This study used DARPA 2000 dataset since it has been widely used by researchers in the field. The type of NIDSs used to replay the dataset is signature –based RealSecure Version 6.0 (Internet Security System, 2000). This data are downloaded from Ning (2002). It contains four separated files which represent two types of simulated scenarios (Scenario One and Scenario Two) of Distributed Denial of Services (DDoS) network attack on two different networks. Haines (2000) mentioned that attacks in Scenario Two were stealthier than Scenario One. They contain thousand of event logs or network packets that have been reported from the Demilitarized Zone (DMZ) and Inside Networks.

Darpa is adenine dataset consisting of services between source IPs both destinations IPs. This dataset contains different attacks between IPs.

Operationally Transparent Cyber (OpTC) was a technology transition pilot study funded under Boston Fusion Corp.'s Cyber APT Scenarios for Enterprise Systems (CASES) project. Its primary objective was to determine if DARPA Transparent Computing (TC) program technologies could scale without loss of detection performance to address cyber defense capability gaps identified in USTRANSCOM's Joint Deployment Distribution Enterprise (JDDE) solicitation for the government fiscal years 2019-2023. Boston Fusion along with two performers from the TC program (Five Directions providing endpoint telemetry (TA1) and BAE providing analysis over the data (TA2)) worked to scale their systems from two machines to one thousand machines. The OpTC team conducted scaling and detection tests in the fall of 2019. A third performer (Provatek), not originally associated with the TC program, acted as a red team and test coordinator. This data set represents a subset of that activity.

The OpTC system architecture is based on one used in TC program evaluations. Kafka, an open-source stream-processing server, is used to pass information among system components. Each Windows 10 endpoint is equipped with an endpoint sensor that monitors host events, packs them into JSON records, and sends them to Kafka. As these records flow into Kafka, a translation server aggregates them into new data records in a format called eCAR that are then pushed back to Kafka. As the translation server pushes eCAR records to Kafka, a data analytics component integrates them into a graph data structure for analysis and visualization.

OpTC took TC system components that worked well on two hosts in TC program tests and scaled them up to work with one thousand hosts. This scaled-up system was evaluated over two weeks in a highly instrumented environment, and the data in this collection contains approximately a terabyte of data in compressed JSON compatible format from that evaluation. The evaluation started with a period of benign record generation, followed by the injection of malware by a red team. Benign traffic ran continuously during red team activity. Due to constraints in collection data space during the evaluation, data from five hundred hosts were collected rather than from the full set of one-thousand hosts.

"The DARPA Invisible Headlights Dataset is a large-scale multi-sensor dataset annotated for autonomous, off-road navigation in challenging off-road environments. It features simultaneously collected off-road imagery from multispectral, hyperspectral, polarimetric, and broadband sensors spanning wave-lengths from the visible spectrum to long-wave infrared and provides aligned LIDAR data for ground-truth shape. Camera calibrations, LiDAR registrations, and traversability annotations for a subset of the data are available."

The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus

The description of significant features of DARPA 2000 dataset.

