Generalized one-step third derivative implicit hybrid block method for the direct solution of second order ordinary differential equation

In this article, an implicit hybrid method of order six is developed for the direct solution of second order ordinary differential equations using collocation and interpolation approach.To derive this method, the approximate solution power series is interpolated at the first and off-step points and its second and third derivatives are collocated at all points in the given interval.Besides having good numerical method properties, the new developed method is also superior to the existing methods in terms of accuracy when solving the same problems

Intensive pre-processing of KDD Cup 99 for network intrusion classification using machine learning techniques

© 2019, International Association of Online Engineering. Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity and availability of the services. The speed of the IDS is very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The techniques J48, Random Forest, Random Tree, MLP, Naïve Bayes and Bayes Network classifiers have been chosen for this study. It has been proven that the Random forest classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type (DOS, R2L, U2R, and PROBE)

Ensuring telecommunication network security through cryptology: a case of 4G and 5G LTE cellular network providers

This paper aims to present the details regarding telecommunication network security through cryptology protocols. The data was based on scientific data collection and the quantitative method was adopted. The questionnaire was developed and the primary respondents were approached who were working in 4 telecommunication networking companies namely Huawei, Ericsson, SK Telecom and Telefonica. The sample size of the research was 60 participants and the statistical analysis was used to analyze research. The finding shows that cryptology protocol such as SSH, SSL, Kerberos PGP and SET are implemented within the companies in order to secure network

SNMP 2016 dataset

The enormous growth in computer networks and in Internet usage in recent years,combined with the growth in the amount of data exchanged over networks, have shownan exponential increase in the amount of malicious and mysterious threats to computernetworks. Among many security issues, network attack is a major one. For example,Denial of Service (DoS) flooding attacks have recently become attractive to attackers,and these have posed devastating threats to network services. Therefore, the intrusiondetection and network anomalies become very critical tasks in the field of networksecurity research area. Researchers suffer from the lack of real-life datasets. Most ofthe datasets in hand depend on simulated-based approaches, which cannot represent theexact and the nature of network intrusion and anomaly scenarios. Hence, generatingrealistic datasets is very important as it allows for accurate and appropriate evaluationof the detection techniques. To overcome such shortcoming of the existing datasets, inthis paper, we identify the important requirements to generate effective dataset and wealso identify important attack scenarios and the method of injecting them in such data.Our systematic approach involves the investigation of Simple Network ManagementProtocol (SNMP) for network anomaly detection. For that, we present a ManagementInformation Base (MIB) based mechanism capturing realistic SNMP-MIB statisticaldata. Then we use this data from an SNMP agent by means of real-life experimentsinvolving six types of DoS attacks and Brute Force attack. Our dataset consists of 4998records, where each record consists of 34 MIB variables, which are categorized intotheir corresponding groups, namely: Interface, IP, TCP and ICMP.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

High-Performance and Power-Saving Mechanism for Page Activations Based on Full Independent DRAM Sub-Arrays in Multi-Core Systems

Modern DRAM devices’ performance and energy efficiency are significantly improved when the row-buffer locality is exploited properly. In multi-core architectures, however, the DRAM-based main memory banks used by the processing units, called cores, are shared. Memory interference, also known as memory contention, occurs when many cores contend for simultaneous access to the shared banks. The performance benefits provided by utilizing the available row-buffer locality are diminished by the increased memory contention brought on by the integration of more cores. Large DRAM page sizes are therefore activated in order to access only a tiny amount of data. Poor energy efficiency or wasted opportunity to loosen DRAM power timing restrictions are both downsides to this page over-fetching issue. This study introduces a Fine-Grained Activation (FGA) technique to reduce the number of involved bitlines when accessing DRAM memory. This technique significantly improves the parallelism at the DRAM subarray level to support multiple memory accesses routed to distinct subarrays inside the same memory bank. The FGA technique presented in this research intends to provide large energy savings while simultaneously delivering significant performance gains. Our evaluation findings with 4-core multi-program benchmarks demonstrate that the FGA technique proposed in this paper can significantly improve both DRAM performance and DRAM energy efficiency with a negligible area overhead. In comparison to the baseline, the Half-DRAM page activation mechanism, and the recently suggested FGA mechanism, the proposed technique in this study reduces the average DRAM memory access latency for the evaluated four-core applications by 25.6%, 27.1%, and 14.8%, respectively. Our introduced technique also decreases the DRAM activation power by an average of 46.7%, 27.1%, and 14.7%, respectively, when compared with the baseline, Half-DRAM technique, and the recently proposed FGA mechanism