in most cases

NetFlow Version 1 of the datasets is made up of 8 basic NetFlow features. The details of the datasets are published in; Sarhan M., Layeghy S., Moustafa N., Portmann M. (2021) NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems. In: Big Data Technologies and Applications. BDTA 2020, WiCON 2020. Springer, Cham. The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.

INFO ABOUT THE BOT-IOT DATASET, NOTE: only the csv files stated in the description are used

The BoT-IoT dataset can be downloaded from HERE. You can also use our new datasets: the TON_IoT and UNSW-NB15.

--------------------------------------------------------------------------

The BoT-IoT dataset was created by designing a realistic network environment in the Cyber Range Lab of UNSW Canberra. The network environment incorporated a combination of normal and botnet traffic. The dataset’s source files are provided in different formats, including the original pcap files, the generated argus files and csv files. The files were separated, based on attack category and subcategory, to better assist in labeling process.

The captured pcap files are 69.3 GB in size, with more than 72.000.000 records. The extracted flow traffic, in csv format is 16.7 GB in size. The dataset includes DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used.

To ease the handling of the dataset, we extracted 5% of the original dataset via the use of select MySQL queries. The extracted 5%, is comprised of 4 files of approximately 1.07 GB total size, and about 3 million records.

--------------------------------------------------------------------------

Free use of the Bot-IoT dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes should be agreed by the authors. The authors have asserted their rights under the Copyright. To whom intent the use of the Bot-IoT dataset, the authors have to cite the following papers that has the dataset’s details: .

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Benjamin Turnbull. "Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset." Future Generation Computer Systems 100 (2019): 779-796. Public Access Here.

Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay. "Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques." In International Conference on Mobile Networks and Management, pp. 30-44. Springer, Cham, 2017.

Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova. "A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework." Future Generation Computer Systems 110 (2020): 91-106.

Koroniotis, Nickolaos, and Nour Moustafa. "Enhancing network forensics with particle swarm and deep learning: The particle deep framework." arXiv preprint arXiv:2005.00722 (2020).

Koroniotis, Nickolaos, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, and Helge Janicke. "A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports." IEEE Access (2020).

Koroniotis, Nickolaos. "Designing an effective network forensic framework for the investigation of botnets in the Internet of Things." PhD diss., The University of New South Wales Australia, 2020.

--------------------------------------------------------------------------

NetFlow Version 2 of the datasets is made up of 43 extended NetFlow features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Towards a Standard Feature Set for Network Intrusion Detection System Datasets, Mobile Networks and Applications, 103, 108379, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.

masoltani/bot-iot dataset hosted on Hugging Face and contributed by the HF Datasets community

The CICFlowMeter format of the datasets is made up of 83 network features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection, Big Data Research, 30, 100359, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.

NF-BoT-IoT-V2 is the extended NetFlow version of NF-BoT-IoT. Compared to the original NF-NIDS datasets, the feature set of NetFlow features has expanded from 8 to 43.

This is one dataset in the NFV2-collection by the university of Queensland aimed at standardizing network-security datasets to achieve interoperability and larger analyses.

All credit goes to the original authors: Dr. Mohanad Sarhan, Dr. Siamak Layeghy and Dr. Marius Portmann. Please cite their original journal article when using this dataset.

V1: Base dataset in CSV format as downloaded from here V2: Cleaning -> parquet files

In the parquet files all data types are already set correctly, there are 0 records with missing information and 0 duplicate records.

This dataset is an enhanced version of NetFlow-based datasets, incorporating 53 extracted features that provide detailed insights into network flows. The dataset includes binary and multi-class labels, distinguishing between normal traffic and four different types of attacks. It is structured in CSV format, with each row representing a single network flow, labeled accordingly. One of the key aspects of this dataset is the inclusion of temporal features, which allow for a more detailed analysis of traffic over time. The dataset records precise timestamps for each flow, including start and end times, enabling a more structured understanding of flow duration and activity patterns. Additionally, it captures inter-packet arrival time (IAT) statistics, including minimum, maximum, average, and standard deviation values for both source-to-destination and destination-to-source packet transmissions.Note, there are minor changes to the dataset description in this data record, which is slightly different from the information in the download files description. The information presented in this data record is the most up-to-date.

