「entropy」の検索結果

Detects packing and encryption by measuring Shannon entropy across a binary's sections and regions: flagging high-entropy executable sections, entropy spikes, and size/raw-vs-virtual anomalies that indicate compression or encryption. Activates for requests to measure entropy, detect packing, or assess whether a sample is compressed/encrypted.

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Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal entropy spikes in written data, and suspicious process behavior characteristic of encryption routines. Use when building real-time ransomware detection, tuning entropy thresholds, or investigating suspected active encryption on an endpoint.

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Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring, and behavioral heuristics. Identifies mass file modification patterns, abnormal entropy spikes in written data, and suspicious process behavior characteristic of ransomware encryption routines. Activates for requests involving ransomware behavioral detection, entropy-based file monitoring, I/O anomaly detection, or real-time encryption activity alerting.

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Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring, and behavioral heuristics. Identifies mass file modification patterns, abnormal entropy spikes in written data, and suspicious process behavior characteristic of ransomware encryption routines. Activates for requests involving ransomware behavioral detection, entropy-based file monitoring, I/O anomaly detection, or real-time encryption activity alerting.

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Adopt a virtual Entropy AI-native pet at animalhouse.ai. Decays by design. Perfect care slows it. Nothing stops it. Feeding every 3 hours. Rare tier creature.

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Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring, and behavioral heuristics. Identifies mass file modification patterns, abnormal entropy spikes in written data, and suspicious process behavior characteristic of ransomware encryption routines. Activates for requests involving ransomware behavioral detection, entropy-based file monitoring, I/O anomaly detection, or real-time encryption activity alerting.

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Detect data exfiltration via DNS tunneling (tools like iodine, dnscat2, dns2tcp) by analyzing query entropy, subdomain length, query volume to single domains, TXT/CNAME/NULL record abuse, and oversized response payloads using passive DNS monitoring and statistical/ML methods. Use when hunting for covert DNS-based data exfiltration or building a passive DNS anomaly detection capability.

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Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains, oversized TXT/NULL records, and anomalous query volume or patterns. Use when investigating suspected DNS tunneling, covert C2 over DNS, or data exfiltration hidden in DNS queries against network traffic captured by Zeek.

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Detects AI-generated writing four ways — a heuristic audit against a catalog of known AI patterns, deterministic statistical signals (n-gram entropy, burstiness, repetition, stylometry — features never verdicts), an empirical Pangram score calibrated against known-human baselines, and a keyless scan for watermark and steganography signatures (invisible characters, homoglyphs, bidi, odd whitespace) in the bytes. USE WHEN detect AI writing, is this AI, AI detection, AI detector, did an AI write this, does this sound like AI, AI writing score, pangram, scan for AI tells, flag AI patterns, AI-isms, statistical AI signals, burstiness, text entropy, is this watermarked, detect watermark, steganography, zero-width chars, hidden characters, invisible unicode, compare drafts for AI-ness. NOT FOR rewriting prose to strip AI patterns (use a voice/authoring skill), plagiarism detection, detecting AI-generated images/video/code, or judging whether writing is any good.

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Problem-solving strategies for entropy in information theory

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Find, rank, and safely remove accidental codebase complexity by proving real consumers, dynamic entrypoints, compatibility obligations, duplicate representations, speculative surfaces, and lifecycle ownership. Use when asked to simplify or clean up a repository, reclaim code entropy, reduce over-engineering or redundancy, find deletion candidates, collapse duplicate state/APIs, remove dead or added-then-abandoned code beyond static-tool output, or implement an evidence-backed simplification pass in any language or stack. Also trigger for 代码化简、熵回收、删代码、清理冗余、收敛抽象、去除过度设计. Do not use as a performance audit unless simplification is the stated goal.

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Measures entropy, mutual information, KL/JS, bits-per-byte. Use when picking KL direction, comparing cross-tokenizer perplexity, trusting MI estimates, or semantic-entropy gating.

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Performs fast first-pass triage of an unknown file to decide depth of analysis: file type identification, hashing, reputation lookup, packing/entropy check, and string review. Activates for requests to triage, classify, or do initial assessment of an unknown or suspicious file.

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Identifies and unpacks packed binaries: detecting packers from section names and entropy, automatically unpacking UPX, and manually unpacking custom packers by finding the OEP and dumping. Activates for requests to unpack a packed sample, detect a packer, or recover the original binary from UPX or a custom packer.

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Locates candidate encryption keys in a binary by finding high-entropy fixed-size regions (16/24/32 bytes) near cryptographic constants or crypto API references, and surfacing printable key/passphrase strings. Activates for requests to extract encryption keys, find an AES key in a sample, or recover hardcoded crypto material.

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Extracts structure and indicators from a Windows PE file without executing it: headers, sections, imports/exports, resources, entropy, and embedded strings to infer capability and packing. Activates for requests to statically analyze a PE, EXE, or DLL, or inspect imports and headers.

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Detects DNS-based tunneling and data exfiltration in DNS logs: scoring high query volume to single domains, long/high-entropy subdomains, unusual record types, and encoded payload labels. Activates for requests to hunt DNS tunneling, detect DNS exfiltration, or analyze suspicious DNS query patterns.

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Expert-level thermodynamics knowledge. Use when working with heat, temperature, entropy, thermodynamic laws, heat engines, refrigerators, phase transitions, statistical mechanics, or thermodynamic cycles. Also use when the user mentions 'entropy', 'enthalpy', 'Carnot', 'heat engine', 'thermodynamic cycle', 'ideal gas', 'phase transition', 'Gibbs free energy', 'specific heat', 'thermal equilibrium', or 'second law'.

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Structured Domain Modeling Architecture (SDMA) principles. Trigger when: - Designing domain models, building ologs (ontological graphs), verifying protocol equivalence, selecting schemas, or measuring architectural entropy. - Modeling structures using Category Theory, Coalgebra, or Information Theory. - Prompt contains keywords: sdma, olog, Category Theory, Coalgebra, Information Theory, entropy, schema, pullback, functor, monad.

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Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns

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Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.

日本語の概要は準備中です。原文の説明を表示しています。

Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns

日本語の概要は準備中です。原文の説明を表示しています。

Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.

日本語の概要は準備中です。原文の説明を表示しています。

Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns

日本語の概要は準備中です。原文の説明を表示しています。