International Journal of Information Security
Volume:11 Issue: 3, Jul 2019

This paper explores the algebraic matching approach for detection of vulnerabilities in binary codes. The algebraic programming system is used for implementing this method. It is anticipated that models of vulnerabilities and programs to be verified are presented as behavior algebra and action language specifications. The methods of algebraic matching are based on rewriting rules and techniques with usage of conditional rewriting. This process is combined with symbolic modeling that gives a possibility to provide accurate detection of vulnerabilities. The paper provides examples of formalization of vulnerability models and translation of binary codes to behavior algebra expressions.

The Internet of Things (IoT) is a very encouraging and fast-growing area that brings together the benefits of wireless systems, sensor networks, actuators, etc.A wide range of IoT applications have been targeted and several aspects of this field have been identified to address specific issues, as well as technologies and standards developed in various domains such as in radio frequency identification(RFID), sensors, and mobile telephony, to name a few. This article aims to talk specifically about the RFID technology and its accompanying communication, authentication, risk, and security concerns while applied to the IoT field. An important part of this work is indeed focused on security aspects that derive from the use of RFID in IoT, especially in IoT networks. The results of our research work highlighted an excellent integration of RFID in the field of Internet of things, particularly in healthcare systems.

In the recent years, social networks (SN) are now employed for communication and networking, socializing, marketing, as well as one’s daily life. Billions of people in the world are connected though various SN platforms and applications, which results in generating massive amount of data online. This includes personal data or Personally Identifiable Information (PII). While more and more data are collected about users by different organizations and companies, privacy concerns on the SNs have become more and more prominent. In this paper, we present a study on information privacy in SNs through exploring the general laws and regulations on collecting, using and disclosure of information from Canadian perspectives based on the Personal Information Protection and Electronic Document Act (PIPEDA). The main focus of this paper is to present results from a survey and the findings of the survey.

Medical images show a great interest since it is needed in various medical applications. In order to decrease the size of medical images which are needed to be transmitted in a faster way; Region of Interest (ROI) and hybrid lossless compression techniques are applied on medical images to be compressed without losing important data. In this paper, a proposed model will be presented and assessed based on size of the image, the Peak Signal to Noise Ratio (PSNR),and the time that is required to compress and reconstruct the original image.The major objective of the proposed model is to minimize the size of image and the transmission time. Moreover, improving the PSNR is a critical challenge.The results of the proposed model illustrate that applying hybrid losslesstechniques on the ROI of medical images reduces size by 39% and gives better results in terms of the compression ratio and PSNR.

Wireless networks, Internet of Things (IoT), Internet of Everything (IoE), and smart homes have become extremely important terms in our present-day life. Most of the buildings, companies, institutions, and even homes depend onthese technologies for interaction, communication, automation, and everything surrounding humans. To understand the advanced topics in wireless networks and IoT devices, it is necessary to use one of the practical learning tools, calledPacket Tracer. This wireless network simulator is freely available by Cisco Networking Academy. In this paper, we will use Packet Tracer to design a smart home based on wireless and IoT devices and illustrate how to create different networking scenarios to make our homes more comfortable and convenient.

Standard face recognition algorithms that use standard feature extraction techniques always suffer from image performance degradation. Recently, singular value decomposition and low-rank matrix are applied in many applications,including pattern recognition and feature extraction. The main objective of this research is to design an efficient face recognition approach by combining many techniques to generate efficient recognition results. The implemented facerecognition approach is concentrated on obtaining significant rank matrix via applying a singular value decomposition technique. Measures of dispersion are used to indicate the distribution of data. According to the applied ranks, thereis an adequate reasonable rank that is important to reach via the implemented procedure. Interquartile range, mean absolute deviation, range, variance, and standard deviation are applied to select the appropriate rank. Rank 24, 12, and 6reached an excellent 100% recognition rate with data reduction up to 2 : 1, 4 : 1 and 8 : 1 respectively. In addition, properly selecting the adequate rank matrix is achieved based on the dispersion measures. Obtained results on standard face databases verify the efficiency and effectiveness of the implemented approach.

Internet of Things (IoT) approach is empowering smart city creativities all over the world. There is no specific tool or criteria for the evaluation of the services offered by the smart city. In this paper, a new Multilayer Fuzzy Inference System (MFIS) is proposed for the assessment of the Planet Factors of smart city (PFSC). The PFSC system is categorized into two levels. The proposed MFIS based expert system can categories the evaluation level of planet factors of the smart city into low, satisfied, or good.

Simplifying and structuring qualitatively complex knowledge, quantifying it in a certain way to make it reusable and easily accessible are all aspects that are not new to historians. Computer science is currently approaching a solution to some of these problems, or at least making it easier to work with historical data. In this paper, we propose a historical knowledge representation model taking into consideration the quality of imperfection of historical data in terms of uncertainty. To do this, our model design is based on a multilayer approach in which we distinguish three informational levels: information, source, and belief whose combination allows modeling and modulating historical knowledge. The basic principle of this model is to allow multiple historical sources to represent several versions of the history of a historical event with associated degrees of belief. In our model, we differentiated three levels of granularity (attribute, object, relation) to express belief and defined 11 degrees of uncertainty in belief. The proposed model can be the object of various exploitations that fall within the historian’s decision-making support for the plausibility of the history of historical events.

