In 2024, manufacturing saw the highest share of cyberattacks among the leading industries worldwide. During the examined year, manufacturing companies encountered more than a quarter of the total cyberattacks. Organizations in the finance and insurance followed, with around 23 percent. Professional, business, and consumer services ranked third, with 18 percent of reported cyberattacks. Manufacturing industry and cyberattacks The industry of manufacturing has been in the center of cyberattacks in a long time. The share of cyberattacks targeting organizations in this sector in 2018 was at 10 percent, while in 2024, it amounted to 26 percent. The situation is even more compliacted when we look at the cyber vulnerabilities found in this sector. In 2024, critical vulnerabilities in manufacturing companies lasted 205 days on average. IT perspective and prevention With recent technology developments, cybersecurity is crucial to an organization’s success. Realizing this, companies have been gradually increasing cybersecurity investments. Thus, in 2024, the cybersecurity budget worldwide was forecast to increase to nearly 283 billion U.S. dollars. Roughly nine in ten board directors of companies worldwide in professional services and media and entertainment industries say they expect an increase in the cybersecurity budget.

According to a 2024 survey of Chief Information Security Officers (CISO) worldwide, Ransomware attacks were a leading cybersecurity risk, with roughly ** percent naming it as one of the three major cybersecurity threats. A further share of ** percent of the respondents found malware to be a significant risk to their organizations' cybersecurity. Email fraud compromise and DDoS attacks followed closely, with ** percent.

IoT & Cybersecurity Knowledge Graph

This dataset is a unified knowledge graph constructed from various Hugging Face datasets focusing on IoT and Cybersecurity domains. It integrates structured and semi-structured data to provide a comprehensive view of network activities, device behaviors, cyberattacks, and building automation systems.

  Dataset Description

The primary goal of this dataset is to facilitate research and development in areas such as:

IoT Security:… See the full description on the dataset page: https://huggingface.co/datasets/RDLTechworks/cybersec-knowledge-foundation.

This work presents a threat modelling approach to represent changes to the attack paths through an Internet of Things (IoT) environment when the environment changes dynamically, that is, when new devices are added or removed from the system or when whole sub-systems join or leave. The proposed approach investigates the propagation of threats using attack graphs, a popular attack modelling method. However, traditional attack-graph approaches have been applied in static environments that do not continuously change, such as enterprise networks, leading to static and usually very large attack graphs. In contrast, IoT environments are often characterised by dynamic change and interconnections; different topologies for different systems may interconnect with each other dynamically and outside the operator’s control. Such new interconnections lead to changes in the reachability amongst devices according to which their corresponding attack graphs change. This requires dynamic topology and attack graphs for threat and risk analysis. This article introduces an example scenario based on healthcare systems to motivate the work and illustrate the proposed approach. The proposed approach is implemented using a graph database management tool (GDBM), Neo4j, which is a popular tool for mapping, visualising, and querying the graphs of highly connected data. It is efficient in providing a rapid threat modelling mechanism, making it suitable for capturing security changes in the dynamic IoT environment. Our results show that our developed threat modelling approach copes with dynamic system changes that may occur in IoT environments and enables identifying attack paths, whilst allowing for system dynamics. The developed dynamic topology and attack graphs can cope with the changes in the IoT environment efficiently and rapidly by maintaining their associated graphs.

In 2023, ransomware was the most frequently detected cyberattack worldwide, with around 70 percent of all detected cyberattacks. Network breaches ranked second, with almost 19 percent of the detections. Although less frequently, data exfiltration was also among the detected cyberattacks.

Did the COVID-19 pandemic really affect cybersecurity? Short answer – Yes. Cybercrime is up 600% due to COVID-19.

