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866 results for “attack”
Figures 10–12 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 10–12. Adults of Edessa leucogramma. 10) Male reproductive system of Edessa leucogramma, aedeagus: a) photograph of the male aedeagus in ventral view, b) illustration of the male aedeagus in ventral view: (Ej.d.) ejaculatory duct, (Lm.f.) medial lobe of the phallus. 11) Dorsal habitus of the female. 12) Photograph of pygidium and external genitalia of the female: (VIII, IX, X) sternites 8, 9, and 10 respectively; (Sp7 and Sp8) spiracles 7 and 8.
Figures 1–3 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 1–3. Immature stages of Edessa leucogramma. 1) Eggs and oviposition of Edessa leucogramma. a) Number of eggs per clutch. b) Nymphs developed inside the egg. 2) First-instar nymphs of Edessa leucogramma. a) Newly emerged nymphs. b) Nymphs congregating around the egg clutches. c) Clustering nymphs showing dorsal-posterior orange spots on the abdomen. 3) Second-instar nymphs of Edessa leucogramma. a) Dorsal habitus. b) Ventral habitus.
Figures 4–6 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 4–6. Immature states of Edessa leucogramma. 4) Third-instar nymphs of Edessa leucogramma. a) Dorsal habitus. b) Ventral habitus. 5) Fourth-instar nymphs of Edessa leucogramma. a) Dorsal habitus. b) Ventral habitus. 6) Fifth-instar nymphs of Edessa leucogramma on host.
Figures 7–9 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 7–9. Adults of Edessa leucogramma. 7) Adult male of Edessa leucogramma. 8) Female external genitalia and sternites. 9) Internal reproductive system of the male. a) Dissected genitalia. b) Illustration of male genitalia: (Be.) ejaculatory bulb; (Dej.) ejaculatory duct; (Dr.) distal region; (GlsAc.) accessory glands; (Vd.) deferent ducts; (Tes.) testis exhibiting four testicular follicles; (Tues.) testicular follicle; (Pr.) proximal region.
Figs 19–26. Terellia ermolenkoi Korneyev, 1985 in A second species of the genus Urophora (Diptera: Tephritidae) attacking capitula of the genus Psephellus (Asteraceae)
Figs 19–26. Terellia ermolenkoi Korneyev, 1985, females (19, 20, 22, 25, 26) and males (20, 21, 23, 24). 19, female habitus (in lateral view); 20, male wing; 21, glans of phallus; 22, female abdomen (in dorsal view); 23, 24, male thorax (in dorsal view); 25, aculeus; 26, apex of aculeus.
Figs 10–18 in A second species of the genus Urophora (Diptera: Tephritidae) attacking capitula of the genus Psephellus (Asteraceae)
Figs 10–18. Urophora aprica (Fallén, 1820) from European Russia (Ulyanovsk Province; reared from Centaurea cyanus), females (10, 12, 15–18) and males (11, 13, 14). 10, female habitus (in lateral view); 11, male habitus (in lateral view); 12, female abdomen (in dorsal view); 13, male wing; 14, glans of phallus; 15, aculeus; 16, distal part of aculeus; 17, 18, apex of aculeus.
Figs 1–9 in A second species of the genus Urophora (Diptera: Tephritidae) attacking capitula of the genus Psephellus (Asteraceae)
Figs 1–9. Urophora sevanensis sp. nov., paratypes (1–8) and holotype (9). 1, male habitus (in lateral view); 2, female habitus (in lateral view); 3, male wing; 4, female wing; 5, aculeus; 6, apex of aculeus; 7, female thorax (in dorsal view); 8, glans of phallus; 9, female abdomen (in dorsal view).
