Data Mining Practical Machine Learning Tools and Techniques

A Survey of Network-based Intrusion Detection Data Sets

The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being gener...

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.4150

Network Intrusion Detection System: A systematic study of Machine Learning and Deep Learning approaches

Reduction the 10%KDD training dataset up to 99.8% by using modified K-means.New high quality training datasets are constructed for training SVM and ELM.Multi-level model is proposed to improve the performance of detection accuracy.Improve the detection rate of DoS, U2R and R2L attacks.Overall accuracy of 95.75% is achieved with whole Corrected KDD dataset. Intrusion detection has become essential to network security because of the increasing connectivity between computers. Several intrusion detection systems have been developed to protect networks using different statistical methods and machine learning techniques. This study aims to design a model that deals with real intrusion detection problems in data analysis and classify network data into normal and abnormal behaviors. This study proposes a multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks. A modified K-means algorithm is also proposed to build a high-quality training dataset that contributes significantly to improving the performance of classifiers. The modified K-means is used to build new small training datasets representing the entire original training dataset, significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. The popular KDD Cup 1999 dataset is used to evaluate the proposed model. Compared with other methods based on the same dataset, the proposed model shows high efficiency in attack detection, and its accuracy (95.75%) is the best performance thus far.

Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system

Internet of Things: A survey on machine learning-based intrusion detection approaches

Alpha profiling reduces the number of comparisons by 85.76%.Optimal features (21 out of 41) are suggested. Features are reduced by 48.78%.Beta profiling is used to reduce the size of training dataset by 7.83%.Network traffic profiling and feature selection reduce space and time complexity.Accuracy of 98.66% and false positive rate of 1.74% are achieved in 2.43 s. Anomaly based Intrusion Detection Systems (IDS) learn normal and anomalous behavior by analyzing network traffic in various benchmark datasets. Common challenges for IDSs are large amounts of data to process, low detection rates and high rates of false alarms. In this paper, a technique based on the Online Sequential Extreme Learning Machine (OS-ELM) is presented for intrusion detection. The proposed technique uses alpha profiling to reduce the time complexity while irrelevant features are discarded using an ensemble of Filtered, Correlation and Consistency based feature selection techniques. Instead of sampling, beta profiling is used to reduce the size of the training dataset. For performance evaluation of proposed technique the standard NSL-KDD 2009 (Network Security Laboratory-Knowledge Discovery and Data Mining) dataset is used. In this paper time and space complexity of the proposed technique is also discussed. The experimental results yielded an accuracy of 98.66% with a false positive rate of 1.74% and a detection time of 2.43 s for binary class NSL-KDD dataset. The proposed IDS achieve 97.67% of accuracy with 1.74% of false positive rate in 2.65 s of detection time for multi-class NSL-KDD dataset. The Kyoto University benchmark dataset is also used to test the proposed IDS. Accuracy of 96.37% with false positive rate of 5.76% is yielded by the proposed technique. The proposed technique outperforms other published techniques in terms of accuracy, false positive rate and detection time. Based on the experimental results achieved, we conclude that the proposed technique is an efficient method for network intrusion detection.

An intrusion detection system using network traffic profiling and online sequential extreme learning machine

The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

/pdf/implementation-analysis-of-iot-based-offloading-frameworks-4394ldxf27.pdf

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Deep learning-based object detection in low-altitude UAV datasets: A survey

Statistical vertical reduction‐based data abridging technique for big network traffic dataset

The Internet of Things (IoT) represents the physical world of devices and objects connected over the network using wireless sensors. This chapter gives detailed study about the different applications of IoT with the integration of WSN (wireless sensor networks) with Internet connectivity. This allows applications to communicate among themselves and users on a global scale. A large number of IoT applications, like smart home, buildings, transport, water management, healthcare, agriculture, environment and industries, together form the smart city. Along with this, various challenges in the implementation of applications are discussed related to the reliability, sustainability, and efficiency. An open architecture looking into current need of IoT is also proposed and discussed.

Raman Singh

Papers

An intrusion detection system using network traffic profiling and online sequential extreme learning machine

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Deep learning-based object detection in low-altitude UAV datasets: A survey

Statistical vertical reduction‐based data abridging technique for big network traffic dataset

Integration of WSN with IoT Applications: A Vision, Architecture, and Future Challenges