| Title | Intrusion Detection Using Deep Learning and Statistical Data Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Wasi, Sarwar, Shams, Sarmad, Nasim, Shahzad, Shafiq, Arham |
| Conference Name | 2019 4th International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST) |
| Date Published | dec |
| Keywords | artificial neural network, Artificial neural networks, binary classification model, Classification algorithms, computer network security, computer networks, cyber physical systems, Data analysis, data classification, data mining, Data models, Deep Learning, deep learning algorithm, feature extraction, Intrusion detection, KDD cup 99 datasets, learning (artificial intelligence), machine learning, Mathematical model, Network security, neural nets, pattern classification, policy-based governance, pubcrawl, Resiliency, statistical analysis, statistical data analysis |
| Abstract | Innovation and creativity have played an important role in the development of every field of life, relatively less but it has created several problems too. Intrusion detection is one of those problems which became difficult with the advancement in computer networks, multiple researchers with multiple techniques have come forward to solve this crucial issue, but network security is still a challenge. In our research, we have come across an idea to detect intrusion using a deep learning algorithm in combination with statistical data analysis of KDD cup 99 datasets. Firstly, we have applied statistical analysis on the given data set to generate a simplified form of data, so that a less complex binary classification model of artificial neural network could apply for data classification. Our system has decreased the complexity of the system and has improved the response time. |
| DOI | 10.1109/ICEEST48626.2019.8981688 |
| Citation Key | wasi_intrusion_2019 |
Intrusion Detection Using Deep Learning and Statistical Data Analysis