| Title | Analyst Intuition Inspired High Velocity Big Data Analysis Using PCA Ranked Fuzzy K-Means Clustering with Multi-Layer Perceptron (MLP) to Obviate Cyber Security Risk |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Teoh, T. T., Zhang, Y., Nguwi, Y. Y., Elovici, Y., Ng, W. L. |
| Conference Name | 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) |
| Date Published | jul |
| Keywords | Analyst Intuition, analyst intuition inspired high velocity big data analysis, artificial intelligence, attacker IP addresses characteristics, Big Data, Clustering algorithms, computer network security, computer networks, computer security, cyber security, cyber security expert, cyber security log classification, cyber security risk, cyber threats, cyber-attacks, Data analysis, data mining, efficient security monitoring system, Expectation Regulated, expert system, expert systems, Fuzzy k-means (FKM), fuzzy set theory, High Velocity, Human Behavior, integrated datasets, invasive software, IP networks, learning (artificial intelligence), log history annotation, Malware, malware attacks, manually labelled data, MLP, Monitoring, Multi-layer Perceptron (MLP), multilayer perceptrons, network datasets, network protocols, network user, neural network classifier multilayer perceptron base, pattern classification, pattern clustering, PCA ranked fuzzy k-means clustering, principal component analysis, Principal Component Analysis (PCA), pubcrawl, resilience, Resiliency, Scalability, scoring system, security, special semisupervise method, statistical data generation, Virus |
| Abstract | The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks. |
| DOI | 10.1109/FSKD.2017.8393038 |
| Citation Key | teoh_analyst_2017-1 |
Analyst Intuition Inspired High Velocity Big Data Analysis Using PCA Ranked Fuzzy K-Means Clustering with Multi-Layer Perceptron (MLP) to Obviate Cyber Security Risk