Network traffic datasets with novel extended IP flow called NetTiSA flow

Datasets were created for the paper: NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification -- Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka -- which is published in The International Journal of Computer and Telecommunications Networking https://doi.org/10.1016/j.comnet.2023.110147Please cite the usage of our datasets as:

Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification", Computer Networks, Volume 240, 2024, 110147, ISSN 1389-1286

@article{KOUMAR2024110147, title = {NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification}, journal = {Computer Networks}, volume = {240}, pages = {110147}, year = {2024}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2023.110147}, url = {https://www.sciencedirect.com/science/article/pii/S1389128623005923}, author = {Josef Koumar and Karel Hynek and Jaroslav Pešek and Tomáš Čejka} }

This Zenodo repository contains 23 datasets created from 15 well-known published datasets, which are cited in the table below. Each dataset contains the NetTiSA flow feature vector.

NetTiSA flow feature vector

The novel extended IP flow called NetTiSA (Network Time Series Analysed) flow contains a universal bandwidth-constrained feature vector consisting of 20 features. We divide the NetTiSA flow classification features into three groups by computation. The first group of features is based on classical bidirectional flow information---a number of transferred bytes, and packets. The second group contains statistical and time-based features calculated using the time-series analysis of the packet sequences. The third type of features can be computed from the previous groups (i.e., on the flow collector) and improve the classification performance without any impact on the telemetry bandwidth.

Flow features

The flow features are:

Packets is the number of packets in the direction from the source to the destination IP address.

Packets in reverse order is the number of packets in the direction from the destination to the source IP address.

Bytes is the size of the payload in bytes transferred in the direction from the source to the destination IP address.

Bytes in reverse order is the size of the payload in bytes transferred in the direction from the destination to the source IP address.

Statistical and Time-based features

The features that are exported in the extended part of the flow. All of them can be computed (exactly or in approximative) by stream-wise computation, which is necessary for keeping memory requirements low. The second type of feature set contains the following features:

Mean represents mean of the payload lengths of packets

Min is the minimal value from payload lengths of all packets in a flow

Max is the maximum value from payload lengths of all packets in a flow

Standard deviation is a measure of the variation of payload lengths from the mean payload length

Root mean square is the measure of the magnitude of payload lengths of packets

Average dispersion is the average absolute difference between each payload length of the packet and the mean value

Kurtosis is the measure describing the extent to which the tails of a distribution differ from the tails of a normal distribution

Mean of relative times is the mean of the relative times which is a sequence defined as (st = {t_1 - t_1, t_2 - t_1, ..., t_n - t_1} )

Mean of time differences is the mean of the time differences which is a sequence defined as (dt = { t_j - t_i | j = i + 1, i \in {1, 2, \dots, n - 1} }.)

Min from time differences is the minimal value from all time differences, i.e., min space between packets.

Max from time differences is the maximum value from all time differences, i.e., max space between packets.

Time distribution describes the deviation of time differences between individual packets within the time series. The feature is computed by the following equation:(tdist = \frac{ \frac{1}{n-1} \sum_{i=1}^{n-1} \left| \mu_{{dt_{n-1}}} - dt_i \right| }{ \frac{1}{2} \left(max\left({dt_{n-1}}\right) - min\left({dt_{n-1}}\right) \right) })

Switching ratio represents a value change ratio (switching) between payload lengths. The switching ratio is computed by equation:(sr = \frac{s_n}{\frac{1}{2} (n - 1)})

    where \(s_n\) is number of switches.

