Chapter 5: The Power of Graph Inference
BforeAI | The PreCrime™ Company
Predictive Attack Intelligence & Digital Risk Protection Services
Given the current cybersecurity challenges, modern cybersecurity approaches increasingly turn to behavioral analysis and machine learning for detection. These methods look for suspicious activities, such as unusual system calls, network traffic, or behavioral anomalies, rather than relying on static patterns.
Parallel Doctrines in Cybersecurity: Intelligence, Detection, Preemption, and Deterrence
In the field of cybersecurity, the doctrines of intelligence, detection, preemption, and deterrence are equally relevant and can be applied with parallel strategies to counter digital threats.
The development of predictive cybersecurity technologies is deeply rooted in the latest advancements in AI, including machine learning, natural language processing, and deep learning. Machine learning models can now process massive data streams, identifying hidden patterns and potential threats far earlier than traditional methods. Natural language processing allows predictive technologies to understand and analyze threat intelligence reports, picking upon early indicators of threat activity. Furthermore, deep learning techniques are enhancing the ability of predictive systems to detect even the most subtle anomalies, continuously improving accuracy and resilience against a wide array of attack vectors.
Rather than merely reacting to attacks after they occur, deterrence preemptively removes the incentive for threat actors to target an organization in the first place. Think of it like a bank putting money in a time-lock safe - it’s a deterrent that makes it much harder for criminals to obtain access.
By leveraging advanced AI-driven threat intelligence, BforeAI preempts malicious activity long before it escalates into an attack. This predictive approach neutralizes threats early, diminishing the potential for damage and signaling to attackers that targeting the protected environment is both futile and costly.
In this context, BforeAI has developed PreCrime , an advanced technology designed to predict and preempt cyber threats by leveraging graph inference techniques.
PreCrime is built on the methodologies outlined in two pivotal research papers: Following Passive DNS Traces to Detect Stealthy Malicious Domains via Graph Inference and A Domain is Only as Good as its Buddies: Detecting Stealthy Malicious Domains via Graph Inference.
What is Graph Inference?
Graph inference is a method of deriving new information about entities by analyzing the relationships and structures within a graph.
To perform graph inference, data is first organized into a graph structure. For cybersecurity applications, graphs will analyze various digital entities – such as user accounts, network devices, IP addresses, domains, or files – and their relationships, such as shared IP addresses or similar behavior patterns. By analyzing the structure and properties of these graphs, it is possible to infer the likelihood of certain entities being malicious.
Graphs often start with a few known data points or “seed” nodes. These known nodes might have labels indicating benign or malicious status, for example, based on prior threat intelligence. From these seeds, relationships and properties propagate to connected nodes in the graph, gradually building an understanding of other nodes.
How does the Inference Process work?
One of the most common graph inference methods, label propagation involves starting with some known labels or properties of nodes (such as reliable or unreliable) and spreading this information through the network to predict labels for unclassified nodes. For instance, if a node is strongly connected to a majority of reliable nodes, it might infer a similar property.
Graph inference often includes pattern recognition and identifying clusters, or groups of nodes with stronger internal connections than external ones. This helps infer that nodes within a cluster likely share similar characteristics or behaviors.
Inference processes can also use random walks and connectivity-based methods, which involve tracing paths through the graph by randomly selecting edges to follow, which can reveal clusters, identify communities, or estimate the likelihood of relationships. Nodes that are "closer" to each other in terms of graph connectivity might be inferred to have stronger relationships.
Graph Inference Techniques
Graph inference techniques are applied to analyze the constructed graphs. These techniques include:
> Anomaly detection:
By understanding what normal relationships in a graph look like, graph inference can detect anomalies, such as unusual connections or isolated nodes that do not fit the standard pattern.
> Link prediction:
Another key application is predicting which new edges, or connections, are likely to form. This helps in identifying domains that are likely to become malicious based on their current associations.
> Community detection:
This consists of identifying clusters or communities within the graph that exhibit similar behavior. Malicious domains often cluster together due to shared infrastructure or coordinated activities.
> Predictive analytics:
Graph inference is used to make predictions, such as which infrastructure is the most likely to prepare a cyberattack against a certain infrastructure.
Up Next | Chapter 6: How PreCrime Works
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