A look into the information your smartphone leaks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Some smartphone applications (apps) pose a risk to users’ personal information. Events of apps leaking information stored in smartphones illustrate the danger that they present. In this paper, we investigate the amount of personal information leaked during the installation and use of apps when accessing the Internet. We have opted for the implementation of a Man-in-the-Middle proxy to intercept the network traffic generated by 20 popular free apps installed on different smartphones of distinctive vendors. This work describes the technical considerations and requirements for the deployment of the monitoring WiFi network employed during the conducted experiments. The presented results show that numerous mobile and personal unique identifiers, along with personal information are leaked by several of the evaluated apps, commonly during the installation process

Successful Deployment of a Wireless Sensor Network for Precision Agriculture in Malawi

This paper demonstrates how an irrigation management system (IMS) can practically be implemented by deploying a wireless sensor network (WSN). Specifically, the paper describes an IMS which was set up in Manja township, city of Blantyre. Deployment of IMS in rural areas of developing countries like Malawi is a challenge as grid power is scarce. For the system to be self-sustained in terms of power, the study used solar photovoltaic and rechargeable batteries to power all electrical devices. The system incorporated a remote monitoring mechanism through a General Packet Radio Service modem to report soil temperature, soil moisture, WSN link performance, and photovoltaic power levels. Irrigation valves were activated to water the field. Preliminary results in this study have revealed a number of engineering weaknesses of deploying such a system. Nevertheless, the paper has highlighted areas of improvement to develop a robust, fully automated, solar-powered, and low-cost IMS to suit the socioeconomic conditions of small scale farmers in developing countries

Contemporary sequential network attacks prediction using hidden Markov model

Intrusion prediction is a key task for forecasting network intrusions. Intrusion detection systems have been primarily deployed as a first line of defence in a network, however; they often suffer from practical testing and evaluation due to unavailability of rich datasets. This paper evaluates the detection accuracy of determining all states (AS), the current state (CS), and the prediction of next state (NS) of an observation sequence, using the two conventional Hidden Markov Model (HMM) training algorithms, namely, Baum Welch (BW) and Viterbi Training (VT). Both BW and VT were initialised using uniform, random and count-based parameters and the experiment evaluation was conducted on the CSE-CICIDS2018 dataset. Results show that the BW and VT countbased initialisation techniques perform better than uniform and random initialisation when detecting AS and CS. In contrast, for NS prediction, uniform and random initialisation techniques perform better than BW and VT count-based approaches

Successful Deployment of a Wireless Sensor Network for Precision Agriculture in Malawi

This paper demonstrates how an Irrigation Management System (IMS) can practically be implemented by successfully deploying a Wireless Sensor Network (WSN). Specifically, the paper describes an IMS which was set up in Manja Township, City of Blantyre based on an advanced irrigation scheduling technique. Since the system had to be self-sustained in terms of power, which is a challenge for deployment in rural areas of developing countries like Malawi where grid power supply is scarce, we used solar Photovoltaic (PV) and rechargeable batteries to power all electrical devices in this system. The system incorporated a remote monitoring mechanism through a General Packet Radio Service (GPRS) modem to report soil temperature, soil moisture, WSN link performance and PV power levels. Irrigation valves were activated to water the field. Our preliminary results have revealed engineering weakness of deploying such a system. Nevertheless, the paper shows that it is possible to develop a robust, fully-automated, solar powered, and low cost IMS to suit the socio-economic conditions of small scale farmers in developing countries

Hidden Markov models for detecting and predicting sequential network attacks

Hidden Markov models for detecting and predicting sequential network attack

Small Scale Rollout of PV Systems in Chikwawa District, Malawi: Remote Monitoring System Effectiveness

Off-grid solar photovoltaic systems in Malawi are deployed increasingly as the primary option for rural public infrastructure such as primary schools and health centres. Overall, grid-connected electricity access has remained stagnant at around 9% with only 1% of rural population connected. To improve the technical sustainability of such systems, a novel remote monitoring technology utilising Wireless Sensor Networks was installed and the systems were monitored over roughly one year. This paper has described the technical design, performance, and benefits received from deployment of the technology. Furthermore, it has evaluated the cost implications for a larger scale rollout and potential benefits

