Hammertoss: Proof of concept in C#
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The document summarizes the Hammertoss malware which uses steganography to hide encrypted commands in the insignificant EOF section of JPEG images. It decrypts the commands by using a salt from a hashtag tweet along with a hardcoded key. The malware uses the InternetExplorer COM object to download images from tweeted URLs and searches the browser cache to locate and decrypt the hidden commands for execution.
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Nano‐HIFU: Nanoparticles for Liver, pancreas ‐ LabTAU, Inserm, Lyon Nano‐H, Saint‐ 2010‐2013: €1.2M Preclinical POC 29
High Intensity Focused Ultrasound metastases
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Spotistic is a location-based social media monitoring and engagement platform that allows customers to monitor and engage on social media platforms for each individual location. It emphasizes the importance of measuring key customer development metrics like sign-ups, activation of users, retention of active users, and acquiring first paying customers to validate problem/solution fit. The document recommends books on customer development and lean startup principles, and stresses the importance of not lying to yourself about metrics, building something people want, and continuously measuring and learning.
Proof Of Concept Presentation on Concept
This document describes a proposed first aid storage system. The current solutions are inefficient, while the proposed design provides (1) compartments for readily accessing first aid materials, (2) a portable work area for interacting with patients, and (3) identification of the practitioner. It would be made of ABS plastic vacuum formed into compartments bent into shapes and connected in a hexagonal formation, with clear panel doors and hinges. Proof of concept testing showed it was faster than current solutions for accessing materials to treat sprains, fractures, and lacerations.
How to Build a Proof of Concept
A proof of concept (POC) involves building a simple version of a product idea to test it with users before fully developing it. A POC should be completed in 1-4 weeks with a small team and focus on core functionality rather than polish. Usability testing the POC with real users provides critical feedback on whether the idea is worth pursuing further. For example, a POC for a stock trading app may include basic login, search, portfolio views, and simulated trading recommendations to get early feedback from potential users.
An example of a successful proof of concept
In this presentation we explain how to create a successful proof of concept for software, using a real example from our work in the Oil & Gas industry.
Big Data Proof of Concept
RCG proposes a Big Data Proof of Concept (PoC) to demonstrate the business value of analyzing a client's data using Big Data technologies. The PoC involves:
1) Defining a business problem and objectives in a workshop with client.
2) The client collecting and anonymizing relevant data.
3) RCG loading the data into their Big Data lab and analyzing it using Big Data technologies.
4) RCG producing results, insights, and recommendations for applying Big Data and taking business actions.
The PoC requires no investment from the client and provides an opportunity to explore Big Data analytics without committing resources.
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The document discusses Jugaad, a proof-of-concept toolkit that demonstrates code injection on Linux systems similar to CreateRemoteThread on Windows. It does this using the ptrace system call to attach to a process, read/write its memory, and inject shellcode that allocates memory and creates a thread to execute arbitrary code within the target process context. First, it explains how ptrace can be used to manipulate another process. Then it describes how Jugaad uses these ptrace capabilities to meet the requirements of allocating memory, creating a thread, and executing payload code inside the target process.
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Readactor----paper published on S&P(Oakland)2015.
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Design Package to Build and Evaluate Encryption Algorithms
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Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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原版制作(carleton毕业证书)卡尔顿大学毕业证硕士文凭原版一模一样
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When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
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Hammertoss: Proof of concept in C#
- 1. Hammertoss Proof of concept in C# Introspection Analysis of the Malware Behavior Salvatore Saeli aka Jabex
- 2. https://www2.fireeye.com/rs/848-DID-242/images/rpt-apt29-hammertoss.pdf 2 The HAMMERTOSS reference is available at URL
- 4. The injection technique implants the data to hide in the insignificant part of the carrier file, which is normally ignored by operating systems and software applications. Steganography by injection exploits the EOF section and injects secret data after the EOF marker which eventually has no side effect on the carrier file and is often disregarded by the execution environment. HAMMERTOSS uses JPEG file image. 4
- 5. JPEG data are byte streams, always storing 16-bit word values in big-endian format. JPEG data in general is stored as a stream of blocks, and each block is identified by a marker value. 5
- 6. 6 SOI Marker EOI Marker The first two bytes of every JPEG stream are the Start Of Image (SOI) marker values FFh D8h. All JPEG data streams end with the End Of Image (EOI) marker values FFh D9h.
- 7. 7 Relation between hash tag and cryptography algorithm
- 8. 8 APT29 tweet a URL and a hashtag. The URL directs HAMMERTOSS to a webpage containing an image or images. The hashtag provides A. a number representing a location within the image file and B. characters for appending to an encryption key to decrypt instructions within the image ==> it is a salt??
- 9. 9 PBKDF1 as specified in PKCS#5 and RFC_2898 provides Key Derivation and Key Strengthening. The parameters of the function are a hash function (such as SHA-1), a password, a salt, an iteration count and the length of the derived key to be returned. The standard PBKDF1 will just calculate the hash of password concatenated with salt, and then hash the hash value that is returned by the previous step iteration count minus one times.
- 10. 10 Using PBKDF1 obtain a implementation of AES 256 bit with salt, where salt has a variable lenght (from 4 byte to 8 byte).
- 11. 11 The appended data is encrypted, so even if detected, the investigator would be unable to decrypt the data without key material from two sources: the malware binary and the current tweet.
- 13. 13 HAMMERTOSS uses the InternetExplorer.Application COM Object to visit the URL and to obtain the stegaimage. If a instance of this object run as privileged user, the process copies all items in the visited web page into IE user’s cache.
- 14. 14 C:UsersjabexAppDataLocalMicrosoftWindowsTemporary Internet Files C:UsersjabexAppDataLocalMicrosoftWindowsTemporary Internet File Content.IE5I6CX7FXRlena[1].jpg IE cache Logic Path IE cache Real Path
- 15. 15 HAMMERTOSS searches the cache for any images at least as large as the offset specified in the tweet, it locates the encrypted data, and it decrypts the data using a key comprised of hard-coded data from the malware binary appended with the characters from the tweet.
- 16. 16 To get entries from user’s Internet Explorer cache, the process uses the DllImport C# mechanism to make a system call to wininet.dll win32 Api
- 17. 17 The GitHub repository of C# POC is available at URL https://github.com/jabex/hammertoss
- 18. 18 Uploader class The Uploader class check a JPEG image for SOI and EOI makers. If the check have successful, the process encrypts the payload and appends it to JPEG image. After that, it create a hashtag from a random salt and the original size of input image.
- 19. 19 tDiscoverer class This part of POC must run as privileged user! The tDiscoverer class creates a instance of InternetExplorer.Application COM object, makes a HTTP GET request to web page, and put all page’s content into IE user’s cache. After that, the process searches into cache entries all JPEG images at least as large as the offset specified in the hashtag.
- 20. 20 tDiscoverer class From all images obtained in previous step, the process selects the image where at [offset - 2 byte] it matches EOI marker. Finally, the process decrypts the data using encryption password appended with the characters (salt) that it has obtained from the hashtag and it executes the command.
- 21. 21 THE END