School of Information Science and Technology

Permanent URI for this communityhttps://cris.hit.ac.zw/handle/123456789/2

Browse

Filters

2012 - 201829

Settings

End date: 2019Has files: Yes

Search Results

Now showing 1 - 10 of 29
  • Thumbnail Image
    Item
    Clustering West Nile Virus Spatio-temporal data using ST-DBSCAN
    (Procedia Computer Science, 2018-06-13) Chimwayi, K.B; Anuradha, J
    Spatio-temporal data mining has been the talk of the day due to high availability of spatio-temporal data from varied sources in diverse fields. Through many tracking devices, huge amounts of spatio-temporal data are being generated. In epidemiology, diseases, patterns and trends attached can be explored taking advantage of methods such as spatio-temporal clustering to discover new knowledge. In this paper Spatio-Temporal Density Based Spatial Clustering of Applications with Noise (ST-DBSCAN) is implemented and analysed on a public health dataset. Upon the implementation, results are analysed, loopholes spotted and a fuzzy version of ST-DBSCAN is proposed. The method is successfully applied to find spatio-temporal clusters in Chicago West Nile Virus (WNV) surveillance data for the period 2007 to 2017.The drawbacks in the original ST-DBSCAN are identified and solutions are proposed. ST-DBSCAN is an extension of the original Density Based Spatial Clustering of Applications with Noise (DBSCAN).
  • Thumbnail Image
    Item
    Scalable malware identification and classification using deep neural network
    (2017-01-01) Nyamugudza, Tendai; Raja, Kumaravelu
    Malware presents a challenge to organizations as they threaten smooth functioning of both physical and virtual system. Timely identification of malware is critical as it allows organizations to eliminate the threat before damage has been done. This paper proposes a scalable deep learning framework for classifying portable executable files as benign or malicious using file header information. The proposed method relies on the representational power of deep neural networks which allows them to learn complex characteristics found in the file header information. A deep neural network is trained using header information extracted from sample of benign and malicious files binaries. An accuracy of 0.98 and false positive rate of 0.019 were obtained.
  • Thumbnail Image
    Item
    Network traffic intelligence using a low interaction honeypot
    (Proceedings of International Conference On Science, Engineering and Technology, 2017-05-02) Nyamugudza, Tendai; Rajaseka, Venktesh; Sen, Prasad; Viswanathan, Madhu
    Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a lowinteraction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot- honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the networ
  • Thumbnail Image
    Item
    Risk Level Prediction of Chronic Kidney Disease Using Neuro- Fuzzy and Hierarchical Clustering Algorithm (s)
    (International Journal of Multimedia and Ubiquitous Engineering, 2017-01-02) Chimwayi, Kerina Blessmore; Haris, Noorie; Caytiles, Ronnie D; Iyengar, Ch. S. N
    Chronic Kidney Disease (CKD) is usually characterized by a gradual loss of the functioning which the kidney does over time due to various factors. Early prediction and treatment save the kidney and halts the progress of CKD. CKD disease is being viewed as global public health issue for the past decade. The greatest threat for this deadly disease is developing countries where getting therapy is very expensive. The importance of predicting individuals who are at risk of CKD as well as applying clustering techniques cannot be underestimated since these can modify the progression of the disease. Identifying the silent killer disease early offers best opportunities for implementing possible strategies for lessening the probability of kidney loss. Neuro-fuzzy algorithm is applied to determine the risk of CKD in patients. Predictions done using neuro-fuzzy gave an accuracy of 97 percent. Using selected features, prediction for CKD disease is done so as to identify the risk. The results of the prediction are clustered to identify the percentage of patients with a high risk of having kidney disease who have a higher probability of being diabetic. Using hierarchical clustering three clusters formed show that there is a strong relationship between chronic kidney and diabetes
  • Thumbnail Image
    Item
    A Study on Cloud Robotics: Ad-hoc cloud (Cloud Seeding)
    (International Journal of Innovative Research in Computer and Communication Engineering, 2015-04-01) Chifamba, Shepard
    Cloud seeding in cloud robotics is the concept of forming an adhoc cloud using the available robot resources. A team of robots working in the same field utilizing cloud robotics might experience a connection failure to the main node however this should not stop field work. The teamed robots surrender their resources to form a virtual adhoc cloud not only to load balance tasks but to share resources and information. In this paper the researcher explores further on how cloud seeding can best be done, the security implications as well as networking concerns involved. This however is not a permanent infrastructure but a way of circumventing the challenge of network failure between the main cloud infrastructure and the field robots in cloud robotics.
  • Thumbnail Image
    Item
    Virtual Firewall Security on Virtual Machines in Cloud Environment
    (International Journal of Scientific & Engineering Research, 2015-02-01) Jekese, G; Subburaj, R; Hwata, C
    Virtualization is revolutionