In the fourth quarter of 2024, around 512,000 DDoS attacks were registered worldwide. The figure has gradually increased from 274,000 incidents in the first quarter of 2023.

In 2023, organizations in the finance industry worldwide saw the highest share of DDoS attacks, ** percent. The finance industry ranked second, with over ** percent, while healthcare followed, with **** percent.

In 2021, around ** percent of distributed denial of service (DDoS) attacks were directed at the United States. The United Kingdom took the second place, and China the third place with just below ** percent of attacks. The computer and internet industry are most frequently targeted.

The Real-Time DDoS Traffic Dataset for ML is designed to support the development, testing, and validation of machine learning models focused on detecting Distributed Denial of Service (DDoS) attacks in real-time. As cybersecurity threats evolve, particularly in the realm of network traffic anomalies like DDoS, having access to labeled data that mirrors real-world attack scenarios is essential. This dataset aims to bridge this gap by providing comprehensive, structured network traffic data that includes both normal and DDoS attack instances, facilitating machine learning research and experimentation in DDoS detection and prevention.

The dataset is compiled from network traffic that either replicates real-time conditions or is simulated under carefully controlled network configurations to generate authentic DDoS attack traffic. This data encompasses variations in packet transmission and byte flow, which are key indicators in distinguishing between typical network behavior and DDoS attack patterns. The primary motivation behind this dataset is to aid machine learning practitioners and cybersecurity experts in training models that can effectively differentiate between benign and malicious traffic, even under high-stress network conditions.

Data Source and Collection: Include information on how the data was collected, whether it was simulated or recorded from real systems, and any specific tools or configurations used.

Dataset Structure: List and explain the features or columns in the dataset. For instance, you might describe columns such as:

• traffic_type: Indicates whether the traffic is normal or DDoS.
• packet_count: The number of packets in a session.
• packet_count_per_second: The rate of packets over time.
• byte_count: Total data in bytes for a session.
• byte_count_per_second: The data transfer rate over time.

This dataset is ideal for a range of applications in cybersecurity and machine learning:

1.Training DDoS Detection Models: The dataset is specifically structured for use in supervised learning models that aim to identify DDoS attacks in real time. Researchers and developers can train and test models using the labeled data provided.

2.Real-Time Anomaly Detection: Beyond DDoS detection, the dataset can serve as a foundation for models focused on broader anomaly detection tasks in network traffic monitoring.

3.Benchmarking and Comparative Studies: By providing data for both normal and attack traffic, this dataset is suitable for benchmarking various algorithms, allowing comparisons across different detection methods and approaches.

4.Cybersecurity Education: The dataset can also be used in educational contexts, allowing students and professionals to gain hands-on experience with real-world data, fostering deeper understanding of network anomalies and cybersecurity threats.

Limitations and Considerations While the dataset provides realistic DDoS patterns, it is essential to note a few limitations:

Data Origin: The dataset may contain simulated attack patterns, which could differ from real-world DDoS attack traffic in more complex network environments.

Sampling Bias: Certain features or types of attacks may be overrepresented due to the specific network setup used during data collection. Users should consider this when generalizing their models to other environments.

Ethical Considerations: This dataset is intended for educational and research purposes only and should be used responsibly to enhance network security.

Acknowledgments This dataset is an open-source contribution to the cybersecurity and machine learning communities, and it is designed to empower researchers, educators, and industry professionals in developing stronger defenses against DDoS attacks.

2018.

Context

This is a dataset of DDoS Botnet attacks from IOT devices.

Content

Contains all features about packets from bots.

Inspiration:

For making DDoS attack preventable.

The finance industry was the major target of DDoS attacks in Russia in 2023. Furthermore, the share of attacks on e-commerce companies was almost 25 percent. DDoS, or distributed denial-of-service attacks involve sending a high number of requests to a target network or website, more than it is capable of handling, leading to the disruption in the resource's functioning.

