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With the growing use of information technology in all life domains, hackinghas become more negatively effective than ever before. Also with developingtechnologies, attacks numbers are growing exponentially every few months andbecome more sophisticated so that traditional IDS becomes inefficient detectingthem. This paper proposes a solution to detect not only new threats with higherdetection rate and lower false positive than already used IDS, but also itcould detect collective and contextual security attacks. We achieve thoseresults by using Networking Chatbot, a deep recurrent neural network: LongShort Term Memory (LSTM) on top of Apache Spark Framework that has an input offlow traffic and traffic aggregation and the output is a language of two words,normal or abnormal. We propose merging the concepts of language processing,contextual analysis, distributed deep learning, big data, anomaly detection offlow analysis. We propose a model that describes the network abstract normalbehavior from a sequence of millions of packets within their context andanalyzes them in near real-time to detect point, collective and contextualanomalies. Experiments are done on MAWI dataset, and it shows better detectionrate not only than signature IDS, but also better than traditional anomaly IDS.The experiment shows lower false positive, higher detection rate and betterpoint anomalies detection. As for prove of contextual and collective anomaliesdetection, we discuss our claim and the reason behind our hypothesis. But theexperiment is done on random small subsets of the dataset because of hardwarelimitations, so we share experiment and our future vision thoughts as we wishthat full prove will be done in future by other interested researchers who havebetter hardware infrastructure than ours.

Big data analysis and distributed deep learning for next-generation intrusion detection system optimization