In 2024, over 193,000 individuals in the United States reported encountering phishing attacks. This figure had decreased compared to the previous year, when the number of phishing attacks nationwide amounted to nearly 300,000. However, in 2020 and 2019, this number was relatively low, around 241 thousand and 114 thousand, respectively.

Phishing attacks on businesses increased in 2019 with the United States having the highest increase in attacks. According to a survey of IT security professionals, 57 percent of U.S. respondents stated that their organization had experienced an increased rate of phishing attacks compared to the previous year. Only 29 percent of responding professionals from France stated the same.

The statistic presents the distribution of spear phishing e-mail attacks in Japan from 2013 to 2018, broken down by modus operandi. In 2018, the Japanese police confirmed that ** percent of total spear phishing e-mail attacks were of an indiscriminate style, up from a ** percent share in 2013.

Analysis of ‘Phishing Dataset for Machine Learning’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shashwatwork/phishing-dataset-for-machine-learning on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Anti-phishing refers to efforts to block phishing attacks. Phishing is a kind of cybercrime where attackers pose as known or trusted entities and contact individuals through email, text or telephone and ask them to share sensitive information. Typically, in a phishing email attack, and the message will suggest that there is a problem with an invoice, that there has been suspicious activity on an account, or that the user must login to verify an account or password. Users may also be prompted to enter credit card information or bank account details as well as other sensitive data. Once this information is collected, attackers may use it to access accounts, steal data and identities, and download malware onto the user’s computer.

Content

This dataset contains 48 features extracted from 5000 phishing webpages and 5000 legitimate webpages, which were downloaded from January to May 2015 and from May to June 2017. An improved feature extraction technique is employed by leveraging the browser automation framework (i.e., Selenium WebDriver), which is more precise and robust compared to the parsing approach based on regular expressions.

Anti-phishing researchers and experts may find this dataset useful for phishing features analysis, conducting rapid proof of concept experiments or benchmarking phishing classification models.

Acknowledgements

Tan, Choon Lin (2018), “Phishing Dataset for Machine Learning: Feature Evaluation”, Mendeley Data, V1, doi: 10.17632/h3cgnj8hft.1 Source of the Dataset.

--- Original source retains full ownership of the source dataset ---

Estimates from Crime Survey for England and Wales (CSEW) on fraud and computer misuse. Also data from Home Office police recorded crime on the number of online offences recorded by the police and Action Fraud figures broken down by police force area.

These tables were formerly known as Experimental tables.

Please note: This set of tables are no longer produced. All content previously released within these tables has, or will be, redistributed among other sets of tables.

In 2023, the most common type of cyber crime reported to the United States internet Crime Complaint Center was phishing and spoofing, affecting approximately 298 thousand individuals. In addition, over 55 thousand cases of personal data breaches cases were reported to the IC3 during that year. Dynamic of phishing attacks Over the past few years, phishing attacks have increased significantly. In 2023, almost 300 thousand individuals fell victim to such attacks. The highest number of phishing scam victims since 2018 was recorded in 2021, approximately 324 thousand.Phishing attacks can take many shapes. Bulk phishing, smishing, and business e-mail compromise (BEC) are the most common types. In 2023, 76 percent of the surveyed worldwide organizations reported encountering bulk phishing attacks, while roughly three in four were targeted by smishing scams. Impact of phishing attacks Among the most targeted industries by cybercriminals are healthcare, financial, manufacturing, and education institutions. An observation carried out in the first quarter of 2023 found that social media was most likely to encounter phishing attacks. According to the reports, almost a quarter of them stated being targeted by a phishing scam in the measured period. Very often, phishing e-mails contain a crucial risk for the organization. Almost three in ten worldwide organizations that have experienced phishing attacks suffered from a customer or a client data breach as a consequence. Phishing scams that delivered ransomware infections were also common for the surveyed organizations.