Remote Monitoring System Effectiveness : Small Scale Rollout of PV Systems in Chikhwawa District, Malawi

The Remote Monitoring (RM) strand of MREAP was incorporated within a Strategic Energy Project in Chikhwawa led by WASHTED. RM was deployed as a measure to bolster technical sustainability of the project and to consider its readiness for scale-up. Distributed energy systems in Malawi, particularly solar PV systems, have historically had poor sustainability performance. During MREAP it was included at 4 separate locations comprised of 21 separate electrical systems to capture key technical performance data. These systems were each standalone Solar PV installations on health facilities and primary schools. The technology utilized Wireless Sensor Networks (WSN) and open-source models to communicate via mobile phone networks remotely to WASHTED’s office in Blantyre. Data could then be viewed at near real-time, analyzed, and corrective actions could be enacted. The purpose of the RM strand was to demonstrate the technology’s effectiveness towards improving sustainability of off-grid community energy projects, particularly the technical performance

Design of outdoor wireless networks using computer simulation

In this paper, we design a pilot outdoor wireless network based on IEEE 802.11b standard for HealthCare Applications through computer simulation. The purpose is to demonstrate the use of computer simulation in designing outdoor wireless networks. We use a free software tool called Radio Mobile and freely available geographical elevation data downloaded from NASA to determine the radio frequency (RF) coverage of a wireless networking base-station and radio link performance. Furthermore, we demonstrate the flexibility of computer simulation in assessing design alternatives

Predicting Sequential Network Attacks Using Hidden Markov Model - MATLAB Code

This code reproduces the results for the research paper titled Contemporary Sequential Network Attacks Prediction Using Hidden Markov Model by Timothy Chadza, Konstantinos Kyriakopoulos & Sangarapillai Lambotharan presented at the 17th International Conference on Privacy, Security and Trust (PST), IEEE, Fredericton, NB, Canada, 2019. This is published under GNU GENERAL PUBLIC LICENSE Version 3. If you use this code, please, cite the above paper.This work is on the evaluation of detection accuracy for determining all states, the current state, and the prediction of next state of an observation sequence, using the two conventional hidden Markov model training algorithms, namely, Baum Welch and Viterbi training. The training algorithms are initialised using uniform, random and count-based parameters. The experimental evaluation is conducted on the CSE-CIC-IDS2018, a modern dataset comprising seven different attack scenarios over a large network environment. The different attacks are sequentially aggregated to constitute an attack sequence. Viterbi decoding has been used to estimate the next state upon computation of the next attack manifestation.The code is run by executing the main.m file in MATLAB.</p

Transfer Learning with Hidden Markov Models Applied on Network Security - MATLAB Code

This code is published under GNU GENERAL PUBLIC LICENSE Version 3. If you use this code, please, cite our 2020 IEEE Access paper “Learning to Learn Sequential Network Attacks using Hidden Markov Models".This code reproduces the work and results as described in the IEEE Access article "Learning to Learn Sequential Network Attacks Using Hidden Markov Model" by Timothy Chadza, Konstantinos Kyriakopoulos & Sangarapillai Lambotharan.This code considers a transfer learning (TL) approach that exploits already learned knowledge, gained from a labelled source dataset, and adapts it on a different, unlabelled target dataset. Five unsupervised hidden Markov model techniques are developed utilising a TL approach and evaluated against conventional machine learning approaches. Baum-Welch, Viterbi training, gradient descent, differential evolution and simulated annealing, are deployed for the detection of attack stages in the network traffic, as well as, forecasting both the next most probable attack stage and its method of manifestation. The experiments are conducted on DARPA 2000 processed Snort alerts. A comparative performance evaluation between conventional machine learning and TL has been made using the following metrics: prediction and detection accuracy, Bayesian inference criterion, mean square error and adjusted random index.To run this code, simply set your path to the root Code folder and run the main.m file.</div