Features computed at the collectorThe third set contains features that are computed from the previous two groups prior to classification. Therefore, they do not influence the network telemetry size and their computation does not put additional load to resource-constrained flow monitoring probes. The NetTiSA flow combined with this feature set is called the Enhanced NetTiSA flow and contains the following features:

Max minus min is the difference between minimum and maximum payload lengths

Percent deviation is the dispersion of the average absolute difference to the mean value

Variance is the spread measure of the data from its mean

Burstiness is the degree of peakedness in the central part of the distribution

Coefficient of variation is a dimensionless quantity that compares the dispersion of a time series to its mean value and is often used to compare the variability of different time series that have different units of measurement

Directions describe a percentage ratio of packet direction computed as (\frac{d_1}{ d_1 + d_0}), where (d_1) is a number of packets in a direction from source to destination IP address and (d_0) the opposite direction. Both (d_1) and (d_0) are inside the classical bidirectional flow.

Duration is the duration of the flow

The NetTiSA flow is implemented into IP flow exporter ipfixprobe.

Description of dataset files

In the following table is a description of each dataset file:

File name

Detection problem

Citation of the original raw dataset

botnet_binary.csv Binary detection of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

botnet_multiclass.csv Multi-class classification of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

cryptomining_design.csv Binary detection of cryptomining; the design part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

cryptomining_evaluation.csv Binary detection of cryptomining; the evaluation part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

dns_malware.csv Binary detection of malware DNS Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021.

doh_cic.csv Binary detection of DoH Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020

doh_real_world.csv Binary detection of DoH Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022

dos.csv Binary detection of DoS Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.

edge_iiot_binary.csv Binary detection of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

edge_iiot_multiclass.csv Multi-class classification of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

https_brute_force.csv Binary detection of HTTPS Brute Force Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020

ids_cic_binary.csv Binary detection of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

ids_cic_multiclass.csv Multi-class classification of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

unsw_binary.csv Binary detection of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

unsw_multiclass.csv Multi-class classification of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

iot_23.csv Binary detection of IoT malware Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23

ton_iot_binary.csv Binary detection of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021

ton_iot_multiclass.csv Multi-class classification of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets.

Network traffic datasets created by Single Flow Time Series Analysis

Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:

J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.

This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

In the following table is a description of each dataset file:

File name Detection problem Citation of original raw dataset

botnet_binary.csv Binary detection of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

botnet_multiclass.csv Multi-class classification of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

cryptomining_design.csv Binary detection of cryptomining; the design part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

cryptomining_evaluation.csv Binary detection of cryptomining; the evaluation part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

dns_malware.csv Binary detection of malware DNS Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021.

doh_cic.csv Binary detection of DoH

Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020

doh_real_world.csv Binary detection of DoH Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022

dos.csv Binary detection of DoS Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.

edge_iiot_binary.csv Binary detection of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

edge_iiot_multiclass.csv Multi-class classification of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

https_brute_force.csv Binary detection of HTTPS Brute Force Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020

ids_cic_binary.csv Binary detection of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

ids_cic_multiclass.csv Multi-class classification of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

ids_unsw_nb_15_binary.csv Binary detection of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

ids_unsw_nb_15_multiclass.csv Multi-class classification of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

iot_23.csv Binary detection of IoT malware Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23

ton_iot_binary.csv Binary detection of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021

ton_iot_multiclass.csv Multi-class classification of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021

tor_binary.csv Binary detection of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.

tor_multiclass.csv Multi-class classification of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.

vpn_iscx_binary.csv Binary detection of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.

vpn_iscx_multiclass.csv Multi-class classification of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.

vpn_vnat_binary.csv Binary detection of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

vpn_vnat_multiclass.csv Multi-class classification of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

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 proposed method is evaluated using comprehensive benchmarking datasets such as InSDN, BoT-IoT, and IoT-23.

Ben11304/IoT-Bot-DiFL dataset hosted on Hugging Face and contributed by the HF Datasets community

Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure road safety. However, VN are vulnerable to Distributed Denial of Service (DDoS) attacks, posing severe threats in the contemporary Internet landscape. With the surge in Internet traffic, this study proposes novel methodologies for effectively detecting DDoS attacks within Software-Defined Vehicular Networks (SDVN), wherein attackers commandeer compromised nodes to monopolize network resources, disrupting communication among vehicles and between vehicles and infrastructure. The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. Additionally, the approach distinguishes between reconnaissance, Denial of Service (DoS), and DDoS traffic by addressing the challenges of imbalanced and overfitting dataset traces. One of the significant challenges in this integration is managing the computational load and ensuring real-time performance. The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. Ensuring scalability and maintaining high detection accuracy as network traffic grows and evolves is another critical challenge. By leveraging a minimal subset of features from a given dataset, a comparative study is conducted to determine the optimal sample size for maximizing model accuracy. Further, the study evaluates the impact of various dataset attributes on performance thresholds. The K-nearest Neighbor, Random Forest, and Logistic Regression supervised ML classifiers are assessed using the BoT-IoT dataset. The results indicate that the Random Forest classifier achieves superior performance metrics, with Precision, F1-score, Accuracy, and Recall rates of 92%, 92%, 91%, and 90%, respectively, over five iterations.

Set up of IoT and 1 -BoT for tile images extraction of both training and testing set, number of tile images for both CAM and PAIP dataset.

The global Finger Bots market is experiencing steady growth, with a market size of $15.6 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 2.0% from 2025 to 2033. This growth is driven by several factors, including the increasing demand for smart home automation, the rising adoption of Internet of Things (IoT) devices, and the convenience and affordability of finger bot technology. Consumers are increasingly seeking convenient and user-friendly solutions for automating tasks within their homes, leading to greater adoption of these small, versatile robots. Furthermore, technological advancements are continually improving the functionality and capabilities of finger bots, expanding their potential applications beyond simple automation to include areas such as security monitoring and remote control. Key players like Osprey, SwitchBot Global, Nedis, MOES, Adaprox, AUBESS, Xiamen E-leader Electronics, and Shenzhen RiShengHua Technology are actively contributing to market expansion through innovation and product diversification. The market is segmented by various factors such as functionality (e.g., smart home integration, remote control, security features) and price point, catering to a broad spectrum of consumer needs. Despite the positive growth trajectory, the market faces certain challenges. Competition among established players and emerging startups could intensify price wars. The market's success hinges on continuous technological innovation to deliver advanced features and improved user experience. Addressing concerns about data privacy and security associated with IoT devices is crucial to maintain consumer trust and drive widespread adoption. Future growth prospects will depend heavily on the successful integration of finger bots with other smart home ecosystems and the development of sophisticated applications that demonstrate their practical value to a wider audience. The relatively low CAGR indicates a mature but stable market, rather than one experiencing explosive growth. Nevertheless, consistent innovation and effective marketing can propel further market expansion in the coming years.

In response to the rapidly evolving threat landscape in network security, this paper proposes an Evolutionary Machine Learning Algorithm designed for robust intrusion detection. We specifically address challenges such as adaptability to new threats and scalability across diverse network environments. Our approach is validated using two distinct datasets: BoT-IoT, reflecting a range of IoT-specific attacks, and UNSW-NB15, offering a broader context of network intrusion scenarios using GA based hybrid DT-SVM. This selection facilitates a comprehensive evaluation of the algorithm’s effectiveness across varying attack vectors. Performance metrics including accuracy, recall, and false positive rates are meticulously chosen to demonstrate the algorithm’s capability to accurately identify and adapt to both known and novel threats, thereby substantiating the algorithm’s potential as a scalable and adaptable security solution. This study aims to advance the development of intrusion detection systems that are not only reactive but also preemptively adaptive to emerging cyber threats.” During the feature selection step, a GA is used to discover and preserve the most relevant characteristics from the dataset by using evolutionary principles. Through the use of this technology based on genetic algorithms, the subset of features is optimised, enabling the subsequent classification model to focus on the most relevant components of network data. In order to accomplish this, DT-SVM classification and GA-driven feature selection are integrated in an effort to strike a balance between efficiency and accuracy. The system has been purposefully designed to efficiently handle data streams in real-time, ensuring that intrusions are promptly and precisely detected. The empirical results corroborate the study’s assertion that the IDS outperforms traditional methodologies.

Bot Security Solution Market Outlook

The global bot security solution market size is poised to grow significantly, with a projected CAGR of 18.6% from 2024 to 2032. In 2023, the market was valued at approximately USD 1.2 billion and is forecasted to reach around USD 4.5 billion by 2032. This growth can be attributed to the increasing sophistication of bot attacks, the growing reliance on digital channels, and the rising need for robust security measures to protect against cyber threats.