In 2024, the most common type of cybercrime reported to the United States internet Crime Complaint Center was phishing, with its variation, spoofing, affecting approximately 193,000 individuals. In addition, over 86,000 cases of extortion were reported to the IC3 during that year. Dynamic of phishing attacks Over the past few years, phishing attacks have increased significantly. In 2024, over 193,000 individuals fell victim to such attacks. The highest number of phishing scam victims since 2018 was recorded in 2021, approximately 324 thousand.Phishing attacks can take many shapes. Bulk phishing, smishing, and business e-mail compromise (BEC) are the most common types. With the recent development of generative AI, it has become easier to craft a believable phishing e-mail. This is currently among the top concerns of organizations leaders. Impact of phishing attacks Among the most targeted industries by cybercriminals are healthcare, financial, manufacturing, and education institutions. An observation carried out in the fourth quarter of 2024 found that software-as-a-service (SaaS) and webmail was most likely to encounter phishing attacks. According to the reports, almost a quarter of them stated being targeted by a phishing scam in the measured period.

Dataset Title: Cybersecurity Threat Detection and Awareness Program Dataset (2018-2024)

Description: This dataset provides a comprehensive collection of cybersecurity events and network traffic data, spanning from January 2018 to March 2024, collected from real-world corporate environments in Texas, USA. The data includes a diverse range of cybersecurity incidents, covering normal activity as well as various types of threats. It was gathered from multiple sources, such as network traffic logs, system logs, and external threat intelligence feeds, making it suitable for developing machine learning models aimed at threat detection, incident response, and cybersecurity awareness improvement.

The dataset is well-suited for research and experimentation in threat intelligence, intrusion detection, cybersecurity awareness training, and anomaly detection. The included features allow for the modeling of various threat scenarios and multi-class classification tasks. The labeled data provides information on the severity and type of threats detected, supporting both supervised and unsupervised learning techniques.

Features Overview:

Date_Time: The timestamp of the event (e.g., 2022-05-01 14:30:00), indicating when the activity or incident occurred.

Source_IP: IP address of the originating device involved in the event (e.g., 192.168.1.1).

Destination_IP: IP address of the target device involved in the event (e.g., 10.0.0.5).

Source_Port: Port number on the originating device (e.g., 443).

Destination_Port: Port number on the target device (e.g., 80).

Protocol_Type: The protocol used for the communication, such as TCP, UDP, ICMP.

Flow_Duration: Duration of the network flow in milliseconds.

Packet_Size: The size of the packet in bytes.

Flow_Bytes/s: The number of bytes transmitted per second during the flow.

Flow_Packets/s: The number of packets transmitted per second during the flow.

Total_Forward_Packets: Total number of packets sent in the forward direction.

Total_Backward_Packets: Total number of packets sent in the reverse direction.

Packet_Length_Mean: Average packet length for the flow.

IAT_Forward: Inter-arrival time for packets in the forward direction.

IAT_Backward: Inter-arrival time for packets in the reverse direction.

Active_Duration: Duration of active time for the connection.

Idle_Duration: Duration of idle time for the connection.

IDS_Alert_Count: Number of intrusion detection system alerts triggered during the event.

Anomaly_Score: A score indicating the anomaly level of the event, derived from anomaly detection algorithms.

Attack_Vector: Type of attack vector used (e.g., Phishing, DDoS, Brute Force).

Attack_Severity: Severity of the detected threat, categorized as Low, Medium, High, or Critical.

Compromised_Hosts_Count: Number of hosts compromised during the event.

Botnet_Family: Family of botnet detected (if applicable), such as Mirai, Zeus.

Malware_Type: Type of malware detected, such as Ransomware, Trojan.

User_Login_Attempts: Number of login attempts during the event.

Geolocation: Geographic location of the originating IP (Country, City).

Device_Type: Type of device involved (e.g., Server, Router, Mobile).

Firewall_Logs: Binary indicator (0 or 1) showing whether firewall logs flagged the activity.

