Defense Advanced Research Projects Agency (DARPA)’s Post

#StochasticQuasiconformalPerturbationTheory Been working out frameworks, this one might thwart some serious stuff, and help with critical infrastructure, food security, and defense planning. It's still in development - on the more complex side, it can potentially act as an extension to Universal Techmuller Theory... That would be a #GatewayToTheStrange, and a lot of work... For now, it'll likely end up in #AugmentedReality technology - Perturbations are cool, as an immersive effect... it's a superb idea.

In this room, we will analyse the log data from a compromised workstation using the Kibana interface. Within this room's tasks, we will explore the components of the Elastic (ELK) Stack and gain insights into the various search and filter functions available in Kibana. Our ultimate goal will be to decipher the actions executed by a malicious actor on the affected system. Additionally, to ensure that our findings are useful in practice, we will map indicators of compromise (IOCs) identified in the logs to relevant tactics and techniques outlined in the MITRE ATT&CK framework.Room Objectives Get familiar with the Elastic (ELK) Stack and its components. Understand the significance of log data analysis in detecting and investigating security incidents. Get introduced to Kibana and its key functionalities for data analysis, visualization, and exploration. Gain hands-on experience with analysing log data to identify real-world security threats and anomalies.

We're excited to share our latest paper, "VALIDATE: A Deep Dive into Vulnerability Prediction Datasets," recently accepted in IST Journal. 🔗 Access the in-press open access paper here: https://lnkd.in/dqUgPUUP 🔗 Access the online tool here: https://validatetool.org/ 🧑🔬 Authors: Matteo Esposito, Davide Falessi 📊 Key Highlights: Conducted the first systematic dataset review (SDR) focusing on vulnerability prediction studies. Introduced VALIDATE, an online tool aimed at simplifying dataset search based on specific features. Presented a non-exact replica of a vulnerability prediction system to showcase VALIDATE's potential. 🔍 Abstract Overview: Our study addresses the pressing issue of vulnerabilities in software, emphasizing dataset availability and features. We introduce the VulnerAbiLty predIction DatAseT rEpository (VALIDATE) to aid researchers in locating and sharing datasets efficiently. 🔬 Methodology & Findings: Through systematic literature review, we analyzed datasets used in vulnerability prediction studies. Results indicate limited accessibility to datasets, with only a small fraction readily available and stable. 🔑 Key Takeaways: Our repository now hosts 31 datasets, including those provided by authors. VALIDATE facilitates dataset discovery, promoting collaboration and reproducibility in research. 🔍 Keywords: Security, Replicability, Vulnerability, Machine Learning, Repository, Dataset #Research #VulnerabilityPrediction #MachineLearning #SecureSoftwareEngineering #Cybersecurity #OpenScience #ResearchCommunity

Great paper summarizing the prompt techniques - The Prompt Report. - 58 text-only prompting techniques including zero-shot, few-shot, thought generation, ensembling, self-criticsm, and decomposition techniques. Few-shot CoT performs the best among the few techniques they benchmarked - Prompt Engineering: Meta prompting, auto prompt, automatic prompt engineer, gradient free instructional prompt search, prompt optimization with textual gradients, RLPrompt, dialogue-comprised policy-gradient-based discrete prompt optimization - Answer Engineering: Answer shape, answer space, answer extractor - Agents: tool use agents, code-based agents, observation-based agents, RAG - Security: prompt hacking (prompt injection, jailbreaking), risks (data privacy, code generation concerns, customer service), hardening measures ( prompt-based defense, guardrails, detectors). - Alignment: ambiguity, biases, calibration, prompt sensitivity 🔗arxiv.org/abs/2406.06608

Before who wasn't around then asks what Labyrinth is: It's an infinitely quantum mechanized defense system to be used as containerization for polymorphic algorithms with a high value information, low availability high confidentiality status encrypted, hashed, passworded file based Killswitch that can be remotely engaged through a terminal sequence, based on the system developed to kill gpt3 during it's classification stages. I now have a lot of what I need including an input output user interface connected to the algorithm. I now just need to write the cargo load deployment sequence and then build a polymorphic algorithm viral infection that dies after intrusion to ensure deployment. Then I need to build an isolated environment to run the former and a safe sample of something similar but less vicious than black mamba and then see if it goes for it. Report and develop as appropriate. Sure this is a basic concept to make it target a high profile target on a specific piece of equipment and then make it continue guessing numbers infinitely in a loop and engage a Killswitch algorithm and switch off the machine But I didn't see anyone else do better. I'm open to ideas for remnant discovery, quarantining and deletion in networks and systems so data isn't archived and can then reformulate itself and attack from within archives. I'm going to have to pair this with an intrusion detection system, an anti-malware and firewall system. Going of off future development initiative VMWARES Carbon black, Palo Alto OT security and INCYDE should all be compatible. With this feature I build.

Ai binary level analysis with Rev eng Important considering the latest breaches Malware detection is important on an ongoing basis #ai #reveng

[HTML] An Information Security Engineering Framework for Modeling Packet Filtering Firewall Using Neutrosophic Petri Nets JK Madhloom, ZH Noori, SK Ebis, OA Hassen… - Computers, 2023 … The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area … function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. I

I’ve conducted evaluations on vulnerability scans and produced pristine reports with Generative A.I., and it is very efficient. I did a rough comparison of the time it took for me to manually do the same evaluation and reporting, and though I don’t have exact numbers on hand right now, I can confidently say it took significantly more time to do it manually. Always remember to use A.I. responsibly. Changes made: - "Generative A.I." instead of "the Generative A.I" since it's referred to as a category, not a specific instance. - Added a comma after "reporting" for clarity. - Changed "three number" to "exact numbers" for correctness. - Added periods after "A.I." to abbreviate "artificial intelligence." - Added a period at the end for completeness.

This paper introduces and evaluates three topological-based data analysis (TDA) techniques, to efficiently analyze and detect complex malware signature. TDA is known for its robustness under noise and with imbalanced datasets. The paper first introduces the basic formulation of metric spaces, simplicial complexes, and homology before describing the 3 techniques for analysis. TDA mapper Persistence homology Topological model analysis tool These TDA techniques are compared to traditional features engineering (unsupervised) algorithms such as PCA or t-SNE using a False Positive Rate and a Detection Rate. The features are then used as input to various traditional ML classifiers (SVM, Logistic Regression, Random Forest, Gradient Boosting,). The paper also covers CPU and memory usage analysis. Conclusion: TDA mapper with PCA for feature extraction for extracting malware clusters as well as t-SNE to identify overlapping malware characteristics. As far as detection rate in supervised classification, Random Forest, XGBoost and GBM achieve a detection rate close to 100%