Tigray War: photographic dataset recorded in the aftermath of the December 2020 battles and drone attacks in Werkamba
<p>The Tigray war (Ethiopia) lasted from 4 November 2020 to 2 November 2022, when a Cessation of Hostilities Agreement (CoHA) came into existence. One year later, the CoHA is not fully implemented, there are enormous food shortages in Werkamba, but at least there is no more active warfare.</p><p>We can now publish a series of photographs taken at Werkamba (13°44'N, 39°E) and its environs, as soon as the Ethiopian and Eritrean armies had left the town, to engage warfare with the TDF (Tigray Defence Forces) in other fronts. For some weeks there were no military in Werkamba, and the residents who had fled found their town back in ruins, including killed civilians lying on the street.</p><p>Hence we have to warn the reader that some shocking imagery is ahead in this dataset that contains evidence of the horror of warfare.</p>
Mosquitoes escape looming threats by actively steering into the bow-wave induced by the attacker
<p>To detect and escape a threat, night flying insects must rely on other senses than vision alone. Here we study how anthropophilic malaria mosquitoes can escape a swatting hand in the dark using high-speed videography and numerical simulations. We show that these night flying mosquitoes escape looming objects by using the object-induced airflow in two ways. They first actively steer into the <a>bow-wave</a><span><span> </span></span> produced by the attacker, and then passively travel with this bow-wave away from the attacker; these two aspects explain two-thirds and one-third of their escape accelerations, respectively. Thus, flying mosquitoes being attacked in the dark rely both on airflow-sensing to trigger their escape, and on attacker-induced airflow to maximize their escape performance. Similar escape strategies are probably common among small lightweight insects.</p> <div> <div> <div></div> </div> </div>
GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles (presentation video)
<p>Video of the presentation for the publication M. Kamal, A. Barua, C. Vitale, C. Laoudias and G. Ellinas, "GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles," 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7, doi: 10.1109/VTC2021-Fall52928.2021.9625567.</p>
LoRaWAN Network Attacks
<p>This dataset includes a dump of messages seen on a LoRaWAN network collected during validation of the GUARD project. It includes plain traffic from real devices installed on city bus and some attacks artificially generated by replicating or altering messages.</p>
SLQ Injection Attack for training (D1)
<p>This dataset has SQL injection attacks as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLmap tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>The version of NetFlow used to build the datasets is 5.</p>
SLQ Injection Attack for Test (D2)
<p>This dataset has SQL injection attacks as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLmap tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>The version of NetFlow used to build the datasets is 5.</p>
SQL Injection Attack Netflow
<p><strong>Introduction</strong></p> <p>This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p><strong>Datasets</strong></p> <p>The firts dataset was colleted to train the detection models (<strong>D1</strong>) and other collected using different attacks than those used in training to test the models and ensure their generalization (<strong>D2</strong>).</p> <p>The datasets contain both benign and malicious traffic. All collected datasets are balanced.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <table> <thead> <tr> <th scope="col">Dataset</th> <th scope="col">Aim</th> <th scope="col">Samples</th> <th scope="col">Benign-malicious<br> traffic ratio</th> </tr> </thead> <tbody> <tr> <td>D1</td> <td>Training</td> <td>400,003</td> <td>50%</td> </tr> <tr> <td>D2</td> <td>Test</td> <td>57,239</td> <td>50%</td> </tr> </tbody> </table> <p> </p> <p><strong>Infrastructure and implementation</strong></p> <p>Two sets of flow data were collected with <a href="https://niebla.unileon.es/cybersecurity/dorothea">DOROTHEA</a>. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor<a href="https://github.com/aabc/ipt-netflow"><em> ipt_netflow</em></a> installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.</p> <p>DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)</p> <p>Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).</p> <p>The malicious traffic collected (SQLI attacks) was performed using <a href="https://sqlmap.org/">SQLMAP</a>. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.</p> <p>The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.