Antivirus_Alerts: Binary indicator (0 or 1) showing whether antivirus software detected a threat.

Open_Ports_Count: Number of open ports on the target device.

Reputation_Score: A score indicating the reputation of the IP/domain based on threat intelligence sources.

Blacklisted_IP: Binary indicator (0 or 1) indicating if the IP is listed on a blacklist.

Known_Vulnerability: Binary indicator (0 or 1) showing if the target system has known vulnerabilities (based on CVE).

Threat_Intelligence_Source: Source from which the threat intelligence information was gathered.

System_Patch_Status: Indicates whether the system is patched (Up-to-date, Outdated).

CPU_Utilization: CPU usage percentage during the event.

Memory_Utilization: Memory usage percentage during the event.

Employee_Training_Completion: Completion rate of cybersecurity awareness training for the employee involved.

Phishing_Simulation_Success: Result of phishing simulation attempts (Success, Failure).

Reported_Incidents: Number of cybersecurity incidents reported by the user.

Incident_Response_Time: Time taken to respond to the incident in minutes.

Label (Target Variable):

Threat_Severity: The severity level of the threat, categorized as: 0: No Threat 1: Low-Level Threat 2: Medium-Level Threat 3: High-Level Threat 4: Critical Threat Usage: This dataset is ideal for training and testing machine learning models for tasks such as:

Multi-class classification for threat detection. Anomaly detection. Predictive modeling for incident response prioritization. Cybersecurity awareness program improvement. Researchers and...

Fraud detected in Defence SA from July 2018 to June 2021.

Dataset

This dataset was created by PMR3508-2018-b0013d9fad

Contents

Snapshot img

Internet Security Market Size 2024-2028

The internet security market size is forecast to increase by USD 18.63 billion at a CAGR of 8.53% between 2023 and 2028. The market is witnessing significant growth due to the increasing number of cyber threats targeting large enterprises. With the rise of digital technologies, there is a growing need for advanced network security solutions to protect against hacking, phishing, and other malicious activities. The adoption of BYOD (Bring Your Own Device) policies, remote work, and digital transactions has created new security gaps, making it essential for organizations to invest in specialized expertise and data protection systems. Managed Security Service providers (MSSPs) are gaining popularity as they offer cost-effective threat protection and digital privacy systems. The healthcare sector, in particular, is under immense pressure to secure customer healthcare records from breaches. As digital technologies continue to evolve, it is crucial for businesses to stay updated and implement strong security measures to safeguard their assets.

What will be the Size of the Market During the Forecast Period?

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The Market is a dynamic and evolving industry that focuses on protecting digital technologies, e-commerce platforms, and critical infrastructure from cyberattacks. The market encompasses various solutions such as network security, machine learning, artificial intelligence, and advanced security solutions. Digital transactions and remote work have increased the risk of digital attacks, including data breaches, phishing, malware, and hacking. Enterprise security solutions are in high demand, particularly in sectors like healthcare, where sensitive data is a priority. Cloud technologies and virtual private network have revolutionized the way businesses operate, leading to an increased focus on cloud security. The Internet of Things (IoT) has introduced new vulnerabilities, managed security service necessitating advanced security solutions.

Further, data protection is a major concern, with machine learning and artificial intelligence being used to detect and prevent cyberattacks. Certified Ethical Hacking and other cybersecurity certifications are essential for professionals in the field. Antivirus, antimalware, intrusion detection, and security information are fundamental security solutions that continue to be relevant. In conclusion, the Market is a vital industry that addresses the ever-evolving threat landscape of digital technologies such as wireless router. It offers a range of solutions to protect against digital attacks, including network security solutions, machine learning, artificial intelligence, and advanced security solutions. The market is expected to grow as businesses and individuals continue to rely on digital technologies for transactions, communication, and data storage.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Solution

  Products
  Services


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Solution Insights

The products segment is estimated to witness significant growth during the forecast period. In the market, large enterprises are investing heavily in advanced network security solutions to mitigate cyber threats and protect digital privacy systems. The demand for security services is on the rise, particularly in sectors with significant customer healthcare records and digital transactions, such as healthcare and finance.

