An Implementation of K-NN Classification Algorithm for Detecting Impersonators in Online Examination Environment

The online examination platforms also known as computer-based testing (CBT) platforms for conducting mass-driven examinations over computer networks to eliminate certain issues such as delay in marking, misplacement of scripts, monitoring, etc., associated with the conventional Pen and Paper Type (PPT) of examination have also been bedeviled with the issue of impersonation commonly associated with the PPT system. The existing online examination platforms rely on passive mechanisms such as the CCTV system and the human invigilators for monitoring the examination halls against cheating and impersonation. The proposed model integrates some level of intelligence into existing online examination prototype by designing and developing an intelligent agent service that could assess students against impersonation threat in an online examination environment using the K-Nearest Neighbor (K-NN) machine learning classification technique considering the level of accuracy and response time in answering the questions. A total of 3,083 dataset was downloaded from an online repository; 80% (2,466) of the dataset was used for training the model, while 20% (617) dataset was used in testing the model to enable the model detect unseen data correctly. Results showed that the developed model has a 99.99% accuracy rate, precision, recall and f-score.

An Integrated Model for Monitoring Nodes in Computer Networks

Monitoring complex computer network environment is now a very challenging task for network administrators despite the various existing monitoring applications for networks that are faced with the issues of centralized monitoring, which causes network traffic, reduces network bandwidth, and are unable to concurrently run two or more network services. This research paper was designed to tackle the problems exhibited by the existing network monitoring application by integrating different network monitoring services in a single model using the power of agent’s distributed processing and monitoring services. Data about the existing and proposed model was gathered using key informant interview approach, and observation of the existing software. Iterative software model was adopted as the software development life cycle based on its strengths and suitability. The proposed model was developed using use-case and sequence diagrams. Suitable programming languages and development environment such as Java, JavaScript, Hypertext Preprocessor, Hypertext markup language and MySQL were used in coding the software prototype. The functionality of the proposed system was tested and results showed that the proposed system has 100% anomaly network intrusion detection rate and better functional features as compared to the existing network monitoring applications observed.

A Bit of Hacking Techniques for Vulnerable Subdomain

Hacking as it is popularly called is an official phrase for unauthorised access to a network/data. However, there are different methods used in gaining access to a designated network; hence, this paper explained a few hacker’s intent using classified commercially available tools and techniques to expose the vulnerability of data and applications within networks. It also explained few simple ways of gaining access to a network without really having the key technical ability.