One of the primary growth factors driving the bot security solution market is the exponential increase in the volume and sophistication of bot attacks. Bots are used for various malicious activities such as data scraping, account takeovers, and DDoS attacks. As organizations increasingly rely on digital channels, the need to protect sensitive data and ensure seamless user experiences has become paramount. Consequently, businesses across different sectors are investing heavily in advanced bot security solutions to safeguard their digital assets and maintain the trust of their customers.

Another key driver of market growth is the rapid digitization of industries and the proliferation of Internet of Things (IoT) devices. With more devices connected to the internet, the attack surface for cybercriminals has expanded significantly. IoT devices, in particular, are vulnerable to bot attacks due to their often weak security measures. As a result, there is a growing demand for comprehensive security solutions that can protect a wide range of devices and networks from bot-related threats. The increasing adoption of cloud services and the need for secure cloud environments also contribute to the demand for bot security solutions.

The regulatory landscape is also playing a crucial role in the growth of the bot security solution market. Governments and regulatory bodies worldwide are implementing stringent data protection and cybersecurity regulations to protect consumers and businesses from cyber threats. Compliance with these regulations requires organizations to adopt robust security measures, including bot security solutions. This has led to increased investments in security technologies and services to ensure compliance and avoid hefty fines and reputational damage.

From a regional perspective, North America leads the bot security solution market due to the presence of major cybersecurity vendors, high awareness levels, and significant investments in cybersecurity infrastructure. The region's advanced IT and telecommunications sector further bolsters its leadership position. Europe follows closely, driven by stringent data protection regulations like GDPR and increasing cyber threats. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid digital transformation, increasing internet penetration, and rising cyber threats. Latin America and the Middle East & Africa are also experiencing growing demand for bot security solutions, albeit at a slower pace.

Component Analysis

The bot security solution market can be segmented by component into software and services. The software segment includes solutions designed to detect, block, and mitigate bot attacks, such as bot management platforms, fraud detection systems, and web application firewalls. These solutions are essential for organizations looking to protect their digital assets and maintain the integrity of their online services. As bot attacks become more sophisticated, the demand for advanced software solutions with real-time threat intelligence and machine learning capabilities is increasing. This segment is expected to grow significantly during the forecast period as organizations prioritize the implementation of robust security measures.

On the other hand, the services segment encompasses professional services and managed services. Professional services include consulting, implementation, and training services provided by cybersecurity experts to help organizations design and deploy effective bot security strategies. These services are crucial for organizations lacking in-house expertise or resources to manage their bot security needs. Managed services, which involve outsourcing the management of bot security to third-party providers, are gaining traction due to the complexities involved in continuously monitoring and mitigating bot threats. This segment is expected to see substantial growth as more organizations opt

AI-Based Energy Market Making Bot Market Outlook

According to our latest research, the AI-Based Energy Market Making Bot market size reached USD 1.21 billion globally in 2024, demonstrating robust momentum in the wake of digital transformation across the energy sector. The market is projected to expand at a CAGR of 22.3% from 2025 to 2033, with the total market value expected to reach USD 8.93 billion by 2033. This exceptional growth is primarily driven by the increasing adoption of AI-driven automation in energy trading, the need for real-time decision-making, and the growing complexity of energy markets. As per our latest research, the AI-Based Energy Market Making Bot market is witnessing rapid technological advancements, positioning itself as an indispensable tool for market participants seeking efficiency, profitability, and sustainability in a rapidly evolving energy landscape.

The surge in the AI-Based Energy Market Making Bot market is fueled by the escalating demand for automation and intelligent decision support in energy trading. As global energy markets become increasingly volatile and decentralized, traditional trading methods are proving inadequate to handle the scale, speed, and complexity of transactions. AI-based bots, equipped with advanced algorithms and machine learning capabilities, are enabling market participants to analyze vast datasets in real time, predict price fluctuations, and execute trades with unprecedented accuracy and speed. This automation not only improves operational efficiency but also minimizes human error and enhances profitability, making it a compelling value proposition for energy companies and traders worldwide. Furthermore, the integration of AI bots with advanced analytics and IoT devices is facilitating a more dynamic and responsive approach to market making, enabling organizations to capitalize on micro-opportunities and respond proactively to market shifts.