</p> <table> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>'--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'</td> <td>Enumerate users, password hashes, privileges, roles, databases, tables and columns</td> </tr> <tr> <td>--level=5</td> <td>Increase the probability of a false positive identification</td> </tr> <tr> <td>--risk=3</td> <td>Increase the probability of extracting data</td> </tr> <tr> <td>--random-agent</td> <td>Select the User-Agent randomly</td> </tr> <tr> <td>--batch</td> <td>Never ask for user input, use the default behavior</td> </tr> <tr> <td>--answers="follow=Y"</td> <td>Predefined answers to yes</td> </tr> </tbody> </table> <p>Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the <a href="https://www.mysql.com/">MYSQL </a>or <a href="https://www.microsoft.com/es-es/sql-server/sql-server-2019">SQLServer </a>database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).</p> <p>The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24.<br> The malicious traffic in the test sets was collected under different conditions. For <strong>D1</strong>, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.</p> <p>However, for <strong>D2</strong>, BlindSQL SQLIAs were performed against the web form connected to a <a href="https://www.postgresql.org/">PostgreSQL </a>database. The IP address spaces of the networks were also different from those of <strong>D1</strong>. In <strong>D2</strong>, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.</p> <p>To run the MySQL server we ran <a href="https://mariadb.org/">MariaDB</a> version 10.4.12.<br> Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.</p>
Thermal Attacks Dataset (ThermoSecure)
<p>Thermal cameras can be utilized inconspicuously to expose heat traces left on input interfaces, posing a rising threat of a new front for side channel attacks. This research project aims to significantly contribute to and build on previous studies on thermal attacks by investigating deep learning models that can improve the accuracy of thermal attacks and testing them in real-world scenarios in an attempt to understand the impact of thermal attacks on user privacy and security. As part of the evaluation of our deep learning model, we captured and annotated 1,500 thermal images to create the first dataset of thermal images that capture the heat traces following an interaction (i.e. password entries).</p>
WiFi 2.4 GHz Jamming attack scenario P2 measurements using ADALM Pluto and Maia SDR
<p>The dataset comprises physical-layer data measurements (I-Q samples) collected using an ADALM Pluto SDR version B. The original firmware from Analog Devices was replaced with the Maia-SDR Firmware (<a href="https://maia-sdr.org/">https://maia-sdr.org/</a>). The data was gathered within a 250 square meter area of the WIRID-LAB (<a href="https://wirid-lab.umng.edu.co/">https://wirid-lab.umng.edu.co/</a> laboratory at the Military University Nueva Granada.</p> <p>The dataset is divided into two groups of measurements labeled 'JAMMER' and 'NORMAL', each containing 165 files. These files represent data collected from 15 different points across 11 WiFi channels.</p> <ul> <li><strong>NORMAL Group:</strong> Measurements were taken under standard WiFi traffic conditions without any interference from a jammer.</li> <li><strong>JAMMER Group:</strong> Measurements were taken while deploying a Legacy Short Training Field Jammer attack from a static point.</li> </ul> <p>Each .zip compressed file contains data for 15 measurement points, with each point captured over one second at a sampling rate of 15 Msps. The data is formatted according to the Signal Metadata Format (SigMF), with each measurement point having one <code>.sigmf-data</code> file and one <code>.sigmf-meta</code> file.</p> <p>File names indicate the WiFi channel (enumerated from 1 to 11), signal type (Jammer or Normal), and the attacker node's position 'P2'.</p> <p>An accompanying image (Deployment of a Jammer Attack Scenario inside WiridLAB.png) illustrates the test scenario."</p>
Dataset for "On a Collision Course: Unveiling Wireless Attacks to the Aircraft Traffic Collision Avoidance System (TCAS)"
<p>The dataset associated with "On a Collision Course: Unveiling Wireless Attacks to the Aircraft Traffic Collision Avoidance System (TCAS)"</p>
Fig. 1 in First report of Heilus freyreissi (Coleoptera: Curculionidae) attacking avocado and associated with Colletotricum sp. in Brazil
Fig. 1. Adult Heilus freyreissi isolated (A, B) and aggregated under the bark of the trunk of an avocado (C). The white ellipse indicates the location of the aggregation of adult beetles.
Fig. 2 in First report of Heilus freyreissi (Coleoptera: Curculionidae) attacking avocado and associated with Colletotricum sp. in Brazil
Fig. 2. Injury caused by adults of Heilus freyreissi on branches (A, B), avocado central leaf vein (C), inflorescence (D), peduncle (E), and fruit (F).
Fig.6. Instar II larva attacking a in Methodologicalaspects Of Study On Biologyand Development Cycles Of Dytiscus Latissimus (Coleoptera: Dytiscidae) In Laboratory Environment. Spring-Summer Period
Fig.6. Instar II larva attacking a caddis larva (with Fig.7. D. latissimus instar III larva exuvium on its left)
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.