On the performance of traffic-aware reactive routing in MANETs

Research on mobile ad hoc networks (MANETs) has intensified over recent years, motivated by advances in wireless technology and also by the range of potential applications that might be realised with such infrastructure-less networks. Much work has been devoted to developing reactive routing algorithms for MANETs which generally try to find the shortest path from source to destination. However, this approach can lead to some nodes being loaded much more than others in the network. As resources, such as node power and channel bandwidth, are often at a premium in MANETs, it is important to optimise their usage as far as possible. Incorporating traffic aware techniques into routing protocols in order to distribute load among the network nodes would helps to ensure fair utilisation of nodes' resources, and prevent the creation of congested regions in the network. A number of such traffic aware techniques have been proposed. These can be classified into two main categories, namely end-to-end and on-the-spot, based on the method of establishing and maintaining routes between source and destination. In the first category, end-to-end information is collected along the path with intermediate nodes participating in building routes by adding information about their current load status. However the decision as to which path to select is taken at one of the endpoints. In the second category, the collected information does not have to be passed to an endpoint to make a path selection decision as intermediate nodes can do this job. Consequently, the decision of selecting a path is made locally, generally by intermediate nodes. Existing end-to-end traffic aware techniques use some estimation of the traffic load. For instance, in the traffic density technique, this estimation is based on the status of the MAC layer interface queue, whereas in the degree of nodal activity technique it is based on the number of active flows transiting a node. To date, there has been no performance study that evaluates and compares the relative performance merits of these approaches and, in the first part of this research, we conduct such a comparative study of the traffic density and nodal activity approaches under a variety of network configurations and traffic conditions. The results reveal that each technique has performance advantages under some working environments. However, when the background traffic increases significand, the degree of nodal activity technique demonstrates clear superiority over traffic density. In the second part of this research, we develop and evaluate a new traffic aware technique, referred to here as load density, that can overcome the limitations of the existing techniques. In order to make a good estimation of the load, it may not be sufficient to capture only the number of active paths as in the degree of nodal activity technique or estimate the number of packets at the interface queue over a short period of time as in the traffic density technique. This is due to the lack of accuracy in measuring the real traffic load experienced by the nodes in the network, since these estimations represent only the current traffic, and as a result it might not be sufficient to represent the load experienced by the node over time which has consumed part of its battery and thus reduced its operational lifetime. The new technique attempts to obtain a more accurate picture of traffic by using a combination of the packet length history at the node and the averaged number of packets waiting at node's interface queue. The rationale behind using packets sizes rather than just the number of packets is that it provides a more precise estimation of the volume of traffic forwarded by a given node. Our performance evaluation shows that the new technique makes better decisions than existing ones in route selection as it preferentially selects less frequently used nodes, which indeed improves throughput and end-to-end delay, and distributes load more, while maintaining a low routing overhead. In the final part of this thesis, we conduct a comparative performance study between the end-to-end and on-the-spot approaches to traffic aware routing. To this end, our new load density technique has been adapted to suggest a new "on-the-spot" traffic aware technique. The adaptation is intended to ensure that the comparison between the two approaches is fair and realistic. Our study shows that in most realistic traffic and network scenarios, the end-to-end performs better than the local approach. The analysis also reveals that relying on local decisions might not be always good especially if all the potential paths to a destination pass through nodes with an overload condition in which case an optimal selection of a path may not be feasible. In contrast, there is most often a chance in the end-to-end approach to select the path with lower load

Authentication techniques in smart grid: a systematic review

Smart Grid (SG) provides enhancement to existing grids with two-way communication between the utility, sensors, and consumers, by deploying smart sensors to monitor and manage power consumption. However due to the vulnerability of SG, secure component authenticity necessitates robust authentication approaches relative to limited resource availability (i.e. in terms of memory and computational power). SG communication entails optimum efficiency of authentication approaches to avoid any extraneous burden. This systematic review analyses 27 papers on SG authentication techniques and their effectiveness in mitigating certain attacks. This provides a basis for the design and use of optimized SG authentication approaches

A Secure Cloud Computing Model based on Data Classification

AbstractIn cloud computing systems, the data is stored on remote servers accessed through the internet. The increasing volume of personal and vital data, brings up more focus on storing the data securely. Data can include financial transactions, important documents, and multimedia contents. Implementing cloud computing services may reduce local storage reliance in addition to reducing operational and maintenance costs. However, users still have major security and privacy concerns about their outsourced data because of possible unauthorized access within the service providers. The existing solutions encrypt all data using the same key size without taking into consideration the confidentiality level of data which in turn will increase the cost and processing time. In this research, we propose a secure cloud computing model based on data classification. The proposed cloud model minimizes the overhead and processing time needed to secure data through using different security mechanisms with variable key sizes to provide the appropriate confidentiality level required for the data. The proposed model was tested with different encryption algorithms, and the simulation results showed the reliability and efficiency of the proposed framework

Advanced security testing using a cyber-attack forecasting model: A case study of financial institutions

As the number of cyber-attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber-security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network-based cyber-attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber-attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber-crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures.Dado que el número de ciberataques a instituciones financieras ha aumentado en los últimos años, es esencial contar con un sistema avanzado que sea capaz de predecir el objetivo de un ataque. Un sistema de este tipo debe integrarse en los sistemas de detección existentes de las instituciones financieras, ya que les proporciona controles proactivos con los que detener un ataque mediante la predicción de patrones. Los sistemas de predicción avanzados también mejoran el diseño de software y las pruebas de seguridad de nuevas medidas avanzadas de ciberseguridad al proporcionar nuevos escenarios de prueba respaldados por la previsión de ataques. Este presente estudio desarrolló un modelo que pronostica futuros ciberataques basados ​​en redes contra instituciones financieras utilizando una red neuronal profunda. El conjunto de datos que se utilizó para entrenar y probar el modelo consistió en algunos de los mayores ataques cibernéticos a instituciones bancarias en los últimos tres años. Esto proporcionó información sobre nuevos patrones que pueden terminar en un delito cibernético. Estos nuevos ataques también fueron evaluados para determinar similitudes de comportamiento con el ataque conocido más cercano o una combinación de varios ataques existentes. Luego se evaluó el desempeño del modelo de pronóstico en un entorno bancario real y proporcionó una precisión de pronóstico del 90,36%. Como tal, las instituciones financieras pueden utilizar el modelo de pronóstico propuesto para mejorar sus medidas de prueba de seguridad.2022-2

Software Design and Experimental Evaluation of a Reduced AES for IoT Applications

IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but conventional cryptographic algorithms are too heavyweight and resource-demanding to run on IoT infrastructures. This paper presents a lightweight version of AES (called LAES), which provides competitive results in terms of randomness levels and processing time, operating on GF(24). Detailed mathematical operations and proofs are presented concerning LAES rounds design fundamentals. The proposed LAES algorithm is evaluated based on its randomness, performance, and power consumption; it is then compared to other cryptographic algorithm variants, namely Present, Clefia, and AES. The design of the randomness and performance analysis is based on six measures developed with the help of the NIST test statistical suite of cryptographic applications. The performance and power consumption of LAES on a low-power, 8-bit microcontroller unit were evaluated using an Arduino Uno board. LAES was found to have competitive randomness levels, processing times, and power consumption compared to Present, Clefia, and AES