No abstract

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

Distributed denial of service (DDoS) attacks are a major threat to any network-based service provider. The ability of an attacker to harness the power of a lot of compromised devices to launch an attack makes it even more complex to handle. This complexity can increase even more when several attackers coordinate to launch an attack on one victim. Moreover, attackers these days do not need to be highly skilled to perpetrate an attack. Tools for orchestrating an attack can easily be found online and require little to no knowledge about attack scripts to initiate an attack. Studies have been done severally to develop defense mechanisms to detect and defend against DDoS attacks. As defense schemes are designed and developed, attackers are also on the move to evade these defense mechanisms and so there is a need for a continual study in developing defense mechanisms. This paper discusses the current DDoS defense mechanisms, their strengths and weaknesses.

Wireless Sensor Network (WSN) localization is an important and fundamental problem that has received a lot of attention from the WSN research community. Determining the absolute and relative coordinate of sensor nodes in the network adds much more meaning to sense data. The research community is very rich in proposals to address this challenge in WSN. This paper, explores the various techniques proposed to address the acquisition of location information in WSN. The paper also evaluate the performance of these techniques based on the energy consumption, the skill and man hours needed to implement the technique and localization accuracy (error rate) and discuss some open issues for future research. KeywordsWireless sensor network, sensor nodes, localization centralized and distributed algorithm, range-based and rangefree algorithm.

The security of a wireless sensor network is greatly increased with increasing levels of trustworthiness of nodes on the network. There are a number of trust models proposed that use various trust evaluation methods. The paper categorises trust evaluation methods for wireless sensor networks into socio-inspired, bio-inspired, and analytical methods. Biologically inspired methods and socio constructs are rarely used in the design of trust schemes as opposed to analytical methods (probability, agent based, fuzzy, etc). The paper discusses existing bio-and socio-inspired-based trust schemes implemented for wireless sensor networks, viz quality-based distance vector protocol, enhanced bio-inspired trust and reputation model for wireless sensor network, bio-inspired trust routing protocol, machine learning-based bio-inspired trust and reputation model, socio-psychological trust and reputation model, and finally reputation framework for sensor network. The contributions, trust evaluation methods and limitations of recent trust models (i.e. analytical based, biological and socio-inspired models) are discussed in this paper. The pros and cons of analytical, socio, and biologically inspired algorithms for computing are outlined to easily ascertain the relevance of adopting either biological or socio-inspired approaches for solving trust-related problems in wireless sensor networks. KEYWORDSbio-inspired algorithms, socio-inspired algorithms, trust models, wireless sensor network Int J Commun Syst. 2018;31:e3444.wileyonlinelibrary.com/journal/dac

The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time-variant weights for the velocity of the particle swarm optimization and the non-dominated sorting and mutation schemes from NSGA-III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA-II), and the Pareto Envelope-based Selection Algorithm with region-based selection (PESA-II), and NSGA-III. The proposed AT-MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT-MOPSO achieved 52% energy efficiency compared to NSGA-III. To show how this algorithm can be applied to a real-world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT-MOPSO can be used with existing Blockchain systems and the benefits it provides.

In Ghana and many countries within Sub-Sahara Africa, Long Term Evolution (LTE) is being considered for use within the sectors of Governance, Energy distribution and transmission, Transport, Education and Health. Subscribers and Governments within the region have high expectations for these new networks and want to leverage the promised enhanced coverage and high data rates for development. Recent performance evaluations of deployed WiMAX networks in Ghana showed promising performance of a wireless broadband technology in supporting the capacity demands needed in the peculiar Sub-Saharan African terrain. The deployed WiMAX networks, however could not achieve the optimal quality of service required for providing a seamless wireless connectivity demands needed for emerging mobile applications. This paper evaluates the performance of some selected key network parameters of a newly deployed LTE network in the 2600 MHz band operating in the peculiar Sub-Saharan African terrain under varied MIMO Antenna Configurations. We adopted simulation and field measurement to aid us in our evaluation. Genex Unet has been used to simulate network coverage and throughput performance of 2X2, 4X4 and 8X8 MIMO configurations of the deployed networks. The average simulated throughput per sector of 4X4 MIMO configuration was seen to be better than the 2X2 configuration. However, the percentage coverage for users under the 2x2 MIMO simulation scenario was better than that of the adaptive 4x4 MIMO configuration with 2x2 MIMO achieving 60.41% of coverage area having throughput values between 1 -40Mbps as against 55.87% achieved by the 4x4 MIMO configuration in the peculiar deployment terrain.

Digitization and automation have engulfed every scope and sphere of life. Internet of Things (IoT) has been the main enabler of the revolution. There still exist challenges in IoT that need to be addressed such as the limited address space for the increasing number of devices when using IPv4 and IPv6 as well as key security issues such as vulnerable access control mechanisms. Blockchain is a distributed ledger technology that has immense benefits such as enhanced security and traceability. Thus, blockchain can serve as a good foundation for applications based on transaction and interactions. IoT implementations and applications are by definition distributed. This means blockchain can help to solve most of the security vulnerabilities and traceability concerns of IoTs by using blockchain as a ledger that can keep track of how devices interact, in which state they are and how they transact with other IoT devices. IoT applications have been mainly implemented with technologies such as cloud and fog computing, and AI to help address some of its key challenges. The key implementation challenges and technical choices to consider in making a successful blockchain IoT (BIoT) project are clearly outlined in this paper. The security and privacy aspect of BIoT applications are also analyzed, and several relevant solutions to improve the scalability and throughput of such applications are proposed. The paper also reviews integration schemes and monitoring frameworks for BIoT applications. A hybrid blockchain IoT integration architecture that makes use of containerization is proposed.