Evidence Detection in Cloud Forensics

Citation Author(s):
Prasad
Purnaye
Researcher, MIT World Peace University, India
Vrushali
Kulkarni
Professor, MIT World Peace University, India
Submitted by:
Prasad Purnaye
Last updated:
Mon, 05/23/2022 - 23:28
DOI:
10.21227/2yr5-7z67
Data Format:
Links:
License:
1
1 rating - Please login to submit your rating.

Abstract 

Cloud forensics is different than digital forensics because of the architectural implementation of the cloud. In an Infrastructure as a Service (IaaS) cloud model. Virtual Machines (VM) deployed over the cloud can be used by adversaries to carry out a cyber-attack using the cloud as an environment. Investigation of such a crime requires sufficient evidence data to prove the attack in the court of law. Electronic evidence (EE) is any data that produce information relevant to the investigation.  Identifying evidence from the data generated in a cloud environment is a tedious and manual process. Adhering to RFC 3227 the evidence collection can be carried out once the evidence data is detected with appropriate triage.

Cyber attack originating from a VM leaves its trails on the resource that it utilizes. These patterns of attacks on the resource and its properties can be used to detect and acquire evidence data generated in a cloud.

We have generated a dataset using the following settings:

To generate the dataset a private cloud was set up. The system configuration included Intel® CoreTM i5-4590 Processor with 12 GB of RAM with 1TB of HDD. The private cloud setup was done using a KVM type-1 hypervisor along with OpenNebula (version 5.12) as a cloud management platform. To simulate the real-time cloud environment a script generating synthetic workload was deployed on the virtual machines of the cloud. An attack was carried out. The dataset is manually tagged with the known state of attack or normal to respective VM.

 

Instructions: 

 

 

 

 

 

 

 

 

 

 

 

 

About the dataset
The dataset generated is a KVM monitoring dataset however we proposed a novel feature-set. The methodology used to generate these novel features is explained in https://www.degruyter.com/document/doi/10.1515/comp-2022-0241/html. where the features can be used to train ML models for evidence detection.  

The second portion of the dataset is published under the standard dataset of IEEE Dataport under the name of Memory Dumps of Virtual Machines for Cloud Forensics.  

How to use
These two datasets can be used together as they are the outcome of the same experiment. Memory dumps have timestamp and VMID, UUID features. 
or 
This Dataset can be used to study the impact of an attack (origin) on the Rate of Resource utilization of a VM monitored at the hypervisor.

 

Sr No

Category

Feature

Description

1

Meta-data

LAST_POLL

epoch timestamp

2

VMID

The ID of the VM

3

UUID

unique identifier of the domain

4

dom

domain name

5

Network

rxbytes_slope

Rate of received bytes from the network

6

rxpackets_slope

Rate of received packets from the network

7

rxerrors_slope

Rate of the number of receive errors from the network

8

rxdrops_slope

Rate of the number of received packets dropped from the network

9

txbytes_slope

Rate of transmitted bytes from the network

10

txpackets_slope

Rate of transmitted packets from the network

11

txerrors_slope

Rate of the number of transmission errors from the network

12

txdrops_slope

Rate of the number of transmitted packets dropped from the network

13

Memory

timecpu_slope

Rate of time spent by vCPU threads executing guest code

14

timesys_slope

Rate of time spent in kernel space

15

timeusr_slope

Rate of time spent in userspace

16

state_slope

Rate of running state

17

memmax_slope

Rate of maximum memory in kilobytes

18

mem_slope

Rate of memory used in kilobytes

19

cpus_slope

Rate of the number of virtual CPUs chaged

20

cputime_slope

Rate of CPU time used in nanoseconds

21

memactual_slope

Rate of Current balloon value (in KiB)

22

memswap_in_slope

Rate of The amount of data read from swap space (in KiB)

23

memswap_out_slope

Rate of The amount of memory written out to swap space (in KiB)

24

memmajor_fault_slope

Rate of The number of page faults where disk IO was required

25

memminor_fault_slope

Rate of The number of other page faults

26

memunused_slope

Rate of The amount of memory left unused by the system (in KiB)

27

memavailable_slope

Rate of The amount of usable memory as seen by the domain (in KiB)

28

memusable_slope

Rate of The amount of memory that can be reclaimed by balloon without causing host swapping (in KiB)

29

memlast_update_slope

Rate of The timestamp of the last update of statistics (in seconds)

30

memdisk_cache_slope

Rate of The amount of memory that can be reclaimed without additional I/O, typically disk caches (in KiB)

31

memhugetlb_pgalloc_slope

Rate of The number of successful huge page allocations initiated from within the domain

32

memhugetlb_pgfail_slope

Rate of The number of failed huge page allocations initiated from within the domain

33

memrss_slope

Rate of Resident Set Size of the running domain's process (in KiB)

34

Disk

vdard_req_slope

Rate of the number of reading requests on the vda block device

35

vdard_bytes_slope

Rate of the number of reading bytes on the vda block device

36

vdawr_reqs_slope

Rate of the number of write requests on the vda block device

37

vdawr_bytes_slope

Rate of the number of write requests on vda  the block device

38

vdaerror_slope

Rate of the number of errors in the vda block device

39

hdard_req_slope

Rate of the number of read requests on the hda block device

40

hdard_bytes_slope

Rate of the number of read bytes on the had block device

41

hdawr_reqs_slope

Rate of the number of write requests on the hda block device

42

hdawr_bytes_slope

Rate of the number of write bytes on the hda  block device

43

hdaerror_slope

Rate of the number of errors in the hda block device

44

TARGET

Status

Attack/Normal