The adoption of digital technologies for remote work and digital transactions has exposed new security gaps, leading to an increased need for specialized expertise in threat protection. hardware security components, including firewalls and intrusion detection and prevention systems, are seeing increased sales due to their ability to secure network infrastructures. Security software, which automates and enhances network monitoring, is also gaining popularity, especially in the automotive and healthcare sectors, where easy integration with IoT applications is crucial.

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The products segment accounted for USD 19.37 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

APAC is estimated to contribute 37% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in the US is witn

Annual Report data on instances of fraud in DHS Dataset includes: Data from 2012-13 financial year through to 2018-19 financial year Source: DHS Incident Management Unit Includes data from …Show full descriptionAnnual Report data on instances of fraud in DHS Dataset includes: Data from 2012-13 financial year through to 2018-19 financial year Source: DHS Incident Management Unit Includes data from predecessor agency Department for Communities and Social Inclusion (DCSI).

Spam messages accounted for 45.1 percent of e-mail traffic in March 2021. During the most recently measured period, Russia generated the largest share of unsolicited spam e-mails with 23.52 percent of global spam volume. Despite its ubiquity, the global e-mail spam rate has actually been decreasing: the global annual spam e-mail rate in 2018 was 55 percent, down from 69 percent in 2012. Spam e-mail It is almost impossible to think about e-mail without considering the issue of spam. In 2019, 293.6 billion e-mails were sent and received on a daily basis. This includes billions of promotional e-mails sent by marketers every day. Whilst many e-mail users believe such content belongs in their spam folder, marketing e-mails are generally harmless, if annoying to the user. In 2018, the spam placement rate of commercial e-mails had declined to nine percent, down from 14 percent in 2017.

Malicious spam Not all spam are benign promotional e-mails though. A significant portion of spam messages are of a more malicious nature, aiming to damage or hijack user systems. The most common variants of malicious spam worldwide include trojans, spyware, and ransomware.

Snapshot img

Network Security Appliance Market Size 2024-2028

The network security appliance market size is forecast to increase by USD 7.95 billion, at a CAGR of 8.2% between 2023 and 2028. The network security appliance market is experiencing significant growth due to the increasing demand for advanced security solutions. With the rise in cyber threats and data breaches, organizations in various sectors such as healthcare, energy and utilities are investing in intrusion prevention systems and web security to safeguard their network infrastructure. Professional and managed services are also gaining popularity as organizations seek expert assistance in implementing and managing these security solutions. However, the implementation process can pose challenges, including potential failures, which underscores the importance of selecting reliable security companies. Intrusions and cyberattacks continue to be major concerns, necessitating the adoption of comprehensive security measures. Key trends in the market include the integration of advanced technologies such as artificial intelligence and machine learning to enhance threat detection and response capabilities.

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Network security appliances play a crucial role in safeguarding network infrastructure against cyber threats, ensuring data confidentiality, integrity, and availability for various industrial verticals. With the increasing prevalence of cybercrimes, network security has become an essential aspect of IT infrastructure management. Cybersecurity threats, such as intrusions, data breaches, DDoS attacks, ransomware, malware, phishing, and others, pose significant risks to businesses. These threats can lead to financial losses, reputational damage, and regulatory non-compliance. Network security appliances offer advanced security solutions to mitigate these risks and provide visibility analytics for effective security management. Industrial verticals, including telecommunications, healthcare, finance, and retail, rely on strong network security technologies to protect their critical IT infrastructures. Network security appliances provide access controls and intrusion detection systems to prevent unauthorized access and detect potential intrusions. Security management software integrated with network security appliances offers advanced features, such as real-time threat detection, automated response, and reporting capabilities.

