The database includes three datasets. All of them were extracted from a dataset published by X (Twitter Transparency Websites) that includes tweets from malicious accounts trying to manipulate public opinion in the Kingdom of Saudi Arabia. Although the propagandist tweets were published by malicious accounts, as X (Twitter) stated, the tweets at their level were not classified as propaganda or not. Propagandists usually mix propaganda and non-propaganda tweets in an attempt to hide their identities. Therefore, it was necessary to classify their tweets as propaganda or not, based on the propaganda technique used. Since the datasets are very large, we annotated a sample of 2,100 tweets. The datasets are made up of 16,355,558 tweets from propagandist users focused on sports and banking topics.

Dataset consisting of feature vectors of 215 attributes extracted from 15,036 applications (5,560 malware apps from Drebin project and 9,476 benign apps). The dataset has been used to develop and evaluate multilevel classifier fusion approach for Android malware detection, published in the IEEE Transactions on Cybernetics paper 'DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection'. The supporting file contains further description of the feature vectors/attributes obtained via static code analysis of the Android apps.

Cyberthreat intelligence (CTI) reports on past cyberattacks describe the sequence of actions of attackers in terms of time. The sequence contains temporal relations among attack actions, such as \textit{a malware is first downloaded and then executed}. Information related to temporal relations enables cybersecurity practitioners to investigate past cyberattack incidents and analyze attackers' behavior. However, cybersecurity practitioners must extract such information automatically, in a structured manner, through a common vocabulary to reduce human effort and enable sharing and collaboration. \textit{The goal of this paper is to aid security practitioners in proactive defense against attacks by automatic information extraction of temporal relations among attack actions from cyberthreat intelligence reports}. We propose \textbf{ChronoCTI}, an automated pipeline for extracting temporal relations among attack actions from CTI reports. The attack actions are represented as MITRE ATT&CK techniques, and the relations are represented as a knowledge graph. To construct \textbf{ChronoCTI}, we build a ground truth dataset of temporal relations and apply large language models, natural language processing, and machine learning techniques. \textbf{ChronoCTI} demonstrates higher precision but lower recall performance on a real-world dataset of 94 CTI reports. \textbf{ChronoCTI} achieves macro precision, recall, and F1 scores of 0.75, 0.46, and 0.54, respectively. ChronoCTI aids practitioners in analyzing large volumes of CTI reports, thinking like attackers, and knowing what malicious actions are likely to happen next, which enables the practitioners to assess imminent threats and strengthen their cybersecurity readiness.

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Cyber Weapon Market Size 2024-2028

The Cyber Weapon Market size is estimated to grow by USD 9.50 billion, at a CAGR of 11.83% between 2023 and 2028. The market is experiencing significant expansion due to various influencing factors. Primarily, the escalating IT security budgets of organizations worldwide reflect the increasing importance placed on safeguarding digital assets. Furthermore, the expanding need for robust infrastructure protection in the face of escalating cyber threats is driving market growth. Additionally, international conflicts have heightened the demand for advanced cyber weapons, as nations seek to gain strategic advantages over their adversaries. These factors collectively contribute to the dynamic and evolving cyber weapon market landscape.

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

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Market Dynamics

In today's interconnected world, the rise of malicious activities poses a significant threat to various sectors, including healthcare, where confidential data and critical information are at risk. Organizations such as Privacy International are closely monitoring surveillance technologies and spyware, recognizing the importance of safeguarding national security and digital infrastructure. With the increasing reliance on IT infrastructure, cyber vulnerabilities have become a pressing concern, leading to potential risks such as terrorism, economic disruption, and cyber espionage. The market landscape depends on Data mining, Machine learning, Semantic analysis, Neural networks, Multivariate statistics, Social media analytics, Skilled professionals, Data security, Privacy concerns, and Experience Cloud. To counter these threats, governments and international organizations have established cyber defense mechanisms, including the Cyber Mission Force and defense budgets allocated for cybersecurity initiatives. However, the corporate sector remains a target for cybercriminals seeking financial gain through security breaches and phishing attacks targeting banks, telecoms, and e-commerce platforms.

Key Market Driver

One of the key factors driving the market growth is the increasing IT security budget. Several organizations across various sectors are increasing their IT budgets due to the increasing number of cybersecurity threats. In addition, there is a significant increase in global spending on IT security budgets across the globe which mainly comprise expenses on cyber security services. For example, global spending on IT security services was around USD 71.68 billion in 2022, which is anticipated to reach approximately USD 76 billion by the end of 2023.

In addition, there is an increase in the number of cyber attacks as there is an increase in the use of websites and web applications by end-users, which fuels the number of instances of identity and data theft. Hence, the increasing complexity of cyber-attacks and the growing awareness of security risks are positively impacting the market. Hence, such factors are expected to drive the market during the forecast period.