Disclaimer DARPA is releasing these files in the public domain to stimulate further research. Their release implies no obligation or desire to support additional work in this space. The data is released as-is. DARPA makes no warranties as to the correctness, accuracy, or usefulness of the released data. In fact, since the data was produced by research prototypes, it is practically guaranteed to be imperfect. Nonetheless, as this data represents a very large repository of semantically rich and structured data, DARPA believes that it is in the best interests of the Department of Defense and the research community to make them freely available. Distribution Statement A: Approved for public release. Distribution is unlimited. OpTC OverviewOperationally Transparent Cyber (OpTC) was a DARPA transition pilot activity funded under Boston Fusion Corp.'s (BFC) Cyber APT Scenarios for Enterprise Systems (CASES) project. The main goal of the pilot was to determine if technology developed under the DARPA Transparent Computing (TC) program could scale up to one thousand clients while maintaining detection performance. Boston Fusion along with two performers from the TC program (Five Directions and BAE) developed the OpTC prototype. Provatek joined the team to serve as test coordinator, conducting scaling and detection tests in 2019. This data set represents a subset of collection from that activity. OpTC was evaluated at the National Cyber Range (NCR), which provided a well-instrumented facility to measure the impact of the system on network and client machine bandwidth, disk, and memory usage. Client machines created using VMware were programmed to complete general tasks such as creating, editing, and deleting presentations and text documents; sending, receiving, and downloading attachments from emails; browsing various websites; and mimicking generic daily user activities. Each client machine in the NCR evaluations was equipped with an Acuity Intelligent Agent (AIA) sensor developed by Five Directions. This sensor sends real time, system-level data to servers equipped with Acuity Translator (AT) software, also developed by Five Directions. The Acuity Translator servers compile co-related events into aggregate messages and forwards the contents to Rapid Infiltration and Prevention of Exfiltration (RIPE) translators developed by BAE. The messages then undergo additional refinement before being sent to the RIPE Data Analytics Engine, which generates a network topology graph that may be queried to identify advanced persistent threat (APT) activity. The OpTC team collected the data in this release over three days, during which the number of clients varied from five hundred to one thousand. Working with five hundred clients tended to be more convenient in terms of the amount of time it took to bring up the system and manage the instrumentation. During the three-day evaluation event, randomly chosen machines were attacked, compromised, and used to perform additional attacks on other network clients. All event data was recorded for post-event analysis with ground truth data on attack insertions documented. The dataset consists of four main directories, each containing a single file per client. These files are sorted by event time and labeled based on data provided by the red team.

High-fidelity, physics-based multichannel radar data cube provided by the DARPA KASSPER project. This data is ideal for analyzing space-time adaptive processing (STAP) algorithms since both sample data and truth data are provided.

SETA Support DARPA FY2010/FY2015 Data Base contains information on contracts and performers of DARPA in 2010 and 2015 fiscal years. Contains information on 3793 contracts, including: Contract Number, Performer's Name, Performer's Type, Agent Name, Program Type (6.1, 6.2, 6.3, or unknown (X)), Program Name, DARPA Office, Program Manager, Fiscal Year, Obligated, SETA Marker, Ratio (SETA/Total), as well as Whole Program Costs and Whole Program SETA-support Costs. Abbreviations: SETA - systems engineering and technical assistance; DARPA - Defense Advanced Research Projects Agency.

This is the data set used for intrusion detector learning task in the Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99, The Fifth International Conference on Knowledge Discovery and Data Mining. The intrusion detector learning task is to build a predictive model (i.e. a classifier) capable of distinguishing between bad'' connections, called intrusions or attacks, andgood'' normal connections.

The 1998 DARPA Intrusion Detection Evaluation Program was prepared and managed by MIT Lincoln Labs. The objective was to survey and evaluate research in intrusion detection. A standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment, was provided. The 1999 KDD intrusion detection contest uses a version of this dataset.

Lincoln Labs set up an environment to acquire nine weeks of raw TCP dump data for a local-area network (LAN) simulating a typical U.S. Air Force LAN. They operated the LAN as if it were a true Air Force environment, but peppered it with multiple attacks.

The raw training data was about four gigabytes of compressed binary TCP dump data from seven weeks of network traffic. This was processed into about five million connection records. Similarly, the two weeks of test data yielded around two million connection records. ; gcounsel@ics.uci.edu

Dataset link: https://github.com/leggedrobotics/cerberus_darpa_subt_datasets

The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus (TIMIT) Training and Test Data

The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems. TIMIT has resulted from the joint efforts of several sites under sponsorship from the Defense Advanced Research Projects Agency - Information Science and Technology Office (DARPA-ISTO). Text corpus design was a joint effort among the Massachusetts Institute of Technology (MIT), Stanford Research Institute (SRI), and Texas Instruments (TI). The speech was recorded at TI, transcribed at MIT, and has been maintained, verified, and prepared for CD-ROM production by the National Institute of Standards and Technology (NIST). This file contains a brief description of the TIMIT Speech Corpus. Additional information including the referenced material and some relevant reprints of articles may be found in the printed documentation which is also available from NTIS (NTIS# PB91-100354).

