Redefining Cybersecurity with AI and Machine Learning

  • Amol Dhondse
  • Sachchidanand Singh
Keywords: Cybersecurity, Artificial Intelligence(AI), Machine Learning(ML), K Nearest Neighbors (KNN), Support Vector Machines (SVM), Markov Decision Process, Q-learning, Temporal Difference (TD), Attack Vector, Attack Surface, Naive Bayes Classifier, Logistic Regression, Neural Networks, Data Security, Decision Trees, Random Forest, Principal Component Analysis (PCA), Distributed Denial of Service (DDoS), TensorFlow, Torch, Caffe, DeepLearning.


In the age of digital transformation with adoption of Cloud and mobile computing and ever-increasing Internet of Things(IoT) devices, the cybersecurity risks and threat levels are increasing at a rapid pace. The data is spread across systems, devices and cloud leading to growing attack surface and increased frequency of the security attacks. IoT is extended to drones, driver-less cars, industrial equipment, smart buildings, consumer goods, home appliances leaving us with more vulnerable attack points. Organizations needs to have effective information security management system (ISMS) in place to proactively detect, react to security threats with reduced time to discover any potential breach. This paper highlights how Artificial Intelligence(AI) and Machine Learning(ML) can redefine cybersecurity to detect, prevent organizations from security threats and data breaches.


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How to Cite
Dhondse, A., & Singh, S. (2019). Redefining Cybersecurity with AI and Machine Learning. Asian Journal For Convergence In Technology (AJCT), 5(2). Retrieved from