In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer.

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Power system state estimation (PSSE) plays a vital role in stable operation of modern smart grids, while it is vulnerable to cyber attacks. False data injection attacks (FDIA), one of the most common cyber attacks, can tamper with measurement data and bypass the bad data detection (BDD) mechanism, leading to incorrect PSSE results.

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the most required videos in a multimedia distribution platform are films, TV reality programs, political speeches, sport competitions and music clips.

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Network penetration testing is a complicated step that requires a lot of research and preparation going into it. Once you’ve decided on conducting the procedure, it’s equally important to select a third-party service provider that upholds quality and possesses adequate experience. For this, there are a series of network penetration testing interview questions – and desired answers – you can keep in mind when screening potential service providers.

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It contains the original data corresponding to all simulation diagrams in the paper, including but not limited to the original data of maps, carpooling users, starting points, destinations, and routes.

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This dataset consisting of MODBUS/TCP communication was created using the Factory.IO simulator (trial version is available). The dataset contains different scenarios that control different industrial processes. For each scenario, files are provided to capture normal communication and communication with anomalies. The purpose of the dataset is to support research and evaluation of anomaly detection methods in the field of ICS.

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You don’t need to be a cybersecurity expert to know that the world of application security is changing at an alarming pace. The tools and techniques that attackers use are becoming more sophisticated, and it’s difficult for even the most well-resourced organizations to keep up with them.

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We constructed a rich AttackDB that consists of CTI from the MITRE ATT\&CK Enterprise knowledge base, the AlienVault Open Threat Exchange, the IBM X-Force Exchange and VirusTotal.

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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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Penetration testing plays an important role in securing websites. However, you need the right tools to run efficient tests. Penetration testing tools have different functions, pentest methodologies, features, and price ranges. It might be difficult to choose the ones most suitable for your organization. This post will briefly describe some of the finest penetration testing tools.

 

 

 

 

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