Muneer Bani Yassein

A new dynamic counter-based broadcasting scheme for Mobile Ad hoc Networks

Broadcasting is an essential operation in Mobile Ad hoc Networks (MANETs) to transmit a message (data packet) from the sender to the rest of the network nodes. Although flooding is the simplest mechanism for broadcasting, where each node retransmits every uniquely received message exactly once, it is usually costly and results in serious redundancy, contention and collisions in the network. These problems are widely referred to as the broadcast storm problem. In the light of this, this study introduces a new counter-based broadcasting scheme to achieve efficient broadcasting in MANETs. This is achieved by using a counter-based scheme with a dynamic threshold to increase the successful delivery rate of packets and enhance the throughput of the network. Extensive simulation experiments have been conducted. Our results show that the new scheme outperforms the well known exiting schemes, namely the two counter-based broadcasting scheme and blind flooding.

A new probabilistic broadcasting scheme for mobile ad hoc on-demand distance vector (AODV) routed networks

Broadcast is a fundamental operation used in Mobile Ad hoc NETworks (MANETs) for many services, such as route discovery and sending information messages. The direct method to perform broadcast is a simple flooding, which can decrease the performance of MANET. Recently, a probabilistic approach to flooding has been proposed as one of most important suggested solutions to solve the broadcast storm problem, which leads to the collision, contention and duplicated messages. This paper presents the Smart Probabilistic Broadcasting (SPB) as a new probabilistic method to improve the performance of existing on-demand routing protocols by reducing the RREQ overhead during the rout discovery operation. The simulation results show that the combination of AODV and a suitable probabilistic rout discovery can reduce the average end-to-end delay as well as overhead, while achieving low normalized routing load, compared to AODV that uses fixed probability and blind flooding. Simulation experiments have been conducted to examine our proposed scheme. The results show that SPB outperforms its counterparts and opens up a promising framework towards optimal probabilistic broadcasting.

A resource-efficient encryption algorithm for multimedia big data

Nowadays, multimedia is considered to be the biggest big data as it dominates the traffic in the Internet and mobile phones. Currently symmetric encryption algorithms are used in IoT but when considering multimedia big data in IoT, symmetric encryption algorithms incur more computational cost. In this paper, we have designed and developed a resource-efficient encryption system for encrypting multimedia big data in IoT. The proposed system takes the advantages of the Feistel Encryption Scheme, an Advanced Encryption Standard (AES), and genetic algorithms. To satisfy high throughput, the GPU has also been used in the proposed system. This system is evaluated on real IoT medical multimedia data to benchmark the encryption algorithms such as MARS, RC6, 3-DES, DES, and Blowfish in terms of computational running time and throughput for both encryption and decryption processes as well as the avalanche effect. The results show that the proposed system has the lowest running time and highest throughput for both encryption and decryption processes and highest avalanche effect with compared to the existing encryption algorithms. To satisfy the security objective, the developed algorithm has better Avalanche Effect with compared to any of the other existing algorithms and hence can be incorporated in the process of encryption/decryption of any plain multimedia big data. Also, it has shown that the classical and modern ciphers have very less Avalanche Effect and hence cannot be used for encryption of confidential multimedia messages or confidential big data. The developed encryption algorithm has higher Avalanche Effect and for instance, AES in the proposed system has an Avalanche Effect of %52.50. Therefore, such system is able to secure the multimedia big data against real-time attacks.

Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

Abstract Efficiently detecting network intrusions requires the gathering of sensitive information. This means that one has to collect large amounts of network transactions including high details of recent network transactions. Assessments based on meta-heuristic anomaly are important in the intrusion related network transaction data’s exploratory analysis. These assessments are needed to make and deliver predictions related to the intrusion possibility based on the available attribute details that are involved in the network transaction. We were able to utilize the NSL-KDD data set, the binary and multiclass problem with a 20% testing dataset. This paper develops a new hybrid model that can be used to estimate the intrusion scope threshold degree based on the network transaction data’s optimal features that were made available for training. The experimental results revealed that the hybrid approach had a significant effect on the minimisation of the computational and time complexity involved when determining the feature association impact scale. The accuracy of the proposed model was measured as 99.81% and 98.56% for the binary class and multiclass NSL-KDD data sets, respectively. However, there are issues with obtaining high false and low false negative rates. A hybrid approach with two main parts is proposed to address these issues. First, data needs to be filtered using the Vote algorithm with Information Gain that combines the probability distributions of these base learners in order to select the important features that positively affect the accuracy of the proposed model. Next, the hybrid algorithm consists of following classifiers: J48, Meta Pagging, RandomTree, REPTree, AdaBoostM1, DecisionStump and NaiveBayes. Based on the results obtained using the proposed model, we observe improved accuracy, high false negative rate, and low false positive rule.

A multithreaded programming approach for multimedia big data: encryption system

Multimedia is currently seen to dominate the internet network and the mobile network traffic; hence, it is seen as the largest Big data. Generally, the symmetric encryption algorithms are applied to the ‘big multimedia data’; however; these algorithms are thought as very slow. In our study, we developed and designed a resource-efficient encryption algorithm system which applies the multithreaded programming process for the encryption of the big multimedia data. This proposed system describes a multi-level encryption model which uses the Feistel Encryption Scheme, genetic algorithms and the Advanced Encryption Standard (AES). Our system has been assessed for actual medical-based big multimedia data and compared to the benchmarked encryption algorithms like the RC6, MARS, 3-DES, DES, and Blowfish with regards to the computational run time and its throughput for the encryption and decryption procedures. In addition, the multithreaded programming approach is adopted to implement the proposed encryption system in order to enhace the system effeciencey and porfermance. Furthermore, we also compared our system with its sequential version for showing its resource efficiency. Our results indicated that our system had the least run time and a higher throughput for the encryption and decryption processes in comparison to the already existing standard encryption algorithms. Also, our system could improve the computation run time by approximately 75% and its throughput was also increased by 4-times in comparison to its sequential version. For fulfilling the security objectives, our algorithm showed a better Avalanche Effect in comparison to the existing algorithms and therefore, could be included in any encryption/decryption process of a big plain multimedia data.

A Conceptual Security Framework for Cloud Computing Issues

In this article, perspectives from Cloud computing practitioners are shown in order to address clients concerns and bring about awareness of the measures that put in place to ensure software security of the client services running in the Cloud. In addition, the authors have investigated the impacts of a number of the existing approaches and techniques to put a systematic survey of the current software security issues in the Cloud environment. Based on such perspectives and survey, a generic framework conceptually is designed to outline the possible current solutions of software security issues in the Cloud and to present a preferred software security approach to investigate the Cloud research community. As a potential enhancement on the proposed Cloud software security framework, the concepts of fuzzy systems might be used to solve a large numbers of issues in the Cloud security on different framework levels.

