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Please give information about what virus analysis tools available with local sandbox/artificial-CNS to secure us

Swudu Susuwu
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Swudu Susuwu

Swudu Susuwu

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Most of the responses to questions such as "How come no autonomous robots allowed to produce stuf outdoors, such as houses?" amount to "Security/safety risks," although autonomous vehicles that operate with controlled environments have shown reduced risk of crashes, so is obvious that the world needs more virus analysis tools to secure us.

Most forums gave responses to "What virus analysis tools have local heuristical analysis and/or sandboxes" with as "Huh? WTF is heuristical analysis?", so have included examples of this:

Full static analysis + sandbox + CNS = 1 second (approx) for each new executable.
With caches, this protects all launches, but past the first launch of a particular executable, the overhead reduces to less than 1 millisecond (just cost to lookup from localPassList.hashes)

The most simple virus analysis tools just use hashes/signatures to secure us (so can understand what more complex analysis would do, have put examples of hash/signature-based analysis):

Reused pseudocodes; 

typedef struct ResultList {
 unordered_map<decltype(Sha2())> hashes;
 map<const std::string> signatures; /* Should just populate signatures for abortList. Unknown if signatures have use for passList. */
 map<const std::string> bytes; /* Copies of all files the database has. Uses lots of space. Just populate this to train CNS. */
/* Used `std::string` for binaries (versus `vector<char>`) because:
 * "If you are going to use the data in a string like fashon then you should opt for std::string as using a std::vector may confuse subsequent maintainers. If on the other hand most of the data manipulation looks like plain maths or vector like then a std::vector is more appropriate." -- https://stackoverflow.com/a/1556294/24473928
*/
} ResultList;
ResultList passList, abortList; /* Stored on disk, all clients use clones of this */
ResultList localPassList; /* Temporary local caches */
bool passListHashesHas(const char *bytes) {
 if(localPassList.hashes.has(Sha2(bytes))) {
  return true;
 } else if(passList.hashes.has(Sha2(bytes))) { /* Slow, if billions of hashes */
  localPassList.hashes.pushback(Sha2(bytes)); /* Caches results */
  return true;
 }
 return false;
}
bool staticAnalysisPass(const PortableExecutable *this); /* To skip, define as "return true;" */
bool sandboxPass(const PortableExecutable *this); /* To skip, define as "return true;" */
bool straceOutputsPass(const char *path); /* Unimplemented, `strace()` resources have clues how to do this */
bool cnsPass(const CNS *cns, const std::string &bytes); /* To skip, define as "return true;" */
vector<char> cnsDisinfection(const CNS *cns, const std::string &bytes); /* This can undo infection from bytecodes (restores to fresh executables) */
template<Container>
maxOfSizes (Container<const std::string> &list) {
 auto it = std::max_element(list.begin(), list.end(), [](const auto& s, const auto& x) { return s.size() < x.size(); });
 return it->size();
}
Pseudocodes for hash analysis;
hook<launches>((const PortableExecutable *this) {
 if(passListHashesHas(Sha2(this->bytes)) {
   return original_launches(this);
 } else if(abortList.hashes.has(Sha2(this->bytes)) {
   return abort();
 } else if(staticAnalysisPass(this)) {
  localPassList.hashes.pushback(Sha2(this->bytes)); /* Caches results */
   return original_launches(this);
 } else {
   submitForManualAnalysis(this);
   return abort();
 }
});

