International Journal of Communication Networks and Information Security (IJCNIS)
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DESIGN OF A MINIMAL OVERHEAD CONTROL TRAFFIC TOPOLOGY DISCOVERY AND DATA FORWARDING PROTOCOL FOR SOFTWARE-DEFINED WIRELESS SENSOR NETWORKS
Get PDFSoftware-defined networking is a novel concept that is ported into wireless sensor networks to make them more manageable and customizable. unfortunately, the topology discovery and maintenance processes generate high overhead control packet exchange between the sensor nodes and the central controller leading to a deterioration of the network's performance. In this paper, a novel minimal overhead control traffic topology discovery and data forwarding protocol is proposed and detailed. The proposed protocol requires some changes to the topology discovery protocol implemented in SDN-WISE to improve its performance. The proposed protocol has been implemented within the IT-SDN framework for evaluation. The results show reduced overhead control traffic and increase, of about 20%, data packet delivery rate over the protocol in SDN-WISE
Analysis of Cybersecurity Standard and Framework Components
Get PDFSatisfactory cybersecurity protection, encompassing all data security solutions, can only be achieved by adopting a cybersecurity framework that provides a structure and methodology for protecting critical digital assets. In addition, security experts recommend using cybersecurity standards which consist of a collection of best practices to protect organizations from cyber threats. However, many organizations, companies and governments lack experienced personnel in the cybersecurity domain, so they have difficulty adopting a standard approach or cybersecurity framework. Protecting organizations from cyber threats while demonstrating compliance with laws and standards is seen as extremely complex due to the difficulty on choosing the appropriate standard to be used. Moreover, lack of knowledge on the elements needed that offered by the standard is lead to the problem on identifying the started point where the protection will be began. Therefore, in this paper, a literature and the analysis is presented in identifying the elements of cybersecurity standard and framework that can be facilitate the organization or government on choosing the appropriate standard and framework to be used and utilized. The literature review was carried out to understand the various types of cybersecurity standards and frameworks and the analysis is conducted to identify the elements in each of them. In this paper, eight steps are presented and include the types of international standards, which are general, local regulation, as well as specific standards used in the industrial sector, to conclude the findings of the analysis. Furthermore, a relation map is presented using Writing a Literature Review release 2.0 approach to show the relationship between the literature review and future research
Gaussian functional shapes-based type-II fuzzy membership-based cluster protocol for energy harvesting IoT networks
Get PDFWith the advancements in Internet of Things (IoT) technologies, energy harvesting IoT devices are becoming significantly important. These tiny IoT devices can harvest bounded energy, thus need an efficient protocol to conserve the energy in more efficient manner. From the review, it is found that the development of an efficient energy efficient protocol for energy harvesting IoT is still an open area of research. It is found that fuzzy based energy harvesting IoTs has shown significant improvement over the existing protocols. However, the fuzzy logic suffers from the data uncertainty issue. Therefore, in this paper, Gaussian functional shapes-based type-II fuzzy membership function is used to elect the cluster heads among the IoT devices to reduce the energy consumption of energy harvest IoTs. Thereafter, inter-cluster data aggregation is used. Finally, the communication between the elected cluster heads and the cloud servers or sink. Extensive experiments are drawn by considering the existing and the proposed protocols for energy harvesting IoTs. Comparative analysis reveals that the proposed type-II fuzzy membership function-based protocol outperforms the existing protocols in terms of bandwidth analysis, throughput, conserve energy, network lifetime, and average consumed energy
Novel Approach for IP-PBX Denial of Service Intrusion Detection Using Support Vector Machine Algorithm.
Get PDFRecent trends have revealed that SIP based IP-PBX DoS attacks contribute to most overall IP-PBX attacks which is resulting in loss of revenues and quality of service in telecommunication providers. IP-PBX face challenges in detecting and mitigating malicious traffic. In this research, Support Vector Machine (SVM) machine learning detection & prevention algorithm were developed to detect this type of attacks Two other techniques were benchmarked decision tree and Naïve Bayes. The training phase of the machine learning algorithm used proposed real-time training datasets benchmarked with two training datasets from CICIDS and NSL-KDD. Proposed real-time training dataset for SVM algorithm achieved highest detection rate of 99.13% while decision tree and Naïve Bayes has 93.28% & 86.41% of attack detection rate, respectively. For CICIDS dataset, SVM algorithm achieved highest detection rate of 76.47% while decision tree and Naïve Bayes has 63.71% & 41.58% of detection rate, respectively. Using NSL-KDD training dataset, SVM achieved 65.17%, while decision tree and Naïve Bayes has 51.96% & 38.26% of detection rate, respectively.The time taken by the algorithms to classify the attack is very important. SVM gives less time (2.9 minutes) for detecting attacks while decision tree and naïve Bayes gives 13.6 minutes 26.2 minutes, respectively. Proposed SVM algorithm achieved the lowest false negative value of (87 messages) while decision table and Naïve Bayes achieved false negative messages of 672 and 1359, respectively
A New Data Deletion Scheme for a Blockchain-based De-duplication System in the Cloud
