing changes, and anomalies.

• BGPmon and RouteViews offer detailed insights into global BGP route updates and AS path changes, allowing SOCs to detect anomalies such as BGP hijacking, route leaks, or suspicious routing behaviors.
• CAIDA provides additional tools for analyzing the dynamics of the global internet topology, offering data about IP address allocations and peering relationships. This information is critical for understanding how ASes interact and for identifying abnormal routing paths that might suggest malicious activity.

1.2 Abuse Reports and Threat Intelligence Feeds In addition to BGP routing data, integrating abuse reports and global threat intelligence feeds strengthens the RBII Framework's understanding of AS reputation. SOCs should collect abuse reports from various sources, including:

• AbuseIPDB and Spamhaus, which track IP-level abuse activities, such as spam and malware hosting. These platforms contribute to understanding how ASes are being used by cybercriminals.
• Global threat intelligence platforms such as AlienVault OTX, Recorded Future, VirusTotal, and Anomali provide insights into specific attack campaigns, threat actor infrastructure, and TTPs associated with certain ASes.

This data aggregation helps SOCs establish a comprehensive threat profile for each AS, allowing for more accurate reputation scoring and threat detection.

Step 2: Reputation Scoring Engine Development

2.1 Dynamic Reputation Scoring A reputation scoring engine is the core component of the RBII Framework, as it assesses the trustworthiness of ASes based on real-time and historical data. SOCs need to either develop or integrate a dynamic reputation engine that continuously updates AS scores as new data is ingested.

• The reputation engine should factor in multiple elements, such as historical behavior, abuse response times, malicious activity proliferation, and geopolitical risk factors.
• Real-time updates should be driven by continuous BGP data analysis, threat intelligence feed integration, and feedback loops from abuse reports and SOC responses.
• The engine must employ weighting mechanisms that allow SOCs to prioritize certain behaviors or indicators based on their organizational risk tolerance. For example, if an AS has a history of hosting ransomware, its reputation score should reflect that risk more heavily.

2.2 Historical Decay and Adaptive Learning Reputation scores should decay over time for previously malicious ASes that demonstrate good behavior. This ensures fairness in scoring, allowing ASes to "recover" if they improve their operational practices. On the other hand, ASes that repeatedly exhibit suspicious or malicious behavior should be flagged as persistent threats and given lower scores.

Additionally, the scoring engine should integrate adaptive learning capabilities that adjust scoring criteria based on new trends in infrastructure abuse or the emergence of new types of attacks. This will enable SOCs to remain flexible and responsive to evolving threat landscapes.

Step 3: Machine Learning Model Training and Continuous Learning

3.1 Model Training on Historical Data Machine learning (ML) is critical for the RBII Framework’s predictive capabilities, helping to identify high-risk ASes before malicious activity occurs. SOCs should train ML models using historical data on known AS compromises, BGP hijacking events, abuse reports, and emerging threat patterns. Training models on this data will enable the framework to detect patterns that may signal potential future compromises.

• The training dataset should include inputs such as AS age, growth rate of IP ranges, ownership changes, traffic anomalies, and malicious peer relationships.
• The model should also learn from patterns of temporary AS setups, which are often used by attackers for short-lived, high-impact campaigns like DDoS attacks or malware distribution.

3.2 Continuous Model Updates and Fine-Tuning As the threat landscape evolves, so too should the models. Continuous retraining is essential for maintaining the accuracy of risk predictions. The ML models should be integrated with feedback loops that incorporate real-world outcomes—such as confirmed incidents of AS compromise or false positives—to improve their performance.

Models should also be fine-tuned to account for regional differences in AS behavior. For example, ASes in certain regions may have different patterns of peering and traffic flows due to geopolitical factors, requiring region-specific learning.

Step 4: Incident Response Playbooks

4.1 Playbook Customization The RBII Framework should include customizable incident response playbooks tailored to different levels of AS risk. Each playbook will outline specific actions based on the AS reputation score, allowing SOCs to respond appropriately to different threat levels.