Another critical growth factor for the AI-Based Energy Market Making Bot market is the increasing penetration of renewable energy sources and the subsequent need for sophisticated trading strategies. The growing share of renewables in the global energy mix introduces significant variability and uncertainty in supply and demand patterns, necessitating agile and adaptive trading mechanisms. AI-based bots are uniquely positioned to address these challenges by leveraging predictive analytics, real-time data feeds, and advanced optimization techniques to balance portfolios, manage risks, and maximize returns. As regulatory frameworks evolve to support renewable integration and grid flexibility, market participants are increasingly turning to AI-powered solutions to navigate complex market dynamics, ensure compliance, and achieve sustainability targets. This trend is particularly pronounced in regions with aggressive renewable energy targets, further accelerating the adoption of AI-based market making bots.

The proliferation of digital infrastructure and the growing availability of high-frequency market data are also key enablers of growth in the AI-Based Energy Market Making Bot market. The widespread deployment of smart meters, IoT sensors, and advanced energy management systems is generating vast volumes of granular data, providing a rich substrate for AI-driven analysis and decision-making. Market making bots leverage this data to identify trading opportunities, optimize bidding strategies, and enhance liquidity in energy markets. Moreover, the increasing availability of cloud-based platforms is democratizing access to advanced AI tools, enabling smaller market participants to compete on a level playing field with established players. This democratization is fostering innovation, driving competition, and expanding the overall addressable market for AI-based energy trading solutions.

From a regional perspective, North America currently leads the AI-Based Energy Market Making Bot market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The maturity of energy trading markets, high digital adoption rates, and supportive regulatory environments in these regions are key factors underpinning their leadership. In North America, the presence of major energy exchanges, advanced IT infrastructure, and a strong focus on grid modernization are driving the uptake of AI-based trading solutions. Europe, with its ambitious renewable energy targets and integrated energy markets, is witnessing significant investments in AI-driven market making technologi

Market Overview The global voicebot market is estimated to be valued at USD 4.73 billion in 2025 and is projected to grow at a CAGR of 22.51% from 2025 to 2033, reaching USD 24.45 billion by 2033. The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies, rising demand for customer service automation, and growing penetration of smart devices are driving the market growth. Additionally, the shift towards cloud-based solutions, advancements in natural language processing (NLP), and the emergence of conversational AI platforms are fueling the market expansion. Market Dynamics Key drivers of the voicebot market include the rising adoption of AI-powered virtual assistants across various industries, the increasing need for efficient customer service and support, and the growing trend of voice-based user interfaces. Market restraints include privacy and security concerns related to voice data, the high cost of development and deployment, and the lack of standardization in voicebot technology. Key trends in the market include the integration of voicebots with other emerging technologies such as blockchain and the Internet of Things (IoT), the development of multi-modal voicebots that can interact through multiple channels, and the personalization of voicebot experiences through the use of AI and ML algorithms. The voicebot market is witnessing a surge in adoption, driven by technological advancements and the growing demand for convenient user experiences. This research report provides a comprehensive overview of the market, covering key trends, challenges, and opportunities. Key drivers for this market are: Increased customer engagement Voicebots enhance customer experiences by providing personalized and interactive interactions.Improved efficiency Voicebots automate repetitive tasks, freeing up human agents to focus on more complex inquiries.Enhanced accessibility Voicebots provide an accessible alternative to traditional text-based interfaces; benefiting individuals with disabilities or language barriers.Growing adoption in healthcare Voicebots streamline patient interactions; improve medication adherence and enhance remote care.Integration with IoT devices: Voicebots enable seamless control and interaction with smart home appliances and other IoT devices . Potential restraints include: Rising adoption of AI assistantsGrowing demand for customer service automationIntegration of voice bots in various industriesAdvancements in natural language processingIncreasing focus on improving user experience .