How to Emulate a Ransomware Attack

Overview Ransomware is here to stay and cyber security professionals need to be trained to prevent, detect, respond, and recover from ransomeware attacks. So, how do we do that in an ethical and repeatable way? This post will walk through how SpecterInsight’s ransomware emulation capability works and give insight into the inner workings of a […]

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How to Leverage Internal Proxies for Lateral Movement, Firewall Evasion, and Trust Exploitation

Overview The primary tactic we will be exploring in this post is the use of proxies inside of a target network. There are a lot of different types of proxies for both offense and defense. This post will focus on Internal Proxies (MITRE 1090.001) which are a sub-technique of Proxy (MITRE 1090). We will cover

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Credential Harvesting with PowerShell and SpecterInsight

Overview Credential harvesting, also known as credential theft or credential stealing, refers to the collection sensitive authentication information from individuals or systems. The goal of credential harvesting is to obtain usernames, passwords, or other authentication tokens that allow access to protected resources. This post will cover a variety of different credential harvesting techniques, how to

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Persistence with WMI Event Subscription and PowerShell Cradles

Overview In this post, we are going to demonstrate how to build a script to automate persistence lay down via WMI Event Subscription and dynamically generated PowerShell payloads. By the end, we will have a single parameterized script that can be leveraged to establish signature resistant persistence, thus alleviating much of the tedious manual work

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PE Malware Machine Learning Dataset

The purpose of this dataset is to provide raw labeled portable executables to security and AI researchers in order to improve cyber security in the industry. Many of the datasets that I have seen (such as this dataset from a Microsoft sponsored Kaggle competition) does not provide the raw binary files themselves, but rather metadata that has already been pre-extracted from the samples. This prevents a lot of potential learning that can come from exploring other features that could be extracted from the raw samples themselves.

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