A new elastic trickle timer algorithm for Internet of Things

The term IoT emerging services is used to refer to the modern kinds of services that IoT can provide to enhance service and experience quality by reducing complexity, speeding up requests, and using techniques involved with cloud, Big Data, and protocols to allows services to function seamlessly. Therefore, IoT utilises protocols found within different network layers. RPL or Routing Protocol for Low Power and Lossy Networks is one of the most important routing protocols utilised on the network layer. This protocol is considered an IPV6 distance vector proactive routing protocol. The trickle timer algorithm is one of its major components. This algorithm is used to control and track the control messages flow throughout the network. However, one weakness of the trickle algorithm is that it suffers from short listen problem that makes some nodes starve for delay and long latency in propagating transmissions. Based on existing literature, there have been several research studies on this trickle method. Development of the Enhanced Trickle algorithm (E-Trickle) was done to get rid of the listen only period. However, there still is relatively low activity on studying the trickle algorithms performance merits. Our proposed elastic trickle timer algorithm will try to fill this gap by dealing only with the listen to only period problems. The power consumption and convergence time are mainly affected. Therefore, our proposed algorithm was incorporated in the Routing Protocol for Low Power and Lossy Networks (RPL). Different network densities were used to evaluate the simulation experiments. Its implementation was done on 20, 40, 60, and 80 nodes using different ratios for reception success (RX) (20%, 40%, 60%, 80%, and 100%), and grid network and random topologies were used. The Cooja 2.7 simulator was used to implement the simulation experiments, and RPL performance was studied through the elastic trickle timer algorithm. Measurement of the simulation experiments was done on various performance metrics such as packet delivery ratio (PDR), convergence time, and power consumption. Comparison of the results was done using the standard trickle timer algorithm. Using random and grid topologies, the results revealed greater enhancements in terms of convergence time. Simulation results revealed that when the network was made up of 20 nodes, there was 35% less convergence time. Moreover, when 40 nodes were randomly placed, there was 62% less convergence time, and 71% less convergence time when 40 nodes were situated in a grid topology. Additionally, there was 70% less convergence time when the network was made up of 60 nodes and approximately 76% less convergence time when there were 80 nodes. Moreover, the simulation results revealed that in terms of energy consumption, the new algorithm exhibited superior performance characteristics.

A Resource Intensive Traffic-Aware Scheme for Cluster-based Energy Conservation in Wireless Devices

Wireless traffic that is destined for a certain device in a network, can be exploited in order to minimize the availability and delay trade-offs, and mitigate the Energy consumption. The Energy Conservation (EC) mechanism can be node-centric by considering the traversed nodal traffic in order to prolong the network lifetime. This work describes a quantitative traffic-based approach where a clustered Sleep-Proxy mechanism takes place in order to enable each node to sleep according to the time duration of the active traffic that each node expects and experiences. Sleep-proxies within the clusters are created according to pairwise active-time comparison, where each node expects during the active periods, a requested traffic. For resource availability and recovery purposes, the caching mechanism takes place in case where the node for which the traffic is destined is not available. The proposed scheme uses Role-based nodes which are assigned to manipulate the traffic in a cluster, through the time-oriented backward difference traffic evaluation scheme. Simulation study is carried out for the proposed backward estimation scheme and the effectiveness of the end-to-end EC mechanism taking into account a number of metrics and measures for the effects while incrementing the sleep time duration under the proposed framework. Comparative simulation results show that the proposed scheme could be applied to infrastructure-less systems, providing energy-efficient resource exchange with significant minimization in the power consumption of each device.

A framework for retrieving Arabic documents based on queries written in Arabic slang language

Due to the widespread use of the internet, there are large amounts of information and documents available in several languages. The Arabic language is one of the available important languages in terms of its usage and structure. Search engines like Google and Yahoo support searching in Arabic, yet fail to get good results when slang terms are used in the query. There are difficulties associated with the Arabic language. The main goal of this research is to refine Arabic text-based searching by using Arabic slang terms in queries. This research proposed a framework to enable users to use their slang language in order to retrieve the relevant documents that have been posted in both forms – slang and classical. The framework is designed and implemented based on a context-free grammar that is used to map the user’s slang queries to the equivalent classical ones. On a classical dataset, results showed a 3% improvement on the average values of precision, recall, and F-measure achieved using classical-based queries rather than slang-based ones. Using slang-based queries gives 13% improvement on the average values of the used measures on a slang dataset and 7% improvement on the average values of the used measures on a hybrid dataset.

Enhanced Fibonacci Backoff Algorithm for Mobile Ad-Hoc Network

The most known drawback of IEEE 802.11 MAC is the low performance in terms of throughput in case of congested networks. Backoff time is one of the most important factors that govern the collision probability in Ad-Hoc network. Using backoff algorithm could push the performance up and decrease collision rate. This study presents an enhanced fibonacci backoff Algorithm (EFB) for Ad-Hoc Network in which contention window size is selected from two choices: linear and Fibonacci. Simulation results show high performance and less average of end to end delay especially at high number of nodes compared to Binary Exponential Backoff algorithm (BEB).