Time Saving and energy consumption has become a vital issue that attracts the attention of researchers in Underwater Wireless Sensor Networks (UWSNs) fields. According to that, there is a strong need to improve MAC protocols performance in UWSNs, particularly enhancing the effectiveness of ALOHA Protocol. In this paper, a time-saving Aloha protocol with slotted carrier sense proposed which we called, ST-Slotted-CS-ALOHA protocol. The results of thesimulation demonstrate that our proposed protocol can save time and decrease the average delay when it compared with the other protocols. Moreover, it decreased energy consumption and raised the ratio of throughput. However, the number of dropped nodes does not give better results compared to other protocols.

Port organizations have focused their efforts on physical or tangible assets, generating profitability and value. However, it is recognized that the greatest sustainable competitive advantage is the creation of knowledge using the intangible assets of the organization. The Balanced ScoreCard, as a performance tool, has incorporated intangible assets such as intellectual, structural and social capital into management. In this way, the port community can count on new forms of managing innovation, strengthening organizational practices, and increasing collaborative work teams. In this study, the concepts from analysis of the cognitive SWOT are applied to diagnose the port activity and its community. In workshops with experts and from the vision, mission, cognitive SWOT and strategies, a cognitive strategic map considering strategic objectives and indicators is designed in the customer, processes, and learning and growth axis for the port and port community. Causal relationships between objectives, associated indicators and incidence factors are established in a forward way from learning and growth axis to customer axis. Then, the incidence matrix is developed and the direct and indirect effects between factors are analyzed, which allows recommending the future course of the port and its community.

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes which is known to deteriorate the performance of classifiers. It also influences its validity and generalizablity. The classification models in this study were built using five machine learning algorithms known as PLS-DA, MLP, SVM, C4.5 and ID3. This model is built after carrying out a number of intensive data preprocessing procedures to tackle the problem of imbalanced classes and improve the performance of the constructed classifiers.These procedures involves applying data transformation, normalization, standardization, re-sampling and data reduction procedures using a number of variables importance scorers. The best performance was achieved by building an MLP model that was trained and tested using five-fold cross-validation using datasets that were re-sampled using SMOTE method and then reduced using SVM variable importance scorer. This model was successful in classifying samples with excellent accuracy and also in identifying the potential disease biomarkers. The results confirm the validity of metabolomics data mining for diagnosis of cachexia. It also emphasizes the importance of data preprocessing procedures such as sampling and data reduction for improving data mining results, particularly when data suffers from the problem of imbalanced classes.

Classifications of several gesture types are very helpful in several applications. This paper tries to address fast classifications of hand gestures using DTW over multi-core simple processors. We presented a methodology to distribute templates over multi-cores and then allow parallel execution of the classification. The results were presented to voting algorithm in which the majority vote was used for the classification purpose. The speed of processing has increased dramatically due to using multi-core processors and DTW.

With the emerging concept of model transformation, information can be extracted from one or more source models to produce the target models. The conversion of these models can be done automatically with specific transformation languages. This conversion requires mapping between both models with the help of dynamic hash tables. Hash tables store reference links between the elements of the source and target model. Whenever there is a need to access the target element, we query the hash table. In contrast, this paper presents an approach by directly creating aspects in the source meta-model with traces. These traces hold references to target elements during the execution. Illustrating the idea of model driven engineering (MDE), This paper proposes a method that transforms UML class models to EMF ECORE model.

The fifth generation (5G) system architecture is defined as service-based and the core network functions are described as sets of services accessible through application programming interfaces (API). One of the components of 5G is Multi-access Edge Computing (MEC) which provides the open access to radio network functions through API. Using the mobile edge API third party analytics applications may provide intelligence in the vicinity of end users which improves network performance and enhances user experience. In this paper, we propose new mobile edge API to access and control the mobility at the network edge. The application logic for provisioning access and mobility policies may be based on considerations like load level information per radio network slice instance, user location, accumulated usage, local policy, etc. We describe the basic API functionality by typical use cases and provide the respective data model, which represents the resource structure and data types. Some implementation aspects, related to modeling the resource states as seen by a mobile edge application and by the network, are discussed.

Standard TCP is the de facto reliable transfer protocol for the Internet. It is designed to establish a reliable connection using only a single network interface. However, standard TCP with single interfacing performs poorly due to intermittent node connectivity. This requires the re-establishment of connections as the IP addresses change. Multi-path TCP (MPTCP) has emerged to utilize multiple network interfaces in order to deliver higher throughput. Resilience to link failures can be better supported in MPTCP as the segments’ communication are maintained via alternative interfaces. In this paper, the resilience of MPTCP to link failures against several challenges is evaluated. Several link failure scenarios are applied to examine all aspects of MPTCP including congestion algorithms, path management, and subflow scheduling. In each scenario, the behavior of MPTCP is studied by observing and analyzing the throughput and delay. The evaluation of the results indicates MPTCP resilience to a low number of failed links. However, as the number of failed links increases, MPTCP can only recover full throughput if the link failure occurs on the server side. In addition, in the presence of link failures, the lowestRTT MPTCP scheduler yields the shortest delivery time while providing the minimum application jitter.