Scoreboard dataset contains statistical data for each second during the testing period. Scoreboard represents srcip:srcport-dstip:dstport pair with statistics count for the number of packets, protocol identification, flag (if it is TCP), and a number of SYN packets (if it is TCP). Each ip:port pair represents one communication channel.

In today's interconnected digital landscape, the DDoS (Distributed Denial of Service) Protection and Mitigation Hardware market plays an essential role in safeguarding organizations from the ever-growing threat of cyberattacks. DDoS attacks can disrupt services and cause significant financial and reputational losses

Distributed Denial of Service (DDoS) attack is a menace to network security that aims at exhausting the target networks with malicious traffic. Although many statistical methods have been designed for DDoS attack detection, designing a real-time detector with low computational overhead is still one of the main concerns. On the other hand, the evaluation of new detection algorithms and techniques heavily relies on the existence of well-designed datasets. In this paper, first, we review the existing datasets comprehensively and propose a new taxonomy for DDoS attacks. Secondly, we generate a new dataset, namely CICDDoS2019, which remedies all current shortcomings. Thirdly, using the generated dataset, we propose a new detection and family classification approach based on a set of network flow features. Finally, we provide the most important feature sets to detect different types of DDoS attacks with their corresponding weights.

The dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017

original paper link: https://ieeexplore.ieee.org/abstract/document/8888419 kaggle dataset link: https://www.kaggle.com/datasets/dhoogla/cicddos2019

particularly those executed by bots

NCSRD-DS-5GDDos v2.0 Dataset
===
NCSRD-DS-5GDDos is a comprehensive dataset recorded in a real-world 5G testbed that aligns with the 3GPP specifications. The dataset captures Distributed Denial of Service (DDoS) attacks initiated by malicious connected users (UEs).

The setup comprises of 3 cells with a total of 9 UEs connected to the same core network. The 5G network is implemented by the Amarisoft Callbox Mini solution (cell 2), and we further employ a second cell using the Amarisoft Classic (cell 1 & 3), that also hosts the 5G core.

The setup utilizes a broad set of UE devices comprising a set of smart phones (Huawei P40), microcomputers (Raspberry Pi 4 - Waveshare 5G Hat M2), industrial 5G routers (Industrial Waveshare 5G Router), a WiFi-6 mobile hotspot (DWR-2101 5G Wi-Fi 6 Mobile Hotspot) and a CPE box (Waveshare 5G CPE Box). All UEs are being operated by subsidiary hosts which are responsible for the traffic generation, occurring from scheduled communications times.

All identifiers are artificially generated and do not represent or based on personal data. We identify each UE through its ‘imeisv’ ID, that corresponds to the device in use, due to vendor implementation, that uses the same IMSI for all UEs.

This dataset captures attack data from a total of 5 malicious User Equipment (UE) devices that initiated various flooding attacks on a 5G network. Each record includes key identifiers such as the IMEISV (International Mobile Equipment Identity Software Version number) and IP address of the attacking UE, along with the device type. The file "summary_report.csv" summarizes this information. The traffic types used in the attacks include syn flooding, UDP flooding, ICMP flooding, DNS flooding, and GTP-U flooding. The benign users stream YouTube and Skype traffic.

The dataset is recorded through the use of a data collector that interfaces with the 5G network and gathers data regarding UEs, gNBs and the Core Network. The data are recorded in an InfluxdB and pre-processed into three separate tabular .csv files for more efficient processing: “amari_ue_data.csv”, “enb_counters.csv” and “mme_counters.csv”. In this version, we use an Amarisoft Classic (cells 1 & 3, Core Network) and an Amarisoft Mini (cell 2) (more information on the products can be found in https://www.amarisoft.com/).

The ”amari_ue_data.csv” provides information on the UEs regarding identification (“imeisv”, “5g_tmsi”, “rnti”), IP addressing, bearer information, cell information (“tac”, “ran_plmn”), and cell information (“ul_bitrate”, “dl_bitrate”, “cell_id”, retransmissions per user per cell “ul_retx” as well as aggregated bit rates for each cell).

The ”enb_counters.csv” focuses on cell-level information, providing downlink and uplink bitrates, usage ratio per user, cpu load of the gNB.

We provide separate files of ”amari_ue_data.csv” and ”enb_counters.csv” generated from each gNB (Amarisoft Classic and Mini).

The “mme_counters.csv” provides information on the Non-Access Stratum (NAS) of the 5G Network and focuses on session status reports (e.g., number of PDU session establishments, paging, context setup. This part gives an overview of the connection management throughout the recording session, and provides information on features suggested by 3GPP for abnormal user behavior.

We also provide a separate pre-processed dataset, that merges the two "amari_ue_data_*.csv" file, including labeling of the malicious/benign samples, and may be more flexible for interested data scientists.

Please refer to README.txt for the features included in each file.

a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore

The statistic shows the distributed denial-of-service (DDoS) and bot protection software market share as of February 2024, by vendor. As of then, Cloudflare Security had the greatest market share worldwide, which stood at 82.16 percent.

The DDoS protection market is experiencing robust growth, driven by the escalating frequency and sophistication of distributed denial-of-service (DDoS) attacks targeting businesses and critical infrastructure globally. The market's expansion is fueled by several key factors: the increasing reliance on cloud services and digital infrastructure, the proliferation of IoT devices creating larger attack surfaces, and the evolution of DDoS attack methodologies towards more complex and evasive techniques. Significant investments in advanced security solutions are being made by organizations across various sectors, including finance, healthcare, and e-commerce, to mitigate the financial and reputational damage caused by successful DDoS attacks. The market is segmented by application (mobile, data center, government & carrier transport) and attack type (UDP flood, ICMP flood, SYN flood, HTTP flood, others), with data centers and cloud providers representing substantial market segments due to their concentration of critical assets. Geographic distribution shows strong growth across North America and Asia-Pacific, driven by higher levels of internet penetration and adoption of cloud-based services in these regions. Competitive landscape features established players like F5 Networks, Akamai Technologies, and Cloudflare, alongside emerging security specialists offering innovative solutions. Continued innovation in AI-powered threat detection and mitigation, coupled with the growing demand for comprehensive security solutions, promises sustained market expansion in the coming years. While precise market sizing data wasn't provided, reasonable estimation based on industry reports suggests a current market value exceeding $5 billion USD. Considering the factors above, a Compound Annual Growth Rate (CAGR) of 15% seems plausible over the forecast period (2025-2033), implying significant growth opportunities. This expansion is moderated by factors such as the cost of implementing advanced DDoS protection solutions and the ongoing challenge of staying ahead of evolving attack vectors. Nevertheless, the rising sophistication of cyberattacks and the escalating consequences of service disruptions will likely drive consistent demand for robust DDoS protection, sustaining the market's positive trajectory. The increasing adoption of hybrid cloud models and the expansion of 5G networks are expected to further fuel market growth, presenting significant opportunities for vendors offering comprehensive solutions that address the complexities of distributed infrastructure.

To cite the dataset please reference it as Y. Kim, S. Hakak, and A. Ghorbani. "DDoS Attack Dataset (CICEV2023) against EV Authentication in Charging Infrastructure," in 2023 20th Annual International Conference on Privacy, Security and Trust (PST), IEEE Computer Society, pp. 1-9, August 2023.

Explore a comprehensive dataset capturing DDoS attack scenarios within electric vehicle (EV) charging infrastructure. This dataset features diverse machine learning attributes, including packet access counts, system status details, and authentication profiles across multiple charging stations and grid services. Simulated attack scenarios, authentication protocols, and extensive profiling results offer invaluable insights for training and testing detection models in safeguarding EV charging systems against cyber threats.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5737185%2F2dec3a047fec426e0b6d2f7672d25016%2Fadjusted-5221113.jpg?generation=1743055158796994&alt=media" alt=""> Figure 1: Proposed simulator structure, source: Y. Kim, S. Hakak, and A. Ghorbani.