Significant Market Trends

One of the major market trends is the growing demand for data safety and security. There is an increase in demand for systems monitoring, hardware, intellectual property, theft, and disposal of targets' data, as well as vital manufacturing and commercial activities fuelled by the market. In addition, there is an increase in demand for control of commercial losses due to factors such as the need for data safety and security.

Moreover, the growing demand across various sectors, such as the military, government, telecommunication, banking, and finance, is positively impacting the market. Furthermore, the personal data in these end-user sectors are highly prone to unexpected damage by cyber hackers. Hence, such factors are driving the market during the forecast period.

Major Market Restraint

The high cost of development is one of the key challenges hindering the market. There is a significant cost associated with the development of cyber weapons as it requires time, resources, and technical expertise. In addition, there are only a handful of companies which has the funds and capacity to help with the production of these complicated technologies.

Moreover, as research is highly confidential, it is inconceivable for potential clients to think about the cost and capacities of these frameworks. Hence, such factors are negatively impacting the market which in turn will hinder the market growth during the forecast period.

Market Segmentation

The defensive segment is estimated to witness significant growth during the forecast period. There is an increasing deployment of virus malware as defensive cyber weapons to prevent the theft of intellectual property or the erasure of data and systems. Factors such as the increase in threats to critical infrastructure in important industries l

On-Belt Threat Detection AI Market Outlook

According to our latest research, the global on-belt threat detection AI market size reached USD 1.37 billion in 2024, demonstrating robust momentum driven by increasing security demands across critical infrastructure sectors. The market is anticipated to exhibit a compelling CAGR of 16.2% from 2025 to 2033, culminating in a forecasted market size of USD 5.16 billion by 2033. The primary growth factor fueling this expansion is the rapid adoption of artificial intelligence for real-time threat detection in transportation and logistics environments, where security and operational continuity are paramount.

The surge in demand for advanced security systems is a pivotal growth factor for the on-belt threat detection AI market. With increasing global threats and the heightened need for stringent security protocols, industries such as aviation, mining, logistics, and public transport are turning to AI-driven solutions to bolster their threat detection capabilities. On-belt AI systems, capable of real-time analysis and automated threat identification, enable organizations to respond swiftly to potential hazards, minimizing operational disruptions and safeguarding human lives. The integration of machine learning algorithms ensures continuous improvement in threat recognition accuracy, which is especially vital in high-traffic environments like airports and railway stations. Moreover, regulatory mandates and international security standards are compelling organizations to modernize their security infrastructure, further propelling market growth.

Technological advancements are another significant driver shaping the on-belt threat detection AI market. The convergence of AI, machine vision, and sensor technologies has revolutionized the detection of concealed threats on conveyor belts and baggage handling systems. Enhanced image processing, deep learning, and data analytics enable these systems to differentiate between benign and malicious objects with unprecedented precision. The proliferation of cloud computing and edge AI has also facilitated the deployment of scalable and cost-effective threat detection solutions. As a result, organizations are able to leverage real-time data insights and predictive analytics to preemptively address security risks. The market is further buoyed by increasing investments in research and development, leading to the introduction of innovative solutions tailored to diverse industry needs.

The expansion of e-commerce and global trade has amplified the complexity and volume of goods moving through logistics and warehousing hubs, thereby elevating the risk of security breaches. This dynamic has created fertile ground for the adoption of on-belt threat detection AI solutions in logistics and warehousing applications. Automated threat detection not only enhances security but also streamlines operational efficiency by reducing manual inspections and false alarms. The growing emphasis on supply chain resilience and the need to comply with evolving regulatory requirements are compelling logistics providers to invest in advanced security technologies. Additionally, the mining sector is increasingly deploying AI-powered on-belt detection systems to mitigate safety hazards associated with ore and material transport, further broadening the market’s application landscape.

From a regional perspective, North America currently leads the on-belt threat detection AI market, accounting for the largest share owing to substantial investments in critical infrastructure security and early adoption of cutting-edge AI technologies. Europe follows closely, driven by stringent regulatory frameworks and the presence of major transportation hubs. The Asia Pacific region is expected to witness the fastest growth over the forecast period, fueled by rapid urbanization, expanding transportation networks, and increasing government initiatives to enhance public safety. Latin America and the Middle East & Africa are also emerging as promising markets, supported by ongoing infrastructure development and rising awareness of advanced security solutions.

Component Analysis

The component segment of the on-belt threat detection AI market is categorized into software, hardware, and services, each playing a critical role in the deployment and operation of comprehensive security solutions. Software forms the backbone of threat detection systems, encompassing AI al

Email Spam Classification, Text Classification Dataset

The dataset consists of a collection of emails categorized into two major classes: spam and not spam. It is designed to facilitate the development and evaluation of spam detection or email filtering systems.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

The spam emails in the dataset are typically unsolicited and unwanted messages that aim to promote products or services, spread malware, or deceive recipients for various malicious purposes. These emails often contain misleading subject lines, excessive use of advertisements, unauthorized links, or attempts to collect personal information.

