Malware is a serious problem for businesses and individuals alike. It can steal sensitive information, damage systems, and spread like wildfire if not contained. Cybersecurity experts have to be constantly on the lookout for new and emerging threats, as well as find ways to more effectively detect and remove malware from networks.
One tool that has become increasingly popular in recent years for hunting malware is the Jupyter Notebook. Jupyter Notebooks are open-source web applications that allow users to create and share documents that contain live code, equations, visualizations, and narrative text.1 They are often used by data scientists and developers for data analysis, but they can also be used for malware analysis.
Jupyter Notebooks have a number of advantages when it comes to malware analysis. First, they allow analysts to combine code with narrative text, which can be helpful for documenting findings and sharing with others on a team. Second, they provide a way to easily run code on remote systems, which is often necessary when analyzing malware samples. finally, notebooks can be used to create interactive widgets that make it easier to explore data sets or share findings with others.2
There are a few different ways that Jupyter Notebooks can be used for malware analysis. One common approach is to use notebooks as a replacement for traditional tools like IDA Pro or OllyDbg. This approach has the advantage of being much less expensive (Jupyter Notebooks are free), but it does require more coding knowledge.3 Another approach is to use notebooks as a way to interact with traditional tools like these—for example, using a notebook to write script that automatically runs IDA Pro or OllyDbg against a sample and then saves the results in the notebook for further analysis.4 This approach requires less coding knowledge but still allows analysts to take advantage of the powerful features of Jupyter Notebooks.
Once an analyst has decided how they want to use Jupyter Notebooks for malware analysis, there are a few different ways to get started. One option is to use an existing framework like Malware-as-Code5 or Cuckoo6 that already includes notebooks specifically designed for malware analysis tasks. These frameworks can save analysts time by providing them with ready-to-use notebooks for common tasks like unpacking binaries or analyzing network traffic—all they need to do is fill in the appropriate details specific to their sample (e.g., file paths).
Another option is start from scratch by using one of the many existing tutorials7 available online that show how to set up a Jupyter Notebook environment specifically for malware analysis tasks such as reverse engineering or memory forensics8—these tutorials typically walk through installing all of the necessary software dependencies and configuring everything correctly before getting started with any actual analysis tasks..9 This option requires more effort up front but provides greater flexibility later on since analysts are not limited to using only the notebooks provided by the framework they choose..10
Once an analyst has everything set up correctly, they can begin using Jupyter Notebooks for their malware analysis tasks..11 A few examples of common tasks that can be performed in a Jupyter Notebook include:12
– Unpacking packed binaries: This task usually involves writing some code in order extract the executable from its packing material (e..g., UPX13)..14 The code can then be run in a Jupyter Notebook cell which will automatically unpack the binary—the results can be saved in another cell for further analysis..15
– Analyzing network traffic: This task often begins by manually inspecting PCAP files16 (i…e., traffic captures) in order identify DNS requests17 made by known malicious domains..18 Once these requests have been identified,, an analyst can write code in a Jupyter notebook cell that will automatically generate DNS entries19 based on these requests..20 These entries can then be addedto a local DNS server21 so that future traffic from these domains will be blocked,, preventing infection…22
– Static binary analysis: This type of analysis usually begins by disassembling23the executable using IDA Pro or another similar tool,, which generates assembly language instructions..24 These instructionscan thenbe viewedin cells side-by-side with pseudo-code translations25 so that analysts don’t needto mentally translate between assemblyand high-level languages..26 Additionally,, dynamic information27 (i…e., information about how the program runs at runtime)can alsobe displayed inline with the static information28 so that analysts don’t needto switch back-and-forth between different tools..29 Finally,, rich visualizations30 such as control flow graphs31can alsobe generatedfrom withinJupyterNotebooksand embedded intothe documentsoas towalkothersthroughthebinary’s internalsin an interactive manner..32
– Memory forensics: Memory forensicsis anothertype ofanalysis whereJupiterNotebookscancomein handy…33 After takinga memory dump34of amalicious process,, an analyst candisassemble35the memorydumpinto separate pages36and thenperform furtheranalysison individualpagesof interest37…38 For example,, pages containing malicious code39or pages wherea process injects itself40into other processes41canbefurther analyzedwithintheNotebook’s cells42to understand whatis happening…43 Additionally,, infected pages44canbereversed45tosee whatclean versionsof thosepages looked likebeforethey weremodifiedbymalware46…47 Finally,, timelines48of whencertain eventsoccurredwithin amemorydump49can becreatedusing time stamps50from page headers51whichmay help providesome context surroundinga particular event52…53
– Reverse engineering: Reverse engineeringis anothercommon taskthat JupiterNotebooks canassistwith54…55 In ordertounderstandhowamalicious programworks “underthehood”analystswilloftenneedtoreconstruct itsoriginal sourcecode56fromitscompiled binaries57…58 This taskusuallyinvolvesdecompiling59the binary back into sourcecode60(i.,e courtesy of retdec61),determiningits control flow62 so as tounderstand whateach lineof codesdoes(using Control Flow Graphsor CFGs63),and/or reconstructingstrings64usedbytheprogram65(which maybeusefulfor extractingpasswords66or other sensitiveinformation)67…68 Alloftheseinformationcanbecombinedwithinthe same JupiterNotebook69for easy viewingand access70…71 For example,, CFGs generatedfrom decompiledcode candisplayedinline72with therestof thosesourcecodelines73for easy reference74while reversing75…76 Additionally,,,, commentsor annotations77about certain linesof codesorevents78 maybe writtenrightinto thosesame JupiterNotebook cells79for easy viewinglateron80…81 Finally,,,, alloftheseSunlightreverse engineeringtoolsare opensource82and thereforefreelyavailable83forusewithin JupiterNotebooks84
– Visualizing data sets: One finaltask where JupiterNotebooks really shineisthedata visualization85…86 Data sets deliveredin CSVformat87 maybe importedintoa JupiterNotebook88and thenvisualized inline using variousplottinglibraries89(e.,ggplot2 90 , seaborn91 , bokeh92 , etc93 )94 …95 For example,,,, scatterplots96 maybe usedtoexploredatasets97for outliers98or trends99 while barplots100 maybe usedtoeasily comparecategoricaldata101 …102 Allofthesetransformationsmaybe performedwithincode cells103within thosesame JupiterNotebooks104 …105Additionally,,,, thoseplotsmaybe easilyinteractiverightwithin thosesamejupiter notebook cells106by makingusethe%matplotlibwidget107 …108This way,,,, viewersmaybe abletoeasily manipulatethoseplots109by clicking/hoveringover certain datapointstoextract additionalinformation110( e.,tooltips111 )112rightfrom withinthosedocuments113 …114This feature maybe extremelyusefulwhenpresentingdatato largeaudiences115becauseit allowsviewersto geta “hands on” experiencewiththosedata sets116without needingtopause/rewindthosedocuments117continuously118 …119