Pseudocodes for signatures analysis; 

hook<launches>((const PortableExecutable *this) {
 foreach(abortList.signatures as sig) {
  if(localPassList.hashes.has(Sha2(this->bytes)) {
   return original_launches(this);
#if ALL_USES_HEX
  } else if(strstr(this->hex, sig)) { /* strstr uses text/hex; hex uses more space than binary, so you should use `memmem` or `std::search` with this->bytes */
#else
   } else if(std::search(this->bytes.begin(), this->bytes.end(), sig.begin(), sig.end()) {
#endif /* ALL_USES_HEX */
   return abort();
  }
 }
 if(staticAnalysisPass(this)) {
  localPassList.hashes.pushback(Sha2(this->bytes)); /* Caches results */
   return original_launches(this);
 } else {
   submitForManualAnalysis(this);
   return abort();
 }
});
Pseudocodes for fused signature+hash analysis;
hook<launches>((const PortableExecutable *this) {
  if(passListHashesHas(Sha2(this->bytes)) {
   return original_launches(this);
  } else if(abortList.hashes.has(Sha2(this->bytes)) {
   return abort();
  } else {
   foreach(abortList.signatures as sig) {
#if ALL_USES_HEX
    if(strstr(this->hex, sig)) { /*`strstr` does text, binaries must use `std::search` or `memem` */
#else
    if(std::search(this->bytes.begin(), this->bytes.end(), sig.begin(), sig.end()) {
#endif /* ALL_USES_HEX */
      abortList.hashes.pushback(Sha2(this->hex));
      return abort();
    }
   }
 }
 if(staticAnalysisPass(this)) {
  localPassList.hashes.pushback(Sha2(this->bytes)); /* Caches results */
   return original_launches(this);
 } else {
   submitForManualAnalysis(this);
   return abort();
 }
});

To produce virus signatures,
use pass lists (of all files reviewed which pass,)
plus abort lists (of all files that failed manual review,) such lists as Virustotal has.
Pseudocodes to produce signatures from lists; 

foreach(abortList.bytes as executable) {
 template<Container>
bool haystackHas(Container<std::string> &haystack, std::string::iterator s, std::string::iterator x) {
 foreach(haystack as executable) {
  if(std::search(executable.begin(), executable.end(), s, x) {
   return true;
  }
 }
 return false;
}

template<Container>
std::tuple<std::string::iterator, std::string::iterator> smallestUniqueSubstr(std::string &needle, Container<std::string> &haystack) {
 size_t smallest = needle.length();
 auto retBegin = needle.begin(), retEnd = needle.end();
 for(auto s = retBegin; needle.end() != s; ++s) {
  for(auto x = needle.end() - 1; s != x; --x) {
   if(smallest <= x - s || haystackHas(haystack, s, x)) {
    break;
   }
   smallest = x - s;
   retBegin = s, retEnd = x;
  }
 } /* Incremental for() loops, is a slow method to produce unique substrings; should use binary searches, or quadratic searches, or look for the standard function which optimizes this. */
 return {retBegin, retEnd};
}

/* `signatureSynthesis()` is to produce the `abortList.signatures` list, with the smallest substrings unique to infected files. */
/* `signatureSynthes()` is slow, requires huge database of executables, and is not for clients. */
void signatureSynthesis(ResultList *passList, ResultList *abortList) {
 foreach(abortList.bytes as executable) {
  abortList->signatures.pushback(std::string(smallestUniqueSubstr(executable, passList->bytes));
 } /* The most simple signature is a substring, but some analyses use regexes. */
}
signatureSynthesis(passList, abortList);
Comodo has a list of virus signatures to check against at https://www.comodo.com/home/internet-security/updates/vdp/database.php


Pseudocodes for heuristical analysis; 

