Malware might have different behaviour under different condition (the if-else cases).
Malware might able to detect the virtual environment, thus hiding its intention.
Time consuming (has to execute to see the behaviour).
Complicated to extract dynamic features.
Possible to detect un-conceived types of malware attacks.
#MALWAREBYTES 3.1.2 PROBLEMS CODE#
Suffer from code obfuscation (fail to detect the polymorphic malwares).
Allows malicious files to be detected prior to execution (do not need to execute).
Emulators/Sandboxes: replicate the behavior of a system with higher accuracy, but require more resources.
Dynamic analysis: running samples (in a controlled and isolated environment) to examine their behavior.
Static analysis: examine the file without execution.
Two main approaches in feature selection of Malware: Some questions and answers regarding ML methods:
Output: the number of N different malware families cases.
Unlabeled data -> find common characteristics -> groups/clustersĭetection vs Classification Problem in Malware DetectionĬlassification problem in Malware Detection :.
Learn inherent latent patterns, relationships and similarities among the input data points.
Labeled data -> develop model to Make accurate predictions on unseen data.
Obfuscation might have Hidden patterns, learnable by Machine Learning.
Human expert to infer rules : time consuming, Too many data for human to process.
There is a large number of malware samples.
Use malware samples to automatically infer rules/signatures.
Machine Learning Malware Detection Methods
Similar to packer, except uses encryption rather than compression to obfuscate the executable’s content.
When unpacked is done, the malware is loaded into memory and triggers the execution.
Obfuscated content is stored within the new exe file (gives a new packed program).
Uses compression to obfuscate the executable’s content.
instructions that “do nothing”, e.g., using NOP (no operation).
Because after Obfuscation, signature will be different.Ĭan you give three examples the malware writers will do so that the malware samples cannot be detected using the traditional signature-based detection system?ĭead code insertion / garbage code insertion.
Obfuscation: attempt to hide the original intentions.
Find profiles of normal program execution (System call, API, memory usage, etc).
Rely on human experts in creating the signature.
#MALWAREBYTES 3.1.2 PROBLEMS UPDATE#
Require continue update of the signature db.
Identify whether the signature can be found for a particular file.
Determine a malware contacts a particular domain/IP address -> use domain/IP address to create a signature and monitor the network traffic to identify all the hosts contacting that address.
Construct malware detection rules manually.
Use signatures, heuristics and hand crafted rules
3 samples: do the same thing, but with different opcode sequences.
Transposition: insert jump instruction, so that code executes in the original order.
Morph code using combinations of transposition, substitution, insertion, deletion.
They have similar logical order, but they morph the codes so they look different.
Infiltration into running processes (add a piece of malicious code into a running process).
create a new record, change an existing record.
create new files, edit, encrypt, delete.
Deceive users and make them click email attachments.
Do they have any similarity so we can detect them?.
Nearly 1 million new malware samples created every day.
230K+ computer users hit by malware in Q2 2019.
#MALWAREBYTES 3.1.2 PROBLEMS SOFTWARE#
take the form of an executable, script, code or any other software.
is a code that performs malicious actions.
Finally we decide against a Multi-process malicious behavior by analyzing the cumulative behavior of identified collaborative processes. Beforehand we have learned different execution policy by employing reinforcement algorithm. In this method, we attempt to inspect the whole processes running on the system and discover collaborative processes by finding processes running along a common execution policy. In this paper, we have presented a new method called PbMMD for detecting Multi-process malware. This malware is the consequence of multiple processes cooperating to fulfill a malicious task each of which performing a partition of main task and none of them shows an identifiable malicious behavior. A recently immersed malware to defeat behavior-based detection approach is Multi-process malware. This approach is based on system call sequences to model a malicious behavior. Behavior-based detection is the most powerful approach to malware detection. Contemporary malware makes wide use of techniques to evade popular detection approaches.