Lightweight Agents, Intelligent Mobile Agent and RPC Schemes: A Comparative Analysis

This paper presents the performance comparison of Lightweight Agents, Single Mobile Intelligent Agents and Remote Procedure Call which are tools for implementing communication in a distributed computing environment. Routing algorithms for each scheme is modeled based on TSP. The performance comparison among the three schemes is based on bandwidth overhead with retransmission, system throughput and system latency. The mathematical model for each performance metric is presented, from which mathematical model is derived for each scheme for comparison. The simulation results show that the LWAs has better performance than the other two schemes in terms of small bandwidth retransmission overhead, high system throughput and low system latency. The Bernoulli random variable is used to model the failure rate of the simulated network which is assumed to have probability of success p = 85% and the probability of failure q = 15%. The network availability is realized by multiplicative pseudorandom number generator during the simulation. The results of simulation are presented

A Prey-Predator Defence Mechanism For Ad Hoc On-Demand Distance Vector Routing Protocol

This study proposes a nature-based system survivability model. The model was simulated, and its performance was evaluated for the mobile ad hoc wireless networks. The survivability model was used to enable mobile wireless distributed systems to keep on delivering packets during their stated missions in a timely manner in the presence of attacks. A prey-predator communal defence algorithm was developed and fused with the Ad hoc On-demand Distance Vector (AODV) protocol. The mathematical equations for the proposed model were formulated using the Lotka-Volterra theory of ecology. The model deployed a security mechanism for intrusion detection in three vulnerable sections of the AODV protocol. The model simulation was performed using MATLAB for the mathematical model evaluation and using OMNET++ for protocol performance testing. The MATLAB simulation results, which used empirical and field data, have established that the adapted Lotka-Volterra-based equations adequately represent network defense using the communal algorithm. Using the number of active nodes as a measure of throughput after attack (with a maximum throughput of 250 units), the proposed model had a throughput of 230 units while under attack and the intrusion was nullified within 2 seconds. The OMNET++ results for protocol simulation that use throughput, delivery ratio, network delay, and load as performance metrics with the OMNET++ embedded datasets showed good performance of the model, which was better than the existing conventional survivability systems. The comparison of the proposed model with the existing model is also presented. The study concludes that the proposed communal defence model was effective in protecting the entire routing layer (layer 2) of the AODV protocol when exposed to diverse forms of intrusion attacks

A PREDICTIVE USER BEHAVIOUR ANALYTIC MODEL FOR INSIDER THREATS IN CYBERSPACE

Insider 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

An Enhanced Cluster-Based Routing Model for Energy-Efficient Wireless Sensor Networks

Energy efficiency is a crucial consideration in wireless sensor networks since the sensor nodes are resource-constrained, and this limited resource, if not optimally utilized, may disrupt the entire network's operations. The network must ensure that the limited energy resources are used as effectively as possible to allow for longer-term operation. The study designed and simulated an improved Genetic Algorithm-Based Energy-Efficient Routing (GABEER) algorithm to combat the issue of energy depletion in wireless sensor networks. The GABEER algorithm was designed using the Free Space Path Loss Model to determine each node's location in the sensor field according to its proximity to the base station (sink) and the First-Order Radio Energy Model to measure the energy depletion of each node to obtain the residual energy. The GABEER algorithm was coded in the C++ programming language, and the wireless sensor network was simulated using Network Simulator 3 (NS-3). The outcomes of the simulation revealed that the GABEER algorithm has the capability of increasing the performance of sensor network operations with respect to lifetime and stability period

Review of Livestock Feed Formulation Techniques

This paper reviews animal feed formulation methods, the conventional methods and intelligent system method. Highlighting their cons and pros. The intelligent system method (neuro-fuzzy) incorporated fuzzy conjunctive into levenberge training of artificial neural network. The neuro-fuzzy system was trained with dataset and validated using Amino acid elements of chicks feed. With 0.05 level of significance on NCCS 2000 platforms, output of the neuro-fuzzy system produced a correlation coefficient of 0.888608 and p-value of 0.97. Intelligent system can be employed to increase productivity in the field of animal feed formulation. Keywords: animal feed formulation, linear programming, neuro-fuzzy, ration

Signal Processing-based Model for Primary User Emulation Attacks Detection in Cognitive Radio Networks

Cognitive Radio Networks (CRNs) have been conceived to improve the efficiency of accessing the spectrum. However, these networks are prone to various kinds of attacks and failures that can compromise the security and performance of their users. One of the notable malicious attacks in cognitive radio networks is the Primary User Emulation (PUE) attack, which results in underutilization and unavailability of the spectrum and low operational efficiency of the network. This study developed an improved technique for detecting PUE attacks in cognitive radio networks and further addressed the characteristics of sparsely populated cognitive radio networks and the mobility of the primary users. A hybrid signal processing-based model was developed using the free space path loss and additive Gaussian noise models. The free space path loss model was used to detect the position of the transmitter, while the additive Gaussian noise model was used to analyze the signal transmitted, i.e., energy detection in the spectrum at the detected location. The proposed model was benchmarked with an existing model using the number of secondary users and the velocity of the transmitter as performance parameters. The simulation results show that the proposed model has improved accuracy in detecting primary user emulation attacks. It was concluded that the proposed hybrid model with respect to the number of secondary users and the velocity of the transmitter can be used for primary user emulation attack detection in cognitive radio networks

An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider

Existing bio-inspired models are challenged with premature convergence among others. In this paper, an adaptive social spider colony optimisation model based on the foraging behaviour of social spider was proposed as an optimisation problem. The algorithm mimics the prey capture behaviour of the social spider in which, the spider senses the presence of the prey through vibrations transmitted along the web thread. Spiders are the search agents while the web is the search space of the optimisation problem. The natural or biological phenomenon of vibration was modeled using wave theory while optimisation theory was considered in optimizing the objective function of the optimisation problem. This objective function was considered to be the frequency of vibration of the spiders and the prey as this is the function that enables the spider differentiates the vibration of the prey from that of neighbouring spiders and therefore forages maximally. To address the parameter tuning problem, the search pattern was controlled by the position of the prey for convergence. The proposed model was tested for convergence using several benchmark functions with different characteristics to evaluate its performance and results compared to an existing state of the arts’ spider algorithm. Results showed that the proposed model performed better by searching the optimum solution of the benchmark functions used to test the model