• High-Risk AS Playbook: For ASes with extremely low reputation scores, immediate actions may include automatic traffic blocking, quarantining traffic, and full route monitoring to prevent further malicious activity.
• Medium-Risk AS Playbook: For ASes with moderately low scores, SOCs may implement traffic throttling, enhanced monitoring, or sandboxing traffic to detect malicious payloads.
• Low-Risk AS Playbook: For ASes with low risk but flagged for abnormal behavior, SOCs can use conditional access controls or monitor specific traffic segments while allowing the bulk of traffic to pass through.

4.2 Dynamic Incident Responses Playbooks should be dynamic and capable of adjusting in real-time based on updated intelligence. For example, if an AS reputation score changes during an incident due to new information (such as a recent abuse report), the playbook should dynamically escalate or de-escalate the response accordingly.

Step 5: SIEM and SOAR Integration

5.1 Seamless SIEM Integration To maximize the RBII Framework’s effectiveness, it must be fully integrated into SIEM (Security Information and Event Management) platforms. This integration allows SOCs to monitor AS reputation scores and correlate them with internal logs and alerts. SIEM systems can be configured to trigger alerts when traffic from low-reputation ASes is detected, enabling automated incident escalation.

• Correlation Rules: SOCs should create custom correlation rules within their SIEMs that use AS reputation data to enrich existing alerts. For instance, when a known malicious AS attempts to communicate with an internal system, it should trigger a high-priority alert.

5.2 SOAR for Automated Responses Integrating the RBII Framework with SOAR (Security Orchestration, Automation, and Response) platforms automates the detection, alerting, and response workflows, drastically reducing manual intervention. SOAR integration enables automated playbook execution based on AS reputation scores, ensuring timely and accurate threat mitigation.

• Automated Remediation: When an AS is flagged as high-risk, SOAR platforms can trigger automated responses such as blocking traffic or isolating affected systems.
• Adaptive Orchestration: SOAR workflows can adapt based on real-time changes in AS risk profiles, ensuring that incident responses remain flexible and responsive.

Step 6: Monitoring and Visualization Dashboards

6.1 Real-Time Dashboard Development SOC analysts need real-time visibility into the global landscape of AS reputation. A real-time dashboard should display the latest AS reputation scores, along with detailed visualizations of traffic flows, routing changes, and geographic distribution of ASes.

• Traffic Flow Visualization: Interactive maps and flow diagrams should show how traffic is moving across different ASes, highlighting potential threats from low-reputation ASes.
• Geolocation Tracking: SOCs can use geolocation features to monitor which regions high-risk ASes operate in, helping them adjust their risk posture based on location-specific threat intelligence.

6.2 Alerting and Reporting Dashboards should provide automated alerting mechanisms that notify analysts when suspicious AS behavior is detected. These alerts can be color-coded based on the severity of the threat, with high-risk AS alerts requiring immediate attention and lower-risk alerts being sent for monitoring.

• Custom Reports: SOCs can generate reports on AS activity, reputation changes, and incident responses, helping teams track trends over time and refine their defensive strategies.

Conclusion: The Future of Infrastructure-Centric Threat Intelligence

The Reputation-Based Infrastructure Intelligence (RBII) Framework represents a new paradigm in cyber threat intelligence, focusing on the infrastructure that enables malicious activities rather than chasing individual IOCs. By analyzing the reputation of BGP Autonomous Systems, SOCs can gain earlier insights into malicious operations and implement more effective, proactive defenses.

Through a combination of reputation scoring, behavioral analysis, machine learning, and collaborative intelligence sharing, RBII provides SOCs with the tools to detect and block malicious infrastructure at scale, while reducing false positives and improving operational efficiency. As attackers continue to exploit internet infrastructure to evade detection, the RBII Framework offers a cutting-edge solution to stay ahead of these sophisticated threats.

By adopting the RBII Framework, SOCs will be better equipped to defend their networks, monitor global infrastructure threats, and proactively mitigate cyber risks in a constantly evolving threat landscape.