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Channel

Cybersecurity and Digital Forensics

@DigitalForensicAndCyberSecurity

On this record: Topic · Growth · Engagement · Reactions · Posts · Telegram's recommendations · Cite this entry

912subscribers

+6 since we began measuring on 1 September 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001365546890
TypeChannel
Username@DigitalForensicAndCyberSecurity
CreatedBetween 1 April 2018 and 31 August 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded1 September 2026
Last confirmed live18 September 2026
Measurements held4
Confirmed unchanged1 time, most recently 18 September 2026
On Telegramt.me/DigitalForensicAndCyberSecurity

Topic

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 19 September 2026 and assigned it the closest of 31 fixed categories, at 93% 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 why it has no browse page of its own yet.

Growth

905912908.51 September 2026 — 906 subscribers1 September 2026 — 906 subscribers11 September 2026 — 905 subscribers18 September 2026 — 912 subscribers1 September 202618 September 2026
4 measurements spanning 17 days, net +6. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 904–913 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
18 Sept 2026, 19:59912+7
11 Sept 2026, 00:57905-1
1 Sept 2026, 18:05906no change
1 Sept 2026, 12:30906first reading

Engagement

20 posts held, back to 20 December 2024the 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.

ERR · 30 days
7.24%
avg views ÷ 912 subscribers
Avg views / post
66.0
1 post measured
Reaction rate
this channel exposes no reaction counts
Posts in window
1
of 20 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 30 August 2026
Posts held20 (20 December 202430 August 2026)
Views total66
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken1 Sept 2026, 12:30 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Reaction mix

20 reactions across 11 posts, in 3 distinct kinds. The most used accounts for 65.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
1365.0%
👍525.0%
👎210.0%

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 11 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 20 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 20 December 2024 to 30 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.

Recent posts

30 Aug 2026, 17:18 UTC66 viewsread 1 September 2026

macOS Red Teaming. • Gathering System Information Using IOPlatformExpertDevice; • Targeting Browser and Diagnostic Logs; • Manipulating the TCC Database Using PackageKit; • Leveraging Application Bundles and User-Specific Data; • Taking Over Electron App TCC Permissions with electroniz3r; • Exploiting Keychain Access; • Signing Your Payload; • Exploiting Installer Packages; • Exploiting DMG Files for Distri

24 Apr 2026, 18:07 UTC924 views3 reactionsread 1 September 2026

Pentration Testing, Beginners To Expert! • Phase 1 – History; • Phase 2 – Web and Server Technology; • Phase 3 – Setting up the lab with BurpSuite and bWAPP; • Phase 4 – Mapping the application and attack surface; • Phase 5 – Understanding and exploiting OWASP top 10 vulnerabilities; • Phase 6 – Session management testing; • Phase 7 – Bypassing client-side controls; • Phase 8 – Attacking authentication/login

3

23 Nov 2025, 17:23 UTC≈1,660 views2 reactionsread 1 September 2026

🖥 X-OSINT This is an OSINT tool designed to gather useful, credible, and valid information about phone numbers, email addresses, IP addresses, and more features to come in future updates. Features of this tool include: 1. Host Lookup: Identifies and retrieves information about a host (server) on the network. 2. Port Scan: Detects open ports on a host and the services associated with them. 3. Subdomain Enumerat

1👍1

13 Sept 2025, 18:30 UTC≈1,490 views2 reactionsread 1 September 2026

🚨 New Malware Tactics Bypass Traditional Defenses If your SOC can’t afford to miss a single threat, this session is for you. 📍Cases & Detection Tips for SOCs In real time, ANY.RUN experts will: — Share insights into strengthening SOC expertise — Demonstrate and break down examples of evasion techniques — Offer detection strategies for ClickFix, phishing kits, and Living-Off-the-Land (LotL) attacks 👥 Who should

2

26 Jul 2025, 06:24 UTC≈1,410 views1 reactionsread 1 September 2026

macOS Red Teaming. • Gathering System Information Using IOPlatformExpertDevice; • Targeting Browser and Diagnostic Logs; • Manipulating the TCC Database Using PackageKit; • Leveraging Application Bundles and User-Specific Data; • Taking Over Electron App TCC Permissions with electroniz3r; • Exploiting Keychain Access; • Signing Your Payload; • Exploiting Installer Packages; • Exploiting DMG Files for Distri