Further, these features enable organizations to respond quickly and effectively to cyber threats, reducing the impact of potential data breaches. Network security appliances also offer protection against various types of cyber threats, including phishing attacks, denial of service attacks, and advanced persistent threats. By implementing network security appliances, organizations can strengthen their cybersecurity posture and minimize the risk of cyberattacks. In conclusion, network security appliances are essential for network infrastructure protection in industrial verticals. They offer advanced security solutions to mitigate various cyber threats, provide visibility analytics for effective security management, and enable organizations to respond quickly and effectively to potential security breaches. By investing in network security appliances, organizations can safeguard their critical IT infrastructures and protect against data confidentiality, integrity, and availability risks.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

End-user

  Telecom and manufacturing
  Government
  BFSI
  Healthcare
  Others


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  Middle East and Africa



  South America

By End-user Insights

The telecom and manufacturing segment is estimated to witness significant growth during the forecast period. Telecommunication companies generate vast amounts of data, necessitating the use of network security appliances for effective data management. These appliances, including firewalls and Unified Threat Management (UTM) systems, are crucial for large enterprises and small-medium enterprises (SMEs) in the telecommunications sector. Network security appliances enable data protection and implement cybersecurity measures against cyber threats. Network Management tools integrated into these appliances provide insights into network performance and facilitate risk management tasks. Data protection is a significant concern for telecommunication companies, and network security appliances play a vital role in safeguarding sensitive information.

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The telecom and manufacturing segment was

The graph shows the average e-mail spam placement rates worldwide in 2018, by category. The source found that **** percent of commercial e-mails advertising insurance products and services were considered spam by inbox providers in 2018. The highest spam placement rate was recorded for education/non profit/government e-mails.

Synthetic transactional data with labels for fraud detection. For more information, see: https://www.kaggle.com/ntnu-testimon/paysim1/version/2

Snapshot img

Email Security Market Size 2025-2029

The email security market size is forecast to increase by USD 6.03 billion, at a CAGR of 14.2% between 2024 and 2029.

The market is experiencing significant growth and transformation, driven by the increasing trend towards remote work and employee mobility. This shift has led to an escalating need for robust email security solutions to protect sensitive business information from cyber threats. Another key trend is the widespread adoption of cloud-based email security services, enabling organizations to benefit from scalability, flexibility, and cost savings. However, the availability of open-source email security solutions poses a challenge for market players, as they must differentiate their offerings and provide added value to compete effectively. Companies seeking to capitalize on market opportunities should focus on offering advanced threat detection and response capabilities, seamless integration with other security solutions, and user-friendly interfaces. Navigating the challenges requires a deep understanding of the evolving threat landscape and the ability to adapt quickly to new technologies and customer needs.

What will be the Size of the Email Security Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the ever-evolving email security landscape, entities continue to integrate various solutions to fortify their defenses against persistent threats. On-premise security, incident response, PCI DSS compliance, artificial intelligence, antivirus protection, threat modeling, penetration testing, access control, and security policy are among the essential components of a robust email security strategy. The market witnesses continuous dynamism, with advancements in areas such as spam detection, malware scanning, email authentication protocols, multi-factor authentication, email filtering, behavioral analytics, security audits, and security software. Threat intelligence, intrusion detection, security services, machine learning, data encryption, phishing prevention, email security appliances, email security gateways, business continuity, single sign-on, vulnerability management, email archiving, risk management, email encryption, hybrid cloud security, and cloud security are other critical aspects of this evolving market. As cybercriminals adopt increasingly sophisticated tactics, organizations must remain vigilant and adapt their email security strategies accordingly. Continuous threat modeling, penetration testing, and vulnerability assessments are essential to identify and address potential weaknesses. Security awareness training is also crucial to ensure that employees are equipped to recognize and respond to phishing attempts and other social engineering attacks. The integration of AI and machine learning technologies is revolutionizing email security, enabling more effective threat detection and response. However, this also presents new challenges, such as the need for robust data encryption and privacy protections. As the market continues to evolve, organizations must stay informed and adapt their strategies to stay ahead of emerging threats.

