Myriam  Rogahn

Myriam Rogahn

1598753280

Deep LSTM based Malware Analysis

Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection. This study is focused on metamorphic malware that is the most advanced member of the malware family. It is quite impossible for anti-virus applications using traditional signature-based methods to detect metamorphic malware, which makes it difficult to classify this type of malware accordingly. Recent research literature about malware detection and classification discusses this issue related to malware behaviour.

Cite The Work

If you find this implementation useful please cite it:

@article{10.7717/peerj-cs.285,
title = {Deep learning based Sequential model for malware analysis using Windows exe API Calls},
author = {Catak, Ferhat Ozgur and Yazı, Ahmet Faruk and Elezaj, Ogerta and Ahmed, Javed},
year = 2020,
month = jul,
keywords = {Malware analysis, Sequential models, Network security, Long-short-term memory, Malware dataset},
volume = 6,
pages = {e285},
journal = {PeerJ Computer Science},
issn = {2376-5992},
url = {https://doi.org/10.7717/peerj-cs.285},
doi = {10.7717/peerj-cs.285}
}

You can access the dataset from my My GitHub Repository.

Introduction

Malicious software, commonly known as malware, is any software intentionally designed to cause damage to computer systems and compromise user security. An application or code is considered malware if it secretly acts against the interests of the computer user and performs malicious activities. Malware targets various platforms such as servers, personal computers, mobile phones, and cameras to gain unauthorized access, steal personal data, and disrupt the normal function of the system.

One approach to deal with malware protection problem is by identifying the malicious software and evaluating its behaviour. Usually, this problem is solved through the analysis of malware behaviour. This field closely follows the model of malicious software family, which also reflects the pattern of malicious behaviour. There are very few studies that have demonstrated the methods of classification according to the malware families.

All operating system API calls made to act by any software show the overall direction of this program. Whether this program is a malware or not can be learned by examining these actions in-depth. If it is malware, then what is its malware family. The malware-made operating system API call is a data attribute, and the sequence in which those API calls are generated is also critical to detect the malware family. Performing specific API calls is a particular order that represents a behaviour. One of the deep learning methods LSTM (long-short term memory) has been commonly used in the processing of such time-sequential data.

#machine-learning #public-dataset #cybersecurity #artificial-intelligence

What is GEEK

Buddha Community

Deep LSTM based Malware Analysis
Angela  Dickens

Angela Dickens

1597424340

Extracting packer injected malware from memory [Remcos RAT]

Extraction of injected malicious PE from dynamic memory in windows (Remcos malware)

Background

Usually, malware comes packed by some packer which obfuscates the original code and helps it to evade AV software or general human suspicion. When executed the packed binary inject actual binary in the memory and runs it from there.

What we will cover in this write-up?

  1. Approach for setting break-point in packed binary to stop it in midst of injecting the malicious code and executing it.
  2. Bypassing malware’s “debugger bypass tricks”
  3. Locate and dump injected code from the memory.
  4. Do necessary patching.

Setup Environment :

_Operating_System _: Windows 10, Enterprise Evaluation 180914 Software_Arsenal : x32debugger, PE bear, Hexadecimal Editor HxD Malware_Binary : Remcos

What is Remcos RAT?

here you go —

Trojan.Remcos

Trojan.Remcos typically infects a system by embedding a specially-crafted settings file into an Office document, this…

blog.malwarebytes.com

Want to follow up with the article?

Here’s your malware, handle with care and caution.

https://malshare.com/sample.php?action=detail&hash=15fdc5c025e9d1645df07110c455aa09

Lab setup and Initial Inspection

We will be using our Windows 10 Virtual Machine. We have set networking to “HOST ONLY” to transfer the binary to VM via Python’s SimpleHTTPServer.

#code-injection #malware #binary-analysis #malware-analysis #data analysis

Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Chet  Lubowitz

Chet Lubowitz

1595065800

Encryption Utility Firm Accused of Bundling Malware Functions

The increasingly prevalent GuLoader malware has been traced back to a far-reaching encryption service that attempts to pass as above-board.

An Italian company that sells what it describes as a legitimate encryption utility is being used as malware packer for the cloud-delivered malicious GuLoader dropper, claim researchers. The tool, according a recent investigation, creates GuLoader samples and helps the malware avoid antivirus detection.

For its part, the company claims it has taken steps to prevent bad actors from using its wares for ill.

According to researchers at Check Point, the company identified as CloudEyE is looking to take a piece of the traditional packer and crypter market – a thriving arena that caters to malware authors looking for obfuscation for their wares.

GuLoader is a widespread dropper that compromises targets and then delivers second-stage malware. It’s been constantly updated over the course of 2020, according to Check Point, with new binaries sporting sandbox evasion techniques, code randomization features, command-and-control (C2) URL encryption and additional payload encryption.

“As a result, we can reasonably assume that behind GuLoader there is a major new service” providing various forms of encryption, according to the researchers.

#cloud security #malware #check point #cloudeye #crypter #darkeye #encryption #guloader #italian company #malware #malware analysis #packer #securitycode.eu