Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set

A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks

Continuous data transmission in wireless sensor networks (WSNs) is one of the most important characteristics which makes sensors prone to failure. a backup strategy needs to co-exist with the infrastructure of the network to assure that no data is missing. The proposed system relies on a backup strategy of building a history file that stores all collected data from these nodes. This file is used later on by fuzzy logic to estimate missing data in case of failure. An easily programmable microcontroller unit is equipped with a data storage mechanism used as cost worthy storage media for these data. An error in estimation is calculated constantly and used for updating a reference “optimal table” that is used in the estimation of missing data. The error values also assure that the system doesn’t go into an incremental error state. This paper presents a system integrated of optimal data table, microcontroller, and fuzzy logic to estimate missing data of failing sensors. The adapted approach is guided by the minimum error calculated from previously collected data. Experimental findings show that the system has great potentials of continuing to function with a failing node, with very low processing capabilities and storage requirements

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade’s Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets