Idea - FICS

Malware Attribution

Team and Contact Details

Student Name School Degree Year Email
Hadia Saif KhanMCSUndergraduateFourth[email protected]
Zojaja ArifMCSUndergraduateFourth[email protected]
Maryam Haq KhattakMCSUndergraduateFourth[email protected]
Syed Ameer AbdullahMCSUndergraduateFourth[email protected]

Inter School Idea ? No
Do you need expertises from another area: No
If Yes please provide details of expertises you need:

Idea Details

Idea Name: Malware Attribution
Slogan: Be Aware Connect With Care
Supervisor Name: Waleed Bin Shahid
Supervisor Designation: Assistant Professor
Supervisor School: MCS
Supervisor Department: Information Security
Contact number: 03333305533
Email ID: [email protected]
Abstract:
    A malware attribution system is responsible for analysing and identifying the characteristics and traits of malware. The system compels to correlate the capabilities with the dataset and assigns the malware samples to known and new campaigns; the system supports extending these detections continuously through new rules.
What is the unmet need in society that your idea will fulfill ?
    Our idea aims at the generation of malware database that would be categorically arranged and wouldn’t cost anyone a penny. The database groups known malware conforming to their family characteristics and key features; and can update itself with the advent of the latest malware.
Who needs it ? How many would benefit ?
   The system targets the researcher’s community that requires and works with large datasets. The dataset is fundamental for the development of the research study. Results backed up by open-source datasets often give better and more accurate results. The malware analysts are presumably the benefactors
How will the solution works
    The system uses anomaly-detection methodology for diagnosing malicious activity in a windows-based operating system. It then collects and analyses the malicious event logs generated using tools like Sysmon and Procmon. The system then identifies and analyses key-features of the malware samples based on API analysis and TTPs. It classifies the malware to the malware families through a deep-learning algorithm.
Who are your competitors ? How is your solution different
    Our competitor is the Kaspersky Malware Attribution System. However, we’re levelling up the competition by bringing in not just a malware attribution system but an open-sourced, free of cost malware database as well. That would be available everyone everywhere.
Status: new
Entry Date & Time: 2022-12-15 (0825)