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@article{164451,
        author = {Kumbarthi Sadhana and Miryala Srinivas and Nadipally Saitharun and Dr. P. S. V. Srinivas Rao},
        title = {FORECASTING AND DETECTING CYBER HACKING BREACHES WITH MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1539-1543},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164451},
        abstract = {Delving further into the evolution of the threat situation can be achieved in part through the analysis of cyber event data sets. There are still a lot of investigations to be done on this relatively young academic issue. In this research, we present a statistical study of a data collection of breach incidents covering cyber hacking operations involving malware attacks during a period of 12 years (2005–2017). We demonstrate that, contrary to the conclusions published in the literature, because stochastic processes display autocorrelations, it is more appropriate to represent hacking breach incidence inter-arrival times and breach sizes as stochastic processes rather than distributions. Next, we provide specific stochastic process models to fit the breach sizes and the inter-arrival timings, respectively. We also demonstrate how these models are able to forecast the intervals between arrivals.},
        keywords = {Cyber incident data analysis ,   Breach incident data set , Cyber hacking activities , Malware attacks , Statistical analysis , Stochastic processes, Autocorrelations  , Machine learning in cybersecurity , Predictive models  , Anomaly detection, Cyber threat evolution , Inter-arrival times, Breach sizes , Trend analysis , Risk mitigation , Support Vector Machine (SVM) , Algorithms in cyber security , Dataflow diagram , System architecture , Differential privacy , Generative adversarial nets , Learning with kernels},
        month = {},
        }