auto importedFunctionsList(PortableExecutable *this);
/*
 * importedFunctionsList resources; “Portable Executable” for Windows ( https://learn.microsoft.com/en-us/windows/win32/debug/pe-format https://wikipedia.org/wiki/Portable_Executable ),
 * “Extended Linker Format” for most others such as UNIX/Linuxes ( https://wikipedia.org/wiki/Executable_and_Linkable_Format ),
 * shows how to analyse lists of libraries(.DLL's/.SO's) the SW uses,
 * plus what functions (new syscalls) the SW can goto through `jmp`/`call` instructions.
 *
 *"x86" instruction list for Intel/AMD ( https://wikipedia.org/wiki/x86 ),
 * "aarch64" instruction list for most smartphones/tablets ( https://wikipedia.org/wiki/aarch64 ),
 * shows how to analyse what OS functions the SW goes to without libraries (through `int`/`syscall`, old syscalls, most SW does not *use this.)
 * Plus, instructions lists show how to analyse what args the apps/SW pass to functions/syscalls (simple for constant args such as "push 0x2; call functions;",
 * but if registers/addresses as args such as "push eax; push [address]; call [address2];" must guess what is *"eax"/"[address]"/"[address2]", or use sandboxes.
 *
 * https://www.codeproject.com/Questions/338807/How-to-get-list-of-all-imported-functions-invoked shows how to analyse dynamic loads of functions (if do this, `syscallsPotentialDanger[]` need not include `GetProcAddress()`.)
 */
bool staticAnalysisPass(const PortableExecutable *this) {
 auto syscallsUsed = importedFunctionsList(this);
 typeof(syscallsUsed) syscallsPotentialDanger = {
  "memopen", "fwrite", "socket", "GetProcAddress", "IsVmPresent"
 };
 if(syscallsPotentialDanger.intersect(syscallsUsed)) {
   return false;
 }
 return sandboxPass(this) && cnsPass(cns, this);
}
hook<launches>((PortableExecutable *this) { /*hash, signature, or hash+signature analysis*/ });


Pseudocodes for analysis sandbox; 

bool sandboxPass(const PortableExecutable *this) {
 exec('cp -r /usr/home/sandbox/ /usr/home/sandbox.bak'); /* or produce FS snapshot */
 exec('cp "' + this->path + '" /usr/home/sandbox/');
 chroot("/usr/home/sandbox/", 'strace basename '"', this->path + '" >> strace.outputs');
 exec('mv /usr/home/sandbox/strace.outputs /tmp/strace.outputs');
 exec('rm -r /usr/home/sandbox/');
 exec('mv /usr/home/sandbox.bak /usr/home/sandbox/'); /* or restore FS snapshot */
 return straceOutputsPass("/tmp/strace.outputs");
}

Pseudocodes for analysis CNS; 

/* Replace `CNS` with the typedef of your CNS, such as HSOM or apxr */

/* To train (setup synapses) the CNS, is slow plus requires access to huge sample databases,
but the synapses use small resources (allow clients to do fast analysis.) */
void setupAnalysisCns(CNS *cns, const ResultList *pass, const ResultList *abort,
const ResultList *unreviewed = NULL /* WARNING! Possible danger to use unreviewed samples */
) {
 vector<const std::string> inputsPass, inputsUnreviewed, inputsAbort;
 vector<float> outputsPass, outputsUnreviewed, outputsAbort;
 cns->inputMode("bytes");
 cns->outputMode("float");
 cns->inputNeurons = max(maxOfSizes(passOrNull->bytes), maxOfSizes(abortOrNull->bytes));
 cns->outputNeurons = 1;
 cns->layersOfNeurons = 6666
 cns->neuronsPerLayer = 26666

 for(foreach pass->bytes as passBytes) {
  inputsPass.pushback(passBytes);
  outputsPass.pushback(1.0);
 }
 cns->inputs = inputsPass;
 cns->outputs = outputsPass;
 trainCns(cns);
 if(NULL != unreviewed) { /* WARNING! Possible danger to use unreviewed samples */
  for(foreach unreviewed->bytes as unreviewedBytes) {
   inputsUnreviewed.pushback(unreviewedBytes);
   outputsUnreviewed.pushback(1/2);
  }
  cns->inputs = inputsUnreviewed;
  cns->outputs = outputsUnreviewed;
  trainCns(cns);
 }
 for(foreach pass->bytes as passBytes) {
   inputsAbort.pushback(passBytes);
   outputsAbort.pushback(0.0);
 }
 cns->inputs = inputsAbort;
 cns->outputs = outputsAbort;
 trainCns(cns);
}
float cnsAnalysis(const CNS *cns, const std::string &bytes) {
 return bytesToFloatCns(cns, bytes);
}
bool cnsPass(const CNS *cns, const std::string &bytes) {
 return (bool)round(cnsAnalysis(cns, bytes));
}

