Channel
@OxxxSec
On this record: Topic · Growth · Engagement · Reactions · Posts · Cite this entry
139subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001941135818 |
|---|---|
| Type | Channel |
| Username | @OxxxSec |
| Created | Between 1 April 2023 and 31 October 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 November 2026 |
| Measurements held | 9 |
| Confirmed unchanged | 1 time, most recently 8 November 2026 |
| On Telegram | t.me/OxxxSec |
Hacking & security — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 25 September 2026 and assigned it the closest of 31 fixed categories, at 78% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Nov 2026, 16:05 | 139 | -1 |
| 19 Oct 2026, 07:48 | 140 | +1 |
| 12 Oct 2026, 14:17 | 139 | -1 |
| 18 Sept 2026, 15:00 | 140 | +2 |
| 10 Sept 2026, 01:40 | 138 | -1 |
| 16 Aug 2026, 14:26 | 139 | -1 |
| 8 Aug 2026, 22:31 | 140 | no change |
| 8 Aug 2026, 04:16 | 140 | +1 |
| 7 Aug 2026, 16:18 | 139 | first reading |
20 posts held, back to 30 June 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 5 August 2026. An engagement rate over an empty window would be a number about nothing.
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1 | 100.0% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 2 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 30 June 2026 to 5 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
⚡️🔬 Side-Channel Analysis on Emulated Traces with Donjon Lascar & Rainbow (Python practice) Hardware side-channel analysis (SCA) typically requires physical devices, oscilloscopes, and complex lab setups. But what if you could evaluate SCA resistance before the silicon is even taped out, or safely test vulnerabilities without touching real hardware? Ledger Donjon’s Lascar combined with the Rainbow emulator allows yo…
🧠⚡️Machine Learning in Donjon Lascar: Training Neural Networks with ProfileEngine (Python practice) Classical statistical attacks remain powerful, but deep learning has become a dominant force in modern side-channel analysis. Ledger Donjon’s Lascar integrates seamlessly with Keras, allowing you to train high-performance neural networks (MLP or CNN) directly on real datasets such as ASCAD. Using ProfileEngine, you ca…
🚀🔬 T-Test Analysis in Donjon Lascar: Leakage Detection with TTestEngine (Python practice) Welch’s t-test is one of the most reliable and widely used methods for side-channel leakage detection. Ledger Donjon’s Lascar makes it straightforward with TTestEngine, which partitions traces into two classes and computes the statistical difference of means at every sample point. This allows you to quickly reveal whether a spe…
📺 🔊Signal-to-Noise Analysis in Donjon Lascar: Characterising Leakage with SnrEngine (Python practice) Before launching a full attack, you need to understand where and how strongly a device leaks. Ledger Donjon’s Lascar provides a clean and efficient way to perform side-channel characterisation using SnrEngine (or the closely related NicvEngine). These engines partition the traces according to a chosen intermediate v…
👌 🌼Profiled Attacks in Donjon Lascar: Building Classifiers with ProfileEngine & MatchEngine (Python practice) When non-profiled attacks like CPA or LRA hit their limits, profiled side-channel analysis becomes the next powerful step. Ledger Donjon’s Lascar framework makes this clean and practical: first train a classifier (sklearn or Keras) on a device with known keys using ProfileEngine, then apply it to a target wi…
🌟📈 Linear Regression Analysis in Donjon Lascar: Cracking Non-Linear Leakages with LRA (Python practice) Correlation Power Analysis (CPA) is the go-to for side-channel attacks, but it relies heavily on an accurate leakage model (like Hamming weight). What happens when the physical leakage is non-linear, unknown, or doesn't fit standard models? Enter LraEngine in Ledger Donjon’s Lascar framework. Linear Regression Ana…
💻⬆️ Second-Order CPA in Donjon Lascar: Breaking Masked Implementations (Python practice) When the target uses masking, first-order CPA fails. Lascar makes second-order attacks practical with clean leakage processing and classic engines. By recombining carefully chosen time samples (via CenteredProductProcessing) you can recover the Hamming weight of the unmasked intermediate and mount a successful CPA against protec…
⚡️🦾 Partitioned Engine in Donjon Lascar: Faster CPA for Optimized Side-Channel Attacks (Python practice) Classical CPA is powerful, but when your leakage model and intermediate values allow smart partitioning, CpaPartitionedEngine in Lascar can dramatically speed things up. Ledger Donjon’s framework gives you this advanced tool to compute correlation attacks more efficiently — especially useful for 8-bit intermediat…
🔥🤑 Introduction to Side-Channel Analysis with Donjon Lascar (Python practice) Getting started with side-channel analysis doesn’t have to be overwhelming. Ledger Donjon’s Lascar is a fast, versatile, and open-source Python library that makes classical and profiled attacks accessible — whether you’re a seasoned hardware security researcher or just exploring power/EM leakage on real targets like AES implementations on …
🌟🧠 Model Optimization in SCAAML: Boosting Deep Learning Side-Channel Attacks (Python practice) While solid training gets you started, true performance in SCAAML comes from smart model optimization. Google’s SCAAML framework provides powerful TensorFlow 2.x tools and patterns to squeeze maximum accuracy and efficiency out of your side-channel models — critical for faster convergence, lower resource usage, and better …
🔥 Use Cases - ChipWhisperer → SCAAML: Convert your TRS/CWP/NPY traces, train on custom attack points/bytes, and iterate fast on Jetson Orin or multi-GPU rigs. - Benchmarking & Research: Run systematic experiments across leakage models, trace lengths, and preprocessing. - Production-Grade Attacks: Scale to large datasets with sharded loading and automatic best-model selection for real key recovery. ⚠️ Caveats & Respo…
🚀🐱 Training Models in SCAAML: Practical Deep Learning for Side-Channel Key Recovery (Python Deep Dive) While many SCA tools stop at data collection, SCAAML (Side Channel Attacks Assisted with Machine Learning) from Google shines when it comes to model training. Its clean TensorFlow 2.x pipelines let you go from raw traces to high-performance models optimized for real-world power/EM leakage, with built-in support for…
Showing the 12 most recent of 20 posts we hold for @OxxxSec. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 8 November 2026 — this entry's latest reading, not the date you are reading this.
“OxxxSec” (@OxxxSec), 139 subscribers as measured 8 November 2026. Telegram Register, tgregister.com/channel/OxxxSec.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.