It is now widely known fact that the Cloud computing and Software defined network paradigms have received a wide acceptance from researchers, academia and the industry. But the wider acceptance of cloud computing and SDN paradigms are hampered by increasing security threats. One of the several facts is that the advancements in processing facilities currently available are implicitly helping the attackers to attack in various directions. For example, it is visible that the conventional DoS attacks are now extended to cloud environments as DDoS attacks.

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With the modern day technological advancements and the evolution of Industry 4.0, it is very important to make sure that the problem of Intrusion detection in Cloud , IoT and other modern networking environments is addressed as an immediate concern. It is a fact that Cloud and Cyber Physical Systems are the basis for Industry 4.0. Thus, intrusion detection in cyber physical systems plays a crucial role in Industry 4.0. Here, we provide the an intrusion detection dataset for performance evaluation of machine learning and deep learning based intrusion detection systems.

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This dataset contains measurements of TPC-C benchmark executions in MySQL server deployed in Google Cloud Platform.

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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.

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 are under publication and will be updated once the research article is published. This is one portion of the dataset. 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

 

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A proactive and reactive service composition service composition (PRSC) method for CMfg based on digital thread. Firstly, the digital thread-driven information interaction architecture is established, based on which the detailed process of PRSC and the constitution of digital thread are designed.

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This repository contains code and instruction to reproduce the experiments presented in the paper
"A Methodology and Simulation-based Toolchain for Estimating Deployment Performance of Intelligent Collective Services at the Edge"
by Roberto Casadei, Giancarlo Fortino, Danilo Pianini, Andrea Placuzzi, Claudio Savaglio, and Mirko Viroli.

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The dataset contains the navigation measurements obtained in the indoor experiment field. The volunteers move on the whole 4th floor of the Building D of Dong Jiu Teaching classes at Huazhong University of Science and Technology. Meanwhile, the experimental area consists of a total area of 717 m 2. These datasets were used and can be used to test and validate the radio map database updating-based localization positioning algorithm through the RSSI signals space.

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This is a dataset of client-server Round Trip Time delays of an actual cloud gaming tournament run on the infrastructure of the cloud gaming company Swarmio Inc. The dataset can be used for designing algorithms and tuning models for user-server allocation and server selection. To collect the dataset, tournament players were connected to Swarmio servers and delay measurements were taken in real time and actual networking conditions.

Instructions: 

Main dataset

For the main dataset, the 189 players and the 9 servers were distributed among 4 different regions: North America, South America, Europe, East Asia. The 9 servers were located in the following cities with their acronyms in the dataset:

  1. Santa Clara (nasc),
  2. Chicago (nach), 
  3. Dallas (nada),
  4. Toronto (nato),
  5. Brazil (sabr),
  6. London (uk), 
  7. Amsterdam (nl), 
  8. Hong Kong (hk), 
  9. Singapore (sg).

Each of the 189 players were able to connect to each of the 9 servers. The following data is registered for each player:

  1. User Identifier (in the field: user_id)
  2. Time of access (in the field: timestamp)
  3. Longitude (in the field: longitude)
  4. Latitude (in the field: latitude)
  5. IP Address (in the field: address)
  6. Access Support Network or Internet Service Provider (in the field: asn_org)

In the dataset file main-dataset.json, every record contains the network delay measurements from a particular player to each of the 9 servers. It should be noted that the URLs and the IP addresses of the servers are provided in a separate file main-dataset-servers.json.

The user ID is a unique 32-character identifier that is generated for each player; for example, 5193b0e1-2412-4338-ac8d-6f519049aa77. The time of access is based on the Unix timestamp which is counted in seconds January 1, 1970; for example, 1528484445170. Longitude and latitude are based on the geo-location of the player; for example, "longitude": "121.0409", "latitude": "14.5832". The Access Support Network is the ISP network in which the player is registered, for example Rogers Communications Canada Inc, Philippine Long Distance Telephone Company, AT&T Services Inc., tec.

After registering each player, a background JavaScript script was run in Swarmio’s client software to obtain the latency measurements connecting to all of the servers. The script would query Swarmio’s portal to retrieve a list of all servers. Then, it would cycle through each server and measure the RTT latency. It would then push the results back to Swarmio’s central server for storage. 

Each measurement consisted of sending 11 packets from the player to the server, and the following measurements were obtained (all in ms):

  1. Median latency/delay (in the field: latency)
  2. Delay jitter (in the field: jitter)
  3. Minimum obtained delay (in the field: min)
  4. Maximum obtained delay (in the field: max)
  5. Average obtained delay (in the field: avr)

It should be noted that out of the 9 servers, only the 1st server (“nl”) was used for testing the connection, and that can be noted from the field “testing” having the value of “1”. Therefore, the value of “stats” for the first server will have no measurements.

Secondary dataset

For the secondary dataset, we set up 11 different servers: 1 server owned by Swarmio Media in Toronto and 10 servers using the AWS cloud in the following locations:

  1. North Virginia,
  2. Ohio,
  3. Northern California,
  4. Oregon,
  5. Montreal,
  6. Brazil,
  7. Singapore,
  8. Mumbai,
  9. Sydney, AU
  10. Ireland

The same script as the main dataset was run in the Swarmio client software of 67 players. This time, each server sent 8 packets to each player, and only the average delay was recorded and stored.

The secondary dataset consists of the JSON file secondary-dataset.json, where the keys are the names of the servers, and the values contain a list of the delays to the 67 players. The players IPs are provided in order in a separate file secondary-dataset-users.json. It is also possible to reuse the code that was used to retrieve the measurements in the file HostsUsersRTT.py  . The IP addresses of the 11 servers can also be accessed in the file secondary-dataset-servers.json where the key of the record will have the name of the server; for example “N Virginia”, and the value will have the IP address of the server

In contrast to the main dataset, the secondary dataset contains only the delay between the servers and the players whereas the main dataset has more information such as the geo-location and the ISP. This makes the secondary dataset more suitable for testing and verification due to having a single label with only 2 features (IP addresses and city names), while the main dataset contains more features and measurements suitable for training and inference.

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Pen testing the method to evaluate the security of an application or network by safely exploiting any security vulnerabilities present in the system. These security flaws can be present in various areas such as system configuration settings, login methods, and even end-users risky behaviors.

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Here are some experimental results of the paper "A Variable-Length Mixed-Variable Pareto Optimization Approach to Evolutionary Cloud Service Allocation ".

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