The non-spam emails in the dataset are genuine and legitimate messages sent by individuals or organizations. They may include personal or professional communication, newsletters, transaction receipts, or any other non-malicious content.

The dataset encompasses emails of varying lengths, languages, and writing styles, reflecting the inherent heterogeneity of email communication. This diversity aids in training algorithms that can generalize well to different types of emails, making them robust against different spammer tactics and variations in non-spam email content.

The dataset's possible applications:

• spam detection
• fraud detection
• email filtering systems
• customer support automation
• natural language processing

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F4d1fdedb2827152696dd0c0af05fd8da%2Ff.png?generation=1690286497115141&alt=media" alt="">

💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

File with the extension .csv

includes the following information:

• title: title of the email,
• text: text of the email,
• type: type of the email

Email spam might be collected in accordance with your requirements.

keywords: spam mails dataset, email spam classification, spam or not-spam, spam e-mail database, spam detection system, email spamming data set, spam filtering system, spambase, feature extraction, spam ham email dataset, classifier, machine learning algorithms, cybersecurity, text dataset, sentiment analysis, llm dataset, language modeling, large language models, text classification, text mining dataset, natural language texts, nlp, nlp open-source dataset, text data

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Artificial Intelligence (AI) Market Size 2025-2029

The artificial intelligence (AI) market size is valued to increase by USD 369.1 billion, at a CAGR of 34.7% from 2024 to 2029. Prevention of fraud and malicious attacks will drive the artificial intelligence (ai) market.

Major Market Trends & Insights

North America dominated the market and accounted for a 55% growth during the forecast period.
By Component - Software segment was valued at USD 27.50 billion in 2023
By End-user - Retail segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 975.62 billion
Market Future Opportunities: USD 369.10 billion
CAGR from 2024 to 2029 : 34.7%

Market Summary

The market is a dynamic and ever-evolving landscape, characterized by continuous advancements in core technologies and applications. Key technologies driving this growth include machine learning, natural language processing, and robotics, while applications span industries such as healthcare, finance, and manufacturing. The market is also witnessing a significant shift towards cloud-based AI services, with major players like Microsoft, Google, and Amazon leading the charge. However, challenges persist, including the need to prevent fraud and malicious attacks, the shortage of AI experts, and increasing regulatory scrutiny.
According to recent reports, the global AI market is expected to reach a 25% adoption rate by 2025, underscoring its transformative potential across various sectors. This data-driven narrative reflects the ongoing unfolding of market activities and evolving patterns, providing valuable insights for businesses looking to leverage AI for competitive advantage.

What will be the Size of the Artificial Intelligence (AI) Market during the forecast period?

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How is the Artificial Intelligence (AI) Market Segmented ?

The artificial intelligence (AI) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Hardware
  Services


End-user

  Retail
  Banking
  Manufacturing
  Healthcare
  Others


Technology

  Deep learning
  Machine learning
  NLP
  Gen AI


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) is revolutionizing the software development landscape as developers leverage AI tools to create intelligent applications. These tools, which include algorithms, libraries, frameworks, and developer kits, enable the integration of machine learning, speech recognition, and other advanced AI features. According to recent studies, the usage of AI in software development is becoming increasingly common, with 30% of developers reporting current implementation and 45% planning to adopt AI tools in the near future. Moreover, the future growth prospects of the AI market are promising, with 35% of businesses anticipating significant increases in AI adoption within the next three years.

AI software is poised to transform various sectors by automating manual tasks, enhancing employee experience, and providing data-driven insights. Fuzzy logic systems and genetic algorithms are integral components of AI, enabling process optimization and data mining techniques. Expert systems, speech recognition technology, sentiment analysis tools, and decision support systems facilitate improved customer relationship management. Deep learning models and risk assessment models contribute to pattern recognition systems, while neural network architecture and natural language generation advance knowledge representation. Anomaly detection systems, machine learning algorithms, and robotic process automation are essential for cognitive computing and AI-powered automation. Fraud detection algorithms, computer vision systems, and reinforcement learning are crucial for industries like finance, healthcare, and manufacturing.

Furthermore, reasoning mechanisms, natural language processing, predictive modeling, image processing techniques, and chatbot development are vital for creating intelligent applications across various sectors. The continuous evolution of AI technology and its applications underscores the importance of staying informed and adopting these tools to remain competitive in today's business landscape.

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The Software segment was valued at USD 27.50 billion in 2019 and showed a gradual increase during the forecast period.

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