Reference:

• http://academictorrents.com/details/34e2b78745138186976cbc27939b1b34d18bd5b3

Background The dataset to be audited was provided which consists of a wide variety of intrusions simulated in a military network environment. It created an environment to acquire raw TCP/IP dump data for a network by simulating a typical US Air Force LAN. The LAN was focused like a real environment and blasted with multiple attacks. A connection is a sequence of TCP packets starting and ending at some time duration between which data flows to and from a source IP address to a target IP address under some well-defined protocol. Also, each connection is labelled as either normal or as an attack with exactly one specific attack type. Each connection record consists of about 100 bytes. For each TCP/IP connection, 41 quantitative and qualitative features are obtained from normal and attack data (3 qualitative and 38 quantitative features) .The class variable has two categories: • Normal • Anomalous

The 2009 DARPA dataset is a synthesized dataset created to simulate real Internet traffic and network attacks. Its duration is 10 days, between November 3 - 12, 2009. The dataset is about 6.4 TB, divided into thousands of pcap files of 954M each. The traffic contains synthetic HTTP, SMTP, and DNS background data. The attacks are large scale network attacks including DNS worms, http worms, and DDoS attacks. The worms and DDoS attacks have been parameterized to exhibit various propagation characteristics.

Environmental data collected during the DARPA Ocean of Things March 2022 Atlantic / Gulf Stream deployment.

The credibility of evidence is important. This statement is as vapid as it is difficult to assess. There is no shortage of evidence or claims, but the tools to assess their credibility are immature, particularly at scale. There is enormous potential return on investment from a dedicated effort that (a) assesses existing methods of determining credibility, (b) fosters innovation in methods, and (c) conducts comparative analysis of accuracy versus resource cost.

SETA Support DARPA FY2010/FY2015 Database contains information on contracts and performers of DARPA in 2010 and 2015 fiscal years. The dataset contains information on 3793 federal contracts, including for each ones: Contract Number, Performer's Name, Performer's Type, Agent Name, Program Type (6.1, 6.2, 6.3, or unknown (X)), Program Name, DARPA Office, Program Manager, Fiscal Year, Obligated, SETA Marker, Ratio (SETA/Total), as well as Whole Program Costs and Program SETA-support Costs.

Abbreviations: SETA - systems engineering and technical assistance; DARPA - Defense Advanced Research Projects Agency.

The dataset is approved for public release, distribution unlimited.

The dataset is contained in two files - scrimmage4_link_dataset.pickle and scrimmage5_link_dataset.pickle

The pickle files are stored as list of tuples, each list corresponding to a single link, and containing two elements. Each element a length equal to the number of frames in that link - it varies between link to link. The first tuple is contains the paramenters - 1. Signal to Noise Ratio ('snr') - 1 element 2. The Modulation and Coding Scheme ('mcs') - 1 element 3. The center frequency of the link ('centerFreq') - 1 element 4. The bandwidth of the link ('bandwidth') - 1 element 5. The Power Spectral Density ('psd') - 16 elements Thus the total width of each element of the first tuple for a link is 20.

The second tuple contains the success of transmission ('rxSuccess'). If it is 1, there is no frame error, if it is 0, there is a frame error.

Here are the links to the dataset files mentioned in the code (one pickle file for each scrimmage):

Scrimmage 4 (547.5 MB) Mirror

Scrimmage 5 (979.7 MB) Mirror

A larger dataset containing complete information about each match is also available. Please refer to SC2_Dataset_Documentation.pdf for more details regarding the structure of the full dataset. SC2_Dataset_Technical_Design_Report.pdf contains more information about the dataset acquisition process.

Here is the link to the full dataset (separate sqlite files for each match):

Full Dataset (135.517 GB) Mirror (Needs Access Request)

Please use the following citation to refer to the dataset: A. S. M. M. Jameel, A. P. Mohamed, X. Zhang and A. El Gamal, "Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Dataset," in IEEE Networking Letters, doi: 10.1109/LNET.2021.3096813.

Environmental data collected during the DARPA Ocean of Things February 2022 Gulf of Mexico deployment.