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2022-10-18 08:41:18 | The benefits of taking an intent-based approach to detecting Business Email Compromise (lien direct) |  By Abhishek Singh.BEC is a multi-stage attack. Adversaries first identify targets, then they establish rapport with the victim before exploiting them for whatever their end goal is. In the case of BEC, a threat actor can impersonate any employee in the organization to trick targets. A policy that checks for authorized email addresses of the sender can prevent BEC attacks. However, scaling the approach for every employee in a large organization is a challenge. Building an executive profile based on email analysis using a machine learning model and scanning emails against that profile will detect BEC. Data collection for building and training machine learning algorithms can take time, though, opening a window of opportunity for threat actors to exploit. Detection of exploitation techniques such as lookalike domains and any differences in the email addresses in the "From" and "Reply-to" fields can also detect BEC messages. However, the final verdict cannot account for the threat actor's intent. The intent-based approach detects BEC and then classifies it into the type of scam. It catches BEC messages, irrespective of whether a threat actor is impersonating a C-level executive or any employee in an organization. Classification based on the type of scam can help identify which segment of an organization was targeted and which employees were being impersonated by the threat actor. The additional information will further assist in better designing preventive features to stop BEC. Business email compromise (BEC) is one of the most financially damaging online crimes. As per the internet crime 221 report, the total loss in 2021 due to BEC is around 2.4 billion dollars. Since 2013, BEC has resulted in a 43 billion dollars loss. The report defines BEC as a scam targeting businesses (not individuals) working with foreign suppliers and companies regularly performing wire transfer payments. Fraudsters carry out these sophisticated scams to conduct the unauthorized transfer of funds. This introduces the challenge of how to detect and block these campaigns as they continue to compromise organizations successfully. There are a variety of approaches to identifying BEC email messages, such as using policy to allow emails from authorized email addresses, detecting exploitation techniques used by threat actors, building profiles by analysis of emails, and validating against the profile to detect BEC. These approaches have a variety of limitations or shortcomings. Cisco Talos is taking a different approach and using an intent-based model to identify and block BEC messages. Before we get too deep into the intent-based model, take a deeper look at the commonly used approaches to block BEC from the simplistic through machine learning (ML) approaches. Policy-based detection The first place to start is with policy-based detection as it is one of the most common and simplistic approaches to blocking BEC campaigns. Let's start by looking at an example of a BEC email. |
Threat Medical Cloud | Yahoo Uber APT 38 APT 37 APT 29 APT 19 APT 15 APT 10 |
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