This paper presents an investigation on the performance of the Non-Orthogonal Multiple Access (NOMA) in the power domain scheme. A Power Allocation (PA) method is proposed from NOMA throughput expression analysis. This method aims to provide fair opportunities for users to improve their performance. Thus, NOMA users can achieve rates higher than, or equal to, the rates obtained with the conventional Orthogonal Multiple Access (OMA) in the frequency domain schemes. The proposed method is evaluated and compared with others PA techniques by computer system level simulations. The results obtained indicate that the proposed method increases the average cell spectral efficiency andmaintains a good fairness level with regard to the resource allocation among the users within a cell.

Data Virtualization (DV) has become an important method to store and handle data cost-efficiently. However, it is unclear what kind of data and when data should be virtualized or not. We applied a design science approach in the first stage to get a state of the art of DV regarding data integration and to present a concept matrix. We extend the knowledge base with a systematic literature review resulting in 15 critical success factors for DV. Practitioners can use these critical success factors to decide between DV and Extract, Transform, Load (ETL) as data integration approach.

The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error(RMSE).

With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, on behalf of the comparison of three algorithms like Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR). BR gives a better result with 60 hidden layers Neurons to other algorithms. BR gives 2.05 MSE and 95.8 regressions in Validation, LM gives 2.91 MSE and 94.06 regressions with this and SCG gives 3.92 MSE and 91.85 regressions.

Internet of Things (IoT) and cloud computing technologies have connected the infrastructure of the city to make the context-aware and more intelligent city for utility its major resources. These technologies have much potential to solve thechallenges of urban areas around the globe to facilitate the citizens. A framework model that enables the integration of sensor’s data and analysis of the data in the context of smart parking is proposed. These technologies use sensors anddevices deployed around the city parking areas sending real time data through the edge computers to the main cloud servers. Mobil-Apps are developed that used real time data, set from servers of the parking facilities in the city. Fuzzification is shown to be a capable mathematical approach for modeling city parking issues. To solve the city parking problems in cities a detailed analysis of fuzzy logic proposed systems is developed. This paper presents the resultsachieved using Mamdani Fuzzy Inference System to model complex smart parking system. These results are verified using MATLAB simulation.

Project management is an important factor to accomplish the decision to implement large-scale software systems (LSS) in a successful manner. The effective project management comes into play to plan, coordinate and control such a complex project. Project management factor has been argued as one of the important Critical Success Factor (CSF), which need to be measured and monitored carefully during the implementation of Enterprise Resource Planning(ERP) systems. The goal of this article is to develop âAœCSF-Live!âAI which is a method for measuring, monitoring, and controlling critical success factors of large-scale software systems. To achieve such goal, we apply CSF-Live for the project management CSF. The CSF-Live uses the Goal/Question/Metric paradigm (GQM) to yield a flexible framework containing several metrics that we used it to develop a formulation to enable the measurement of the project management CSF. The formulation that we developed for the project management CSF implies that the significance of having proper project management when conducting an ERP system implementation, since it is positively associated with the success of the ERP.

The Internet of Things (IoT) becomes the future of a global data field in which the embedded devices communicate with each other, exchange data and making decisions through the Internet. IoT could improve the quality of life in smart cities, but a massive amount of data from different smart devices could slow down or crash database systems. In addition, IoT data transfer to Cloud for monitoring information and generating feedback that will lead to high delay in infrastructure level. Fog Computing can help by offering services closer to edge devices. In this paper, we propose an efficient system architecture to mitigate the problem of delay. We provide performance analysis like response time, throughput and packet loss for MQTT (Message Queue Telemetry Transport) and HTTP (Hyper Text Transfer Protocol) protocols based on Cloud or Fog servers with large volume of data from emulated traffic generator working alongside one real sensor . We implement both protocols in the same architecture, with low cost embedded devices to local and Cloud servers with different platforms. The results show that HTTP response time is 12.1 and 4.76 times higher than MQTT Fog and Cloud based located in the same geographical area of the sensors respectively. The worst case in performance is observed when the Cloud is public and outside the country region. The results obtained for throughput shows that MQTT has the capability to carry the data with available bandwidth and lowest percentage of packet loss. We also prove that the proposed Fog architecture is an efficient way to reduce latency and enhance performance in Cloud based IoT.

In this paper, we implement a Virtualized Network Management Laboratory named (VNML) linked to college campus network for educational purposes. This laboratory is created using Virtualbox virtualizer and GNS3 on Linux UBUNTU single HP DL380 G7 server platform. A total of 35 virtual devices (Routers, Switches and Virtual Machines) are created and distributed over virtualized campus network with seven network management tools configured and run. The proposed laboratory is aimed to overcome the limitations of network hardware existence in any educational facility teach network management subject in their curriculum. The other advantages include ease of managing the laboratory and overrides physical location setup within the same geographical area.