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

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

Optimized routing algorithm for mobile multicast source in Wireless Mesh Networks

Mobility considerations in multicast algorithm design usually focus issues of mobile receivers while few researches emphasize on mobile multicast sources issues. However, mobility of source is a critical issue causing global tree disruption. Furthermore, major research challenges in the delivery of group applications over Wireless Mesh Networks (WMN) comprise delivery of large volume data, incurring large bandwidth and delay. This is in addition to multicast source mobility issues and interfering nature of scarce wireless resources. Mobile multicast scenarios emerge in group-based distribution of information such as obtains in the health sector, disaster recovery operation, remote bus, train control and video surveillance. The mobile multicast problem in this paper is formulated as a bandwidth and delay constrained minimum tree cost problem which is proved to be computationally intensive. Thus this paper proposes a Differential Evolution based optimized mobile multicast routing algorithm for the shared tree architecture. The proposed algorithm is implemented using MATLAB and the evaluation considers analytic and simulation techniques for convergence, scalability and multicast tree stability under source movement. Simulation results show that the proposed algorithm converges and outperforms a source based tree algorithm

Modelling of intelligent intrusion detection system: making a case for snort

Intrusion Detection System (IDS) is a dynamic network security defense technology that can help to provide realtime detection of internal and external attacks on a computer network and alerting the administration for necessary action. However, the inconsistent nature of networks has resulted in a high number of false positives which makes many network administrators thought IDS to be unreliable for today’s network security system. Nowadays, hackers and attackers have created many new viruses and malware to invade one’s computer network system. Hence, this study proposes a method for early detection of an intrusion by using Snort software. The data collected was used to train the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm was simulated using MATLAB software. The performance of this classifier was evaluated based on three parameters: accuracy, sensitivity, and False Positive Rate (FPR). Preprocessing was done to classify the output data into normal and attack. Performance evaluation was done using confusion matrix on the data. The results showed that network-based intrusion detection system could be employed for early detection of intrusion due to the excellent performance recorded which were 94.92% of accuracy, 97.97% for sensitivity, and 0.69% for FP

Differential evolution optimization for constrained routing in Wireless Mesh Networks

An important consideration in efficient routing design is the nature of access network and application requirements. Consequently, routing algorithm designed for general mobile ad hoc networks may not be adequate for Wireless Mesh Networks (WMN) deployment because of significant architectural differences. Furthermore, recent Internet traffic dominated by video data transmission in real-time requires path selection metrics for handling delay stringent nature of such traffic. Incidentally, the mandatory protocol defined for IEEE802.11s WMN implements layer 2 routing based in part on AODV; and even AODV expends enormous route processing on route discovery and maintenance for mobile routing nodes; which constitute overheads in WMN because of its static routing nodes in a rather stable topology with major traffic directed to and from Internet gateway. Thus this paper studies multiple constraints routing problem for path cost minimization over WMN. However, this problem is NP-complete, hence, this paper proposes fast convergent Differential Evolution metaheuristic algorithm with bandwidth and delay constraints for minimum routing cost. This solution addresses efficient and optimal routing path construction for cost and quality metrics of the application. Simulation on NS2 proves its performance advantages over AODV protocol

A deep learning approach to concrete water-cement ratio prediction

Concrete is a versatile construction material, but the water content can greatly influence its quality. However, using the trials and error method to determine the optimum water for the concrete mix results in poor quality concrete structures, which often end up in landfills as construction wastes, thus threatening environmental safety. This paper develops deep neural networks to predict the required water for a normal concrete mix. Standard data samples obtained from certified/leading laboratories were fed into a deep learning model (multilayers feedforward neural network) to automate the calibration of mixing power of the concrete water content for improved water control accuracy. We randomly split the data into 70%, 15% and 15%, respectively, to train, validate and test the model. The developed DNN model was subjected to relevant statistical metrics and benchmarked against the random forest, gradient boosting machines, and support vector machines. The performance indices obtained by the DNN model have the highest reliability compared to other models for concrete water prediction