@Article{crph1125,
AUTHOR = {Lekkala, Seshagirirao and Avula, Raghavaiah and Gurijala, Priyanka},
TITLE = {Big Data and AI/ML in Threat Detection: A New Era of Cybersecurity},
JOURNAL = {Current Research in Public Health},
VOLUME = {2},
YEAR = {2022},
NUMBER = {1},
PAGES = {32-48},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1125},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2022.1125},
ABSTRACT = {The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.},
}

%0 Journal Article
%A Lekkala, Seshagirirao
%A Avula, Raghavaiah
%A Gurijala, Priyanka
%D 2022
%J Current Research in Public Health

%@ 2831-5162
%V 2
%N 1
%P 32-48

%T Big Data and AI/ML in Threat Detection: A New Era of Cybersecurity
%M doi:10.31586/jaibd.2022.1125
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1125

TY  - JOUR
AU  - Lekkala, Seshagirirao
AU  - Avula, Raghavaiah
AU  - Gurijala, Priyanka
TI  - Big Data and AI/ML in Threat Detection: A New Era of Cybersecurity
T2  - Current Research in Public Health
PY  - 2022
VL  - 2
IS  - 1
SN  - 2831-5162
SP  - 32
EP  - 48
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1125
AB  - The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.
DO  - Big Data and AI/ML in Threat Detection: A New Era of Cybersecurity
TI  - 10.31586/jaibd.2022.1125
ER  -