The global indicator 'Estimated Cost of Cybercrime' in the cybersecurity market was forecast to continuously increase between 2024 and 2029 by in total *** trillion U.S. dollars (+***** percent). After the eleventh consecutive increasing year, the indicator is estimated to reach ***** trillion U.S. dollars and therefore a new peak in 2029. Notably, the indicator 'Estimated Cost of Cybercrime' of the cybersecurity market was continuously increasing over the past years. Ransomware and manufacturing industry Cyberattacks remain a significant challenge for organizations worldwide, sectors such as manufacturing, finance, and insurance were the most affected by these attacks. Among the various types of cyber threats, ransomware was the most frequently detected, accounting for around ** percent of all incidents. The manufacturing industry, in particular, faced the highest number of ransomware attacks, making it the most targeted sector globally. Cybersecurity awareness and investments Organizations worldwide became increasingly aware of the dangers posed by cyberattacks, approximately ** percent of internet users became familiar terms such as with "ransomware." In response to these threats, companies invested more in cybersecurity. In 2024, the average annual increase in IT security budgets was expected to reach *** percent. In fact, for companies worldwide, enhancing the cyber resilience of their cybersecurity teams was the top spending priority in 2023.

Between the first quarter of 2023 and the third quarter of 2024, the number of records exposed in data breaches in the United States decreased significantly. In the most recent measured period, over 93.7 million records were reported as leaked, down from around 116 million in the previous quarter.

PowerBench: EVCS Cyber Attack Datasets for Power Distribution Networks

This dataset is part of the PowerBench benchmark suite designed to support machine learning research in resilient and secure power distribution networks. It includes one out of the three types of cyberattacks modeled on IEEE 34-bus, 123-bus, and 8500-node test feeders:

EVCS Attacks

• Adversarial manipulation of the charging behavior of grid-connected electric vehicle charging stations (EVCS).
• Suitable for learning-based intrusion detection and localization of compromised EVCSs.

Each attack dataset contains .pkl simulation files, .gml grid topology, and scenario metadata. All simulations were generated using OpenDSS via OpenDSSDirect.py.

Please refer to the included README.md for detailed task guidance and loading instructions.

PowerBench: PV and Sensor Cyber Attack Datasets for Power Distribution Networks

This dataset is part of the PowerBench benchmark suite designed to support machine learning research in resilient and secure power distribution networks. It includes two out of the three types of cyberattacks modeled on IEEE 34-bus, 123-bus, and 8500-node test feeders:

Sensor Attacks

• Adversarial manipulation of voltage measurements from grid sensors.
• Supports time series anomaly detection, graph classification, and sensor-level localization.

PV Attacks

• Adversarial manipulation of the control system associated with grid-connected PV systems and false data injection on the point of common coupling (PCC) meters.
• Enables detection of cyberattacks and localization of compromised PV systems.

Each attack dataset contains .pkl simulation files, .gml grid topology, and scenario metadata. All simulations were generated using OpenDSS via OpenDSSDirect.py.

Please refer to the included README.md for detailed task guidance and loading instructions.

Revenue in the cybersecurity industry worldwide reached $146.32 billion in 2022.

Over 95% of cybersecurity breaches occur as a result of human error.

Cyber Security Market size was valued at USD 197.4 billion in 2021, and is predicted to reach USD 657.02 billion by 2030, with a CAGR of 12.8% from 2022 to 2030.

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

Insights On Cyberbiosecurity Field

According to Cyberbiosecurity: A New Perspective on Protecting U.S. Food and Agricultural System: "The US's national data and infrastructure security issues affecting the “bioeconomy” are evolving rapidly. Simultaneously, the conversation about cyber security of the U.S. food and agricultural system (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse and they require advanced technologies and efficiencies that rely on computer technologies, big data, cloud-based data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bio economy, but currently protections are minimal and do not broadly exist across the food and agricultural system."

Cyberbiosecurity is an emerging discipline for protecting life sciences data, functions, operation, and the bio-economy. https://www.frontiersin.org/files/Articles/447748/fbioe-07-00063-HTML-r1/image_m/fbioe-07-00063-g001.jpg" alt="">

Insights On The Dataset

The BATtle of the Attack Detection ALgorithms (BATADAL) will objectively compare the performance of algorithms for the detection of cyber attacks in water distribution systems.