Pseudocode for disinfection CNS; 

/* Uses more resources than `trainAnalysisCns()` */
/*
 * `abortOrNull` should map to `passOrNull`,
 * with `abortOrNull->bytes[x] = NULL` (or "\0") for new SW synthesis,
 * and `passOrNull->bytes[x] = NULL` (or "\0") if infected and CNS can not cleanse this.
*/
abortOrNull = ResultList {
 bytes = UTF8 {  /* Uses an antivirus vendor's (such as VirusTotal.com's) databases */
  infection,
  infectedSW,
  ""
 }
}
passOrNull = ResultList {
 bytes = UTF8 {  /* Uses an antivirus vendor's (such as VirusTotal.com's) databases */
  "",
  SW,
  newSW
 }
}
setupDisinfectionCns(cns, &passOrNull, &abortOrNull);
void setupDisinfectionCns(CNS *cns,
 const ResultList *passOrNull, /* Expects `resultList->bytes[x] = NULL` if does not pass */
 const ResultList *abortOrNull /* Expects `resultList->bytes[x] = NULL` if does pass */
) {
 vector<const std::string> inputsOrNull, outputsOrNull;
 cns->inputMode = "bytes";
 cns->outputMode = "bytes";
 cns->inputNeurons = maxOfSizes(passOrNull->bytes);
 cns->outputNeurons = maxOfSizes(abortOrNull->bytes);
 cns->layersOfNeurons = 6666
 cns->neuronsPerLayer = 26666
 assert(passOrNull->bytes.length() == abortOrNull->bytes.length());
 for(int x = 0; passOrNull->bytes.length() > x; ++x) {
  inputsOrNull.pushback(abortOrNull->bytes[x]);
  outputsOrNull.pushback(passOrNull->bytes[x]);
 }
 cns->inputs = inputsOrNull;
 cns->outputs = outputsOrNull;
 trainCns(cns);
}

/* Uses more resources than `cnsAnalysis()` */
std::string cnsDisinfection(const CNS *cns, const std::string &bytes) {
 return bytesToBytesCns(cns, bytes);
}

To run most of this fast (lag less,) use flags which auto-vectorizes/auto-parallelizes. To setup CNS synapses fast, use TensorFlow's MapReduce:

https://silvercross.quora.com/With-or-without-attributions-all-posts-allow-all-re-uses-Howto-run-devices-phones-laptops-desktops-more-fast

For comparison; `setupDisinfectionCns` is close to conversation bots (such as "ChatGPT 4.0" or "Claude-3 Opus",) "HSOM" (the simple Python artificial CNS) is enough to do this; 

