Variable power transmission in highly Mobile Ad-Hoc Networks

Mobile Ad Hoc Networks pose challenges in terms of power control, due to their fixed transmission power, the mobility of nodes and a constantly changing topology. High levels of power are needed in wireless networks, particularly for routing. As a result of the increase in the number of communication devices being used, there is the challenge of increased density within these networks, and a need to extend the battery life of communication devices. In order to address this challenge, this thesis presents the development of a new protocol (Dynamic Power AODV), which is an enhancement of the Ad Hoc On Demand Distance Vector (AODV) protocol. The new protocol dynamically adjusts the transmission power based on the range, which depends on node density. This thesis provides a systematic evaluation of the performance of DP-AODV, in a high speed and high density environment, in comparison with three other routing protocols. The experiments demonstrated that DP-AODV performed better than two of the protocols in all scenarios. As compared to the third protocol (AOMDV), DP-AODV gave better performance results for throughput and Power Consumption, but AOMDV performed better in terms of Packet Delivery Fraction rate and End-to-End Delay in some cases

An ensemble based approach for effective intrusion detection using majority voting

Of late, Network Security Research is taking center stage given the vulnerability of computing ecosystem with networking systems increasingly falling to hackers. On the network security canvas, Intrusion detection system (IDS) is an essential tool used for timely detection of cyber-attacks. A designated set of reliable safety has been put in place to check any severe damage to the network and the user base. Machine learning (ML) is being frequently used to detect intrusion owing to their understanding of intrusion detection systems in minimizing security threats. However, several single classifiers have their limitation and pose challenges to the development of effective IDS. In this backdrop, an ensemble approach has been proposed in current work to tackle the issues of single classifiers and accordingly, a highly scalable and constructive majority voting-based ensemble model was proposed which can be employed in real-time for successfully scrutinizing the network traffic to proactively warn about the possibility of attacks. By taking into consideration the properties of existing machine learning algorithms, an effective model was developed and accordingly, an accuracy of 99%, 97.2%, 97.2%, and 93.2% were obtained for DoS, Probe, R2L, and U2R attacks and thus, the proposed model is effective for identifying intrusion

CICIDS-2017 dataset feature analysis with information gain for anomaly detection

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time