Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection

Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used in analyzing huge traffic data; thus, an efficient classification technique is necessary to overcome the issue. This problem is considered in this paper. Well-known machine learning techniques, namely, SVM, random forest, and extreme learning machine (ELM) are applied. These techniques are well-known because of their capability in classification. The NSL–knowledge discovery and data mining data set is used, which is considered a benchmark in the evaluation of intrusion detection mechanisms. The results indicate that ELM outperforms other approaches

Enhancing Feedback: key Issues and Solutions From the Literature to Help New Lecturers in Higher Education

The National Strategy for Higher Education to 2030 highlights that whilst modularisation has allowed for greater flexibility, it has also produced some problems including fragmentation of programmes with large numbers of modules leaving students feeling over assessed and staff burdened (Hunt, 2011). Nicol & Macfarlene-Dick (2006) have argued that formative assessment can promote better student learning and that assessment can be used more effectively by embedding ‘feedback’ and ‘feedforward’ in curriculum practices. Their studies identify how formative feedback does not have to solely come from the teacher, but can also be provided by peers and even generated by the students themselves. The Irish National Forum for the Enhancement of Teaching and Learning in Higher Education (NFETLHE) has put forward similar arguments to enhance learning if we move away from a purely ‘Assessment OF’ approach and shift towards a more ‘Assessment FOR’ and ‘Assessment AS Learning’ approach, giving the students a more central role (NFETLHE, 2017). Figure 1 below illustrates these concepts and highlights the dynamic relationship between formative assessment and learning (NFETLHE, 2017)

Information Sharing in Vehicular AdHoc Network

Relevance Technique broadcast the useful information and removes the redundant data. 802.11e protocol implementation has certain flaws and is not suitable for VANETs scenarios. Main issue in 802.11e protocol is internal sorting of packets, no priority mechanism within the queues and often lower priority traffic get more medium than high priority traffic. In this paper, the mathematical model of relevance scheme is enhanced so that it can consider the network control in real scenario by considering the impact of malicious node in network. Problems of 802.11e protocol can be resolved by making virtual queue at application level. We analyze the comparison of simple virtual queue with the over all impact of virtual queue and mathematical model. Similarly we compare the mathematical model with over all impact of virtual queue and modified mathematical model using NS-2 simulator

Sensor Based Framework for Secure Multimedia Communication in VANET

Secure multimedia communication enhances the safety of passengers by providing visual pictures of accidents and danger situations. In this paper we proposed a framework for secure multimedia communication in Vehicular Ad-Hoc Networks (VANETs). Our proposed framework is mainly divided into four components: redundant information, priority assignment, malicious data verification and malicious node verification. The proposed scheme jhas been validated with the help of the NS-2 network simulator and the Evalvid tool