Acknowledgment :

The authors sincerely appreciate the support provided by the Canadian Institute for Cybersecurity (CIC), as well as the funding received from the Canada Research Chair and the Atlantic Canada Opportunities Agency (ACOA).

Reference :

Y. Kim, S. Hakak, and A. Ghorbani. "DDoS Attack Dataset (CICEV2023) against EV Authentication in Charging Infrastructure," in 2023 20th Annual International Conference on Privacy, Security and Trust (PST), IEEE Computer Society, pp. 1-9, August 2023.

Distributed Denial-Of-Service (DDoS) Protection Market Outlook

The global Distributed Denial-Of-Service (DDoS) Protection market size was valued at approximately USD 3.41 billion in 2023 and is projected to reach around USD 11.02 billion by 2032, advancing at a compound annual growth rate (CAGR) of 14.01% from 2024 to 2032. The market is experiencing substantial growth driven by the increasing frequency and sophistication of DDoS attacks, which have heightened the need for robust protection solutions across various sectors. As businesses continue to expand their digital presence, the threat landscape becomes more complex, necessitating advanced protective measures to safeguard critical infrastructure and data.

The surge in digital transformation initiatives by enterprises is a significant growth factor for the DDoS protection market. As organizations increasingly shift to digital channels, there is a corresponding rise in the volume and complexity of cyber threats, including DDoS attacks. The transition to cloud-based services and the adoption of IoT devices have expanded the attack surface, making it imperative for companies to invest in efficient DDoS protection to maintain service availability and prevent costly downtime. Moreover, the growing recognition of cybersecurity as a board-level concern has led to increased spending on protective measures, further propelling market growth.

Regulatory compliance is another crucial driver for the DDoS protection market. Governments worldwide are implementing stringent regulations to protect sensitive data and ensure cybersecurity resilience across industries. Organizations are compelled to adhere to these regulations, which often mandate the implementation of comprehensive security measures, including DDoS protection. Compliance with standards such as the General Data Protection Regulation (GDPR) and the Cybersecurity Maturity Model Certification (CMMC) necessitates investment in robust security solutions, thereby fueling the demand for DDoS protection services and solutions.

The escalation of DDoS attacks targeting critical infrastructure and high-profile events is also contributing to market growth. Sectors such as BFSI, healthcare, and government are particularly vulnerable to these attacks, given the sensitive nature of the data they handle. The disruption caused by DDoS attacks can lead to significant financial losses and reputational damage. Consequently, organizations in these sectors are prioritizing the deployment of advanced DDoS protection solutions to safeguard their operations. The increasing trend of targeted attacks during major events or political elections further underscores the necessity for enhanced protective measures.

Regionally, North America remains a frontrunner in the DDoS protection market due to its well-established IT infrastructure and the presence of major cybersecurity vendors. The region's focus on technological innovation and early adoption of advanced security solutions positions it as a leader in the market. However, Asia Pacific is anticipated to witness the highest growth rate over the forecast period, driven by rapid digitalization, increasing internet penetration, and the rising awareness of cybersecurity threats among businesses. Countries like China and India are at the forefront of this growth, with businesses across sectors enhancing their cybersecurity frameworks to combat escalating threats.

Component Analysis

The DDoS protection market is segmented by components into hardware solutions, software solutions, and services. Hardware solutions provide the physical infrastructure required to filter and mitigate incoming DDoS attacks. These solutions include firewalls, load balancers, and intrusion prevention systems, which are crucial for companies with high-security requirements and those that prefer an on-premises setup. The demand for hardware solutions is driven by sectors such as BFSI and government, which require stringent security protocols to protect sensitive data against complex attack vectors. As enterprises seek to enhance their defensive capabilities, the hardware segment is expected to maintain steady growth.

Software solutions encompass a variety of applications and tools designed to detect, mitigate, and respond to DDoS attacks. These solutions are often preferred for their flexibility and scalability, allowing organizations to tailor their security measures to meet specific needs. The increasing adoption of cloud computing and virtualization has accelerated the demand for software-based DDoS protection solutions, as they can be easily integrated into existin

The DDoS (Distributed Denial of Service) Attack Protection Service market is an essential segment of the cybersecurity landscape, responding to the increasing threat posed by cybercriminals who launch disruptive attacks on businesses and services online. DDoS attacks can incapacitate websites and services by overwhe

DDoS Attack Protection Service Market Outlook

The global DDoS Attack Protection Service market size was valued at approximately USD 3.2 billion in 2023 and is projected to reach nearly USD 7.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.1% during the forecast period. The increasing frequency and sophistication of Distributed Denial of Service (DDoS) attacks, coupled with the expanding digital footprint of enterprises, are significant growth factors for the market.

One of the critical drivers for the DDoS Attack Protection Service market is the exponential growth in internet traffic and the increasing reliance on digital platforms for business operations. As more organizations move their critical operations to the cloud and adopt remote working models, the vulnerability to DDoS attacks has surged. Additionally, the rising incidences of high-profile cyber-attacks have heightened awareness and driven the demand for robust DDoS protection solutions. Companies across various industry verticals are increasingly investing in these services to safeguard their digital assets.

Advancements in technology are another significant market growth factor. The development of sophisticated DDoS protection solutions, such as artificial intelligence (AI) and machine learning (ML)-based systems, has enhanced the ability to detect and mitigate threats in real-time. These advanced systems can analyze vast amounts of data and identify unusual patterns that may indicate an impending attack. This technological progression is expected to accelerate the adoption of DDoS protection services, further propelling market growth.

Regulatory requirements and compliance standards are also contributing factors to market expansion. Governments and regulatory bodies across the globe are imposing stringent cybersecurity regulations that mandate the implementation of adequate protective measures against DDoS attacks. Compliance with these regulations is critical for organizations to avoid hefty fines and reputational damage, prompting them to adopt comprehensive DDoS protection services.

From a regional perspective, North America is anticipated to hold a significant market share owing to the high adoption rate of advanced technologies and the presence of major market players in the region. The Asia Pacific region is expected to witness substantial growth due to the rapid digital transformation and increasing cyber threats in countries such as China, India, and Japan. Europe also demonstrates a strong market potential, driven by stringent data protection regulations and an elevated focus on cybersecurity.

The integration of an Anti DDoS System Ads Platform is becoming increasingly vital for businesses aiming to protect their digital advertising infrastructure. As digital platforms continue to expand, they become more susceptible to DDoS attacks, which can disrupt advertising operations and lead to significant financial losses. By incorporating advanced anti-DDoS systems, companies can ensure the seamless delivery of ads, maintain user engagement, and protect revenue streams. These platforms are designed to detect and mitigate threats in real-time, offering advertisers peace of mind and allowing them to focus on optimizing their campaigns without the constant worry of potential disruptions.

Component Analysis

The DDoS Attack Protection Service market by component is segmented into hardware solutions, software solutions, and services. Hardware solutions include specialized appliances that are designed to detect and mitigate DDoS attacks at the network level. These solutions are often deployed in data centers and are critical for enterprises with high traffic volumes. Companies prefer hardware solutions for their robustness and ability to handle large-scale attacks efficiently. Moreover, advancements in hardware technology have led to the development of more sophisticated and powerful devices capable of providing enhanced protection.

Software solutions, on the other hand, provide a flexible and scalable approach to DDoS attack mitigation. These solutions are designed to be integrated into an organizationÂ’s existing IT infrastructure, offering real-time threat detection and automated response capabilities. Software-based DDoS protection solutions are increasingly popular among organizations due to their ease of deployment and cost-effectiveness. The rise of cloud computing has further fueled the demand for softw

Between 2021 and 2024, the magnitude of the largest DDoS attacks have increased steadily. The attack with the largest breadth was recorded in 2024, reaching two Terabits per second.