C-Town Public Utility (CPU) is the main water distribution system operator of C-Town (Figure 1). For many years, CPU has operated a static distribution topology. In the last year, CPU has introduced novel smart technology to enable remote data collection from sensors in the field, and remote control of actuators. Shortly after that new technology has been introduced, anomalous low levels in Tank T5 and high levels in Tank T1 were observed. A month later, a water overflow in Tank T1 occurred. While CPU personnel at the control center were able to see the anomalous readings for the first two episodes, Tank T1 overflow took place unexpectedly while the water level readings were always below the alarm thresholds and pumping operations appeared to be normal. Searching for the causes, CPU engineers suspect potential cyber-attacks for all these episodes. In particular, they are considering adversaries that are able to activate and deactivate the actuators in C-Town, as well as altering the readings of the sensors deployed in the network and the reported status of actuators, and interfering with the connections established between networked components. The participants' task is thus to develop an online alert system for cyber-physical attacks.

Additional Information About The Dataset From Other Papers: - SCADA data are real-time, field-based network measurements (tank water level, pump flow, etc.) transmitted to the central system by programmable logic controllers (PLCs) - C-Town consists of 388 nodes linked with 429 pipes and is divided into 5 district metered areas (DMAs). - More specifically, the SCADA data include the water level at all 7 tanks of the network (T1–T7), status and flow of all 11 pumps (PU1–PU11) and the one actuated valve (V2) of the network, and pressure at 24 pipes of the network that correspond to the inlet and outlet pressure of the pumps and the actuated valve.

https://ascelibrary.org/cms/asset/1375ee5f-cc1e-498f-8a03-1180c61ee9fe/figure1.jpg" alt="">

Graph Annotation: - L_T #: water level of a tank # [meter].​ - S_PU # or S_V # : status of a pump # or a valve # [dmnl]. Binary signal.​ - F_PU # or F_V # : flowrate of a pump # or a valve # [L/s].​ - P_J # : inlet and outlet pressure for a junction # [meter].

Dataset Details (TL:DR): - There are 43 columns and a 1/0 label column, with 1 meaning that the system is under attack and 0 meaning that the system is in normal operation. - Training Dataset 1: This dataset was released on November 20 2016, and it was generated from a one-year long simulation. The dataset does not contain any attacks, i.e. all the data pertains to C-Town normal operations. - Training Dataset 2: This dataset with partially labeled data was released on November 28 2016. The dataset is around 6 months long and contains several attacks, some of which are approximately labeled. - Test Dataset: This 3-months long dataset contains several attacks but no labels. The dataset was released on February 20 2017, and it is used to compare the performance of the algorithms (see rules document for details).

Notes

• More information about the BATADAL dataset and the 2017-2018 competition can be found in here. Paper of the competition is publicly available [here](https://par.nsf.gov/servlets/purl/1010...

This repository contains supplementary materials for the following conference paper:

V. Švábenský, R. Weiss, J. Cook, J. Vykopal, P. Čeleda, J. Mache, R. Chudovský, A. Chattopadhyay. Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE '22), 2022. DOI: https://doi.org/10.1145/3478431.3499414

The materials include the research dataset, source code, and graphs. For more information about the materials, please see the readme in the attached ZIP file.

If you use or build upon the materials, please use the BibTeX entry below to cite the original work.

@inproceedings{Svabensky2022evaluating, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Weiss, Richard and Cook, Jack and Vykopal, Jan and \v{C}eleda, Pavel and Mache, Jens and Chudovsk\'{y}, Radoslav and Chattopadhyay, Ankur}, title = {{Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises}}, booktitle = {Proceedings of the 53rd ACM Technical Symposium on Computer Science Education}, series = {SIGCSE 2022}, location = {Providence, RI, USA}, year = {2022}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, numpages = {7},
isbn = {978-1-4503-9070-5}, doi = {10.1145/3478431.3499414}, }

The average cyber attack takes 280 days to identify and contain and it costs an average of about $3.86 million to deal with properly.

The AI in Cybersecurity Market estimated size and share is projected to exceed USD 219.53 billion by 2034, with a forecasted CAGR of 24.1% during the period.

Some industries are affected by cyber attacks more than others. These next cybersecurity statistics detail specifically who is affected by cyber-attacks and why they are.

Pay attention to the following cybersecurity statistics to learn how to protect yourself from attacks.