The dataset is generated by performing different Man-in-the-Middle (MiTM) attacks in the synthetic cyber-physical electric grid in RESLab Testbed at Texas AM University, US. The testbed consists of a real-time power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master, SEL real-time automation controller (RTAC), and Cisco Layer-3 switch. With different scenarios of MiTM attack, we implement a logic-based defense mechanism in RTAC and save the traffic data and related cyber alert data under the attack.

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The Bitcoin Lightning Network (LN) disrupts the scenario as a fast and scalable method to make payment transactions off-chain, alongside the Bitcoin network, thereby reducing the on-chain burden. Understanding the topology of the LN is crucial, not only because it is key to performance, but also for ensuring its security and privacy guarantees. The topology of the LN affects, among others, the ability to successfully route payments between nodes, its resilience (against both attacks and random failures), and the privacy of payments.

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This dataset consists of 1878 labeled images of flowers from blackberry trees from the specie Rubus L. subgenus Rubus Watson. These are white flowers with five petals that blossom in the spring through summer. The images were collected using an Intel RealSense D435i camera inside a greenhouse.

This images were inicially collected to support a robotic autonomous pollination project.

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This dataset was created by gathering "attack stories" related to IoT devices from the cybersecurity news site Threatpost. Because there aren't many databases of IoT vulnerabilities, we used Threatpost as an index to recent vulnerabilities, which we then researched using a variety of sources, like academic papers, blog posts, code repositories, CVE entries, government and vendor advisories, product release notes, and whitepapers.

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Parasitic infections have been recognised as one of the most significant causes of illnesses by WHO. Most infected persons shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals. Diagnosis of intestinal parasites is usually based on direct examination in the laboratory, of which capacity is obviously limited.

Last Updated On: 
Thu, 02/24/2022 - 15:33
Citation Author(s): 
Duangdao Palasuwan, Thanarat H. Chalidabhongse, Korranat Naruenatthanaset, Thananop Kobchaisawat, Kanyarat Boonpeng, Nuntiporn Nunthanasup, Nantheera Anantrasirichai

WannaCry Bitcoin Cash-in and Cash-out payment network data in JSON along with STIX representation of address 12t9YDPgwueZ9NyMgw519p7AA8isjr6SMw12t9YDPgwueZ9NyMgw519p7AA8isjr6SMw

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This is the dataset for the paper Bayesian Inference of Sector Orientation in LTE Networks based on End-User Measurements published at VTC 2021 - Fall.

It includes a set of Drive-Test RSRP Pathloss Measurements with their relative position to the corresponding eNodeB. In total it contains data for 91 three-sector eNodeBs, which results in  273 sectors.

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Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence ``Large rooms and great breakfast", ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks.

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The time-to-market pressure and the continuous growing complexity of hardware designs have promoted the globalization of the Integrated Circuit (IC) supply chain. However, such globalization also poses various security threats in each phase of the IC supply chain. Although the advancements of Machine Learning (ML) have pushed the frontier of hardware security, most conventional ML-based methods can only achieve the desired performance by manually finding a robust feature representation for circuits that are non-Euclidean data. As a result, modeling these circuits using graph learning to imp

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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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