1

7 May 2025, 17:42 UTC≈1,340 views2 reactionsread 1 September 2026

CVE-2024-22116: RCE in Zabbix, 9.9 rating 🔥 Lack of escaping for script parameters allows an attacker to execute arbitrary code. Dork: http.favicon.hash_sha256:22b06a141c425c92951056805f46691c4cd8e7547ed90b8836a282950d4b4be2 Vendor's advisory: https://support.zabbix.com/browse/ZBX-25016 #zabbix

2

22 Apr 2025, 17:42 UTC≈1,330 viewsread 1 September 2026

https://www.researchgate.net/profile/Subodh-Tiwari-4/stats/report/weekly/2025-04-20

26 Mar 2025, 15:05 UTC≈1,370 views1 reactionsread 1 September 2026

🔍 Linux Persistence Detection Engineering – Part 1 The first part of the Linux Persistence Detection Engineering series explores fundamental persistence techniques used by attackers to maintain unauthorized access to Linux systems. Understanding these mechanisms is crucial for developing effective detection and threat-hunting strategies. 🛠️ Key Persistence Techniques: 1️⃣ Cron Job & Systemd Service Abuse – Attackers

👍1

7 Mar 2025, 17:15 UTC≈1,260 viewsread 1 September 2026

🔥 Malware Datasets for Research & Analysis 🔥 📌 If you're looking for malware datasets for digital forensics, cybersecurity research, and machine learning, check out these resources: 1️⃣ BODMAS: Blue Hexagon Open Dataset 🔗 BODMAS Dataset 2️⃣ Malware Detection Dataset (Kaggle) 🔗 Malware Detection | Kaggle 3️⃣ Malware Memory Analysis Dataset (CIC) 🔗 Malware Memory Analysis Dataset 4️⃣ DikeDataset: Labeled Malicious & Be

4 Mar 2025, 17:30 UTC≈1,140 views1 reactionsread 1 September 2026

😈 Reverse Engineer's Toolkit A collection of tools for those interested in reverse engineering and malware analysis on x86 & x64 Windows systems. 🔗 GitHub: Reverse Engineer's Toolkit 📢 Join the Team: https://t.me/DigitalForensicAndCyberSecurity #Reverse #Malware #CyberSecurity

1

3 Mar 2025, 16:07 UTC≈1,020 viewsread 1 September 2026

👁 Uncle Spfus – Spoof MAC Address & Hostname A tool that automates MAC address and hostname spoofing. It uses macchanger but also verifies whether the MAC address remains spoofed after connecting to a network. This is crucial because some network configurations might reset the MAC to its original state. 🔗 GitHub: Uncle Spfus #CyberSecurity #MACSpoofing #Privacy

25 Feb 2025, 17:20 UTC959 viewsread 1 September 2026

🛡 Ultimate Recon & Threat Intelligence Toolkit 🛡 🔍 Enhance your Threat Intelligence, OSINT, and Pentesting with these powerful tools! 🚨 Threat Intelligence: 🔹 BinaryEdge | GreyNoise | FOFA 🔹 Zoomeye | LeakIX | URLScan 🔹 SOCRadar | Pulsedive 💻 Code Search: 🔹 Grep.app | SearchCode | PublicWWW 🌍 Server Search: 🔹 Shodan | Onyphe | Censys | IVRE 🔎 Attack Surface Discovery: 🔹 Netlas | FullHunt | BinaryEdge 🕵️ OSINT & Recon

Showing the 12 most recent of 20 posts we hold for @DigitalForensicAndCyberSecurity. 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.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Pycode Hubb
@pycode_hubb · 36,933
Telegram ranks this channel #28 of 58 here — alongside 57 others — read 1 September 2026
Jobs | Internship | Off Campus Placement
@Jobs_Internship_Campus_Placement · 31,748
Telegram ranks this channel #73 of 81 here — alongside 80 others — read 5 September 2026

This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

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 18 September 2026 — this entry's latest reading, not the date you are reading this.

“Cybersecurity and Digital Forensics” (@DigitalForensicAndCyberSecurity), 912 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/DigitalForensicAndCyberSecurity.

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.