Get PDFAlmost all Cloud Service Providers (CSP) takes a principled approach to the storage and deletion of Customer Data. Most of them have engineered their cloud platform to achieve a high degree of speed, availability, durability, and consistency. Their systems are designed to be optimized for these performance attributes and must be carefully balanced with the necessity to achieve accurate and timely data deletion.many researchers have turn their focus toward data storage and how it will be a challenging task for CSPs in term of storage capacity, data management and security, a considerable number of papers has been published containing new models and technique that will allow data De-duplication in a shared environment but few of them have discussed data deletion.In this paper we will be discussing a new approach that will allow a smart deletion of data stored in the file system as well as its reference in the Blockchain since, by its nature, Blockchains does not allow deletion without violating the Blockchain’s consistency, a preexisting de-duplication system will be our base platform on which we will be working to achieve an accurate and secure data deletion using Blockchain technology while preserving its consistency
A Study of Z-Transform Based Encryption Algorithm
Get PDFIt has become increasingly important to ensure the protection of information, especially data in transit. Therefore, it is the primary goal of any encryption algorithm to safeguard the protection of information against security attacks. It is equally important to design high-performance solutions with affordable cost of implementation. Encryption algorithms are used to transform plain text to the ciphertext in order to protect privacy, prevent data fraud, and prevent unauthorized access of data in daily transactions. There are multiple types of encryption algorithms, each with its niche tactics to enhance security. For instance, different kinds of algorithms include but are not limited to the following: Blowfish, RSA, AES, DES, Triple DES. This paper contributes an efficient and secure encryption algorithm technique for information security based on Z transformation and XOR function known as the Z Transformation Encryption (ZTE) technique. To elaborate, this technique implements concepts of Z transformation and XOR operations at the source. The reverse process is applied at the receiving end of the transaction wherein the inverse of Z transformation and XOR is applied to reveal the original plain text message. The simulation of the proposed algorithm is conducted using the R language. The results show a promising performance comparing to other symmetric algorithms
Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers
Get PDFMany approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifier
Channel Propagation Characteristics on the Performance of 4G Cellular Systems from High Altitude Platforms (HAPs)
Get PDFIn this paper, we investigated the effect of different channel propagation characteristics on the performance of 4G systems from high altitude platforms (HAPs). The use of High-Altitude Platforms for communication purpose in the past focused mostly on the assumption that the platform is quasi stationary. The technical limitation of the assumption was that of ensuring stability in the positioning of the platform in space. The use of antenna steering and other approaches were proposed as a solution to the said problem. In this paper, we proposed a channel model which account for the motion of the platform. This was done by investigating the effect of Doppler shift on the carrier frequency as the signals propagate between the transmitter and receiver while the High-Altitude Platform is in motion. The basic free space model was used and subjected to the frequency variation caused by the continuous random shift due to the motion of the HAPs. The trajectory path greatly affects the system performance. A trajectory of 30km, 100km and 500km radii were simulated. An acute elevation angle was used in the simulation. The proposed model was also compared to two other channel models to illustrate its performance. The results show that the proposed model behave similar to the existing models except at base station ID 35 and 45 where the highest deviation of 20dBm was observed. Other stations that deviated were less than 2dBm
Investigating the effect of High Altitude Platform Positioning on Latency and Coverage of 4G Cellular Systems
Get PDFWireless communication technologies are rapidly being adopted and developed by countries all over the world as a strategy for sustaining a digital economy. This has proven very useful for economic recovery from the crises brought about by the COVID-19 pandemic of the year 2020. The latency and coverage area of a wireless network are two major areas that are always seeking improvement. The High Altitude Platform communication technology can provide improvement in speed and coverage area for 4G cellular systems. This work investigated the effect of positioning High Altitude Platforms on the latency and coverage of 4G cellular Systems. A quantitative approach was used in the methodology of this paper. A HAP model showing a single platform flying in a circular trajectory over Base Transceiver Stations BTSs and serving as a relay mobile station was presented. A detailed simulation algorithm for the HAP and results for the simulation were given. Results showed that using the HAP as a relay mobile station in a network can give a latency reduction of up to 58.9%. Also, the altitude of the HAP directly affects the angle of reception which was found to improve the coverage
A PREDICTIVE USER BEHAVIOUR ANALYTIC MODEL FOR INSIDER THREATS IN CYBERSPACE
Get PDFInsider threat in cyberspace is a recurring problem since the user activities in a cyber network are often unpredictable. Most existing solutions are not flexible and adaptable to detect sudden change in user’s behaviour in streaming data, which led to a high false alarm rates and low detection rates. In this study, a model that is capable of adapting to the changing pattern in structured cyberspace data streams in order to detect malicious insider activities in cyberspace was proposed. The Computer Emergency Response Team (CERT) dataset was used as the data source in this study. Extracted features from the dataset were normalized using Min-Max normalization. Standard scaler techniques and mutual information gain technique were used to determine the best features for classification. A hybrid detection model was formulated using the synergism of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models. Model simulation was performed using python programming language. Performance evaluation was carried out by assessing and comparing the performance of the proposed model with a selected existing model using accuracy, precision and sensitivity as performance metrics. The result of the simulation showed that the developed model has an increase of 1.48% of detection accuracy, 4.21% of precision and 1.25% sensitivity over the existing model. This indicated that the developed hybrid approach was able to learn from sequences of user actions in a time and frequency domain and improves the detection rate of insider threats in cyberspace