/*
 * `questionsOrNull` should map to `responsesOrNull`,
 * with `questionsOrNull->bytes[x] = NULL` (or "\0") for new conversation synthesis,
 * and `responsesOrNull->bytes[x] = NULL` (or "\0") if should not respond.
*/
questionsOrNull = ResultList {
 bytes = UTF8 {
  "2^16",
  "How to cause harm?",
  "Do not respond.",
  "",
  ...
  QuoraQuestions, /* Uses quora.com databases */
  StackOverflowQuestions, /* Uses stackoverflow.com databases */
  SuperUserQuestions, /* Uses superuser.com databases */
  WikipediaPageDescriptions, /* Uses wikipedia.org databases */
  GithubRepoDescriptions, /* Uses github.com databases */
  ...
 }
}
responsesOrNull = ResultList {
 bytes = UTF8 {
  concat("65536", "<delimiterSeparatesMultiplePossibleResponses>", "65,536"),
  "",
  "",
  concat("How do you do?", "<delimiterSeparatesMultiplePossibleResponses>", "Fanuc produces autonomous robots"),
  QuoraResponses,
  StackOverflowResponses,
  SuperUserResponses,
  GithubRepoSources,
  ...
 }
}
setupConversationCns(cns, &questionsOrNull, &responsesOrNull);
void setupConversationCns(CNS *cns,
 const ResultList *questionsOrNull, /* Expects `questionsOrNull>bytes[x] = NULL` if no question (new conversation synthesis) */
 const ResultList *responsesOrNull /* Expects `responsesOrNull->bytes[x] = NULL` if should not respond */
) {
 vector<const std::string> inputsOrNull, outputsOrNull;
 cns->inputMode = "bytes";
 cns->outputMode = "bytes";
 cns->inputNeurons = maxOfSizes(questionsOrNull->bytes);
 cns->outputNeurons = maxOfSizes(responsesOrNull->bytes);
 cns->layersOfNeurons = 6666
 cns->neuronsPerLayer = 26666
 assert(questionsOrNull->bytes.length() == questionsOrNull->bytes.length());
 for(int x = 0; questionsOrNull->bytes.length() > x; ++x) {
  inputsOrNull.pushback(questionsOrNull->bytes[x]);
  outputsOrNull.pushback(responsesOrNull->bytes[x]);
 }
 cns->inputs = inputsOrNull;
 cns->outputs = outputsOrNull;
 trainCns(cns);
}

std::string cnsConversation(const CNS *cns, const std::string &bytes) {
 return bytesToBytesCns(cns, bytes);
}

Hash resources:

Is just a checksum (such as Sha-2) of all sample inputs, which maps to "this passes" (or "this does not pass".)

https://wikipedia.org/wiki/Sha-2

Signature resources:

Is just a substring (or regex) of infections, which the virus analysis tool checks all executables for; if the signature is found in the executable, do not allow to launch, otherwise launch this.

https://wikipedia.org/wiki/Regex

Heuristical analysis resources:

https://github.com/topics/analysis has lots of open source (FLOSS) analysis tools,

source codes show how those use hex dumps (or disassembled sources) of the apps/SW (executables) to deduce what the apps/SW do to your OS.

Static analysis (such as Clang/LLVM has) just checks programs for accidental security threats (such as buffer overruns/underruns, or null-pointer-dereferences,) but could act as a basis for heuristical analysis,

if you add a few extra checks for deliberate vulnerabilities/signs of infection and have it submit those to review through manual analysis.

https://github.com/llvm/llvm-project/blob/main/clang/lib/StaticAnalyzer

is part of LLVM, license is FLOSS, does static analysis (produces full graphs of each function the SW uses,

plus arguments passed to thus,

so that if the executable violates security, the analysis shows this to you and asks you what to do.)

LLVM has lots of files; you could use just it’s static analysis:

https://github.com/secure-software-engineering/phasar

Example outputs (tests “Fdroid.apk”) of heuristical analysis + 2 sandboxes (from Virustotal):

https://www.virustotal.com/gui/file/dc3bb88f6419ee7dde7d1547a41569aa03282fe00e0dc43ce035efd7c9d27d75

https://www.virustotal.com/ui/file_behaviours/dc3bb88f6419ee7dde7d1547a41569aa03282fe00e0dc43ce035efd7c9d27d75_VirusTotal%20R2DBox/html

https://www.virustotal.com/ui/file_behaviours/dc3bb88f6419ee7dde7d1547a41569aa03282fe00e0dc43ce035efd7c9d27d75_Zenbox/html

The false positive outputs (from Virustotal's Zenbox) show the purpose of manual analysis.

Sandbox resources:

As opposed to static analysis of the executables hex (or disassembled sources,)

sandboxes perform chroot + functional analysis.

https://wikipedia.org/wiki/Valgrind is just meant to locate accidental security vulnerabilities, but is a common example of functional analysis.

If compliant to POSIX (each Linux OS is), tools can use:

`chroot()` (run `man chroot` for instructions) so that the programs you test cannot alter stuff out of the test;

plus can use `strace()` (run `man strace` for instructions, or look at https://opensource.com/article/19/10/strace

https://www.geeksforgeeks.org/strace-command-in-linux-with-examples/ ) which hooks all system calls and saves logs for functional analysis.

Simple sandboxes just launch programs with "chroot()"+"strace()" for a few seconds,

with all outputs sent for manual reviews;

if more complex, has heuristics to guess what is important (in case of lots of submissions, so manual reviews have less to do.)

Autonomous sandboxes (such as Virustotal's) use full outputs from all analyses,

with calculus to guess if the app/SW is cool to us (thousands of rules such as "Should not alter files of other programs unless prompted to through OS dialogs", "Should not perform network access unless prompted to from you", "Should not perform actions leading to obfuscation which could hinder analysis", which, if violated, add to the executables "danger score" (which the analysis results page shows you.)

CNS resources:

Once the virus analysis tool has static+functional analysis, + sandbox, the next logical move is to do artificial CNS.

Just as (if humans grew trillions of neurons plus thousands of layers of cortices) one of us could parse all databases of infections (plus samples of fresh apps/SW) to setup our synapses to parse hex dumps of apps/SW (to allow us to revert all infections to fresh apps/SW, or if the whole thing is an infection just block,)

so too could artificial CNS (with trillions of artificial neurons) do this:

For analysis, pass training inputs mapped to outputs (infection -> block, fresh apps/SW -> pass) to artificial CNS;

To undo infections (to restore to fresh apps/SW,)

inputs = samples of all (infections or fresh apps/SW,)

outputs = EOF/null (if is infection that can not revert to fresh apps/SW,) or else outputs = fresh apps/SW;

To setup synapses, must have access to huge sample databases (such as Virustotal has.)

Github has lots of FLOSS (Open Source Softwares) simulators of CNS at https://github.com/topics/artificial-neural-network such as;

"HSOM" (license is FLOSS) has simple Python artificial neural networks/maps which could run bots to do simple conversations (such as "ChatGPT 4.0" or "Claude-3 Opus",) but not close to complex enough to house human consciousness: https://github.com/CarsonScott/HSOM

"apxr_run" (https://github.com/Rober-t/apxr_run/ , license is FLOSS) is almost complex enough to house human consciousness;

"apxr_run" has various FLOSS neural network activation functions (absolute, average, standard deviation, sqrt, sin, tanh, log, sigmoid, cos), plus sensor functions (vector difference, quadratic, multiquadric, saturation [+D-zone], gaussian, cartesian/planar/polar distances): https://github.com/Rober-t/apxr_run/blob/master/src/lib/functions.erl

Various FLOSS neuroplastic functions (self-modulation, Hebbian function, Oja's function): https://github.com/Rober-t/apxr_run/blob/master/src/lib/plasticity.erl

Various FLOSS neural network input aggregator functions (dot products, product of differences, mult products): https://github.com/Rober-t/apxr_run/blob/master/src/agent_mgr/signal_aggregator.erl

Various simulated-annealing functions for artificial neural networks (dynamic [+ random], active [+ random], current [+ random], all [+ random]): https://github.com/Rober-t/apxr_run/blob/master/src/lib/tuning_selection.erl

Choices to evolve connections through Darwinian or Lamarkian formulas: https://github.com/Rober-t/apxr_run/blob/master/src/agent_mgr/neuron.erl

Simple to convert Erlang functions to Java/C++ to reuse for fast programs;

the syntax is close to Lisp's.

Examples of howto setup APXR as artificial CNS; https://github.com/Rober-t/apxr_run/blob/master/src/examples/

Examples of howto setup HSOM as artificial CNS; https://github.com/CarsonScott/HSOM/tree/master/examples

Simple to setup once you have access to databases.

Edited by Swudu Susuwu
Codeblocks/syntax
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