How is this Email Security Industry segmented?

The email security industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ApplicationBFSIGovernmentHealthcareIT and telecomOthersComponentProductsServicesDeploymentCloudOn-premisesHybridThreat TypePhishingMalwareSpamData Loss PreventionGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Application Insights

The bfsi segment is estimated to witness significant growth during the forecast period.The market is witnessing significant growth, particularly in the Banking, Financial Services, and Insurance (BFSI) sector. With the increasing use of digital technologies like cloud computing, mobile banking, and internet banking, financial institutions are becoming prime targets for sophisticated cyber threats such as phishing, ransomware, and business email compromise (BEC). Email security solutions are essential to protect sensitive financial data and maintain customer trust. Advanced measures like encryption, multi-factor authentication (MFA), and AI-driven threat detection systems are being adopted to safeguard communication channels. Email security appliances and gateways, security software, and security services are crucial components of these solutions. Threat intelligence, machine learning, and behavioral analytics are integral to proactively identifying and mitigating potential threats.

What are the average email marketing results in different countries? Here’s what we’ve found.

Credit Card Fraud Detection Dataset (European Cardholders, September 2013)

As a data contributor, I'm sharing this crucial dataset focused on the detection of fraudulent credit card transactions. Recognizing these illicit activities is paramount for protecting customers and the integrity of financial systems.

About the Dataset:

This dataset encompasses credit card transactions made by European cardholders during a two-day period in September 2013. It presents a real-world scenario with a significant class imbalance, where fraudulent transactions are considerably less frequent than legitimate ones. Out of a total of 284,807 transactions, only 492 are instances of fraud, representing a mere 0.172% of the entire dataset.

Content of the Data:

Due to confidentiality concerns, the majority of the input features in this dataset have undergone a Principal Component Analysis (PCA) transformation. This means the original meaning and context of features V1, V2, ..., V28 are not directly provided. However, these principal components capture the variance in the underlying transaction data.

The only features that have not been transformed by PCA are:

• Time: Numerical. Represents the number of seconds elapsed between each transaction and the very first transaction recorded in the dataset.
• Amount: Numerical. The transaction amount in Euros (€). This feature could be valuable for cost-sensitive learning approaches.

The target variable for this classification task is:

• Class: Integer. Takes the value 1 in the case of a fraudulent transaction and 0 otherwise.

Important Note on Evaluation:

Given the substantial class imbalance (far more legitimate transactions than fraudulent ones), traditional accuracy metrics based on the confusion matrix can be misleading. It is strongly recommended to evaluate models using the Area Under the Precision-Recall Curve (AUPRC), as this metric is more sensitive to the performance on the minority class (fraudulent transactions).

How to Use This Dataset:

1. Download the dataset file (likely in CSV format).
2. Load the data using libraries like Pandas.
3. Understand the class imbalance: Be aware that fraudulent transactions are rare.
4. Explore the features: Analyze the distributions of 'Time', 'Amount', and the PCA-transformed features (V1-V28).
5. Address the class imbalance: Consider using techniques like oversampling the minority class, undersampling the majority class, or using specialized algorithms designed for imbalanced datasets.
6. Build and train binary classification models to predict the 'Class' variable.
7. Evaluate your models using AUPRC to get a meaningful assessment of performance in detecting fraud.

Acknowledgements and Citation:

This dataset has been collected and analyzed through a research collaboration between Worldline and the Machine Learning Group (MLG) of ULB (Université Libre de Bruxelles).

When using this dataset in your research or projects, please cite the following works as appropriate:

• Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015.
• Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon.
• Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE.
• Andrea Dal Pozzolo. Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi).
• Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-Aël Le Borgne, Olivier Caelen, Yannis Mazzer, Gianluca Bontempi. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier.
• Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Gianluca Bontempi. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing.
• Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019.
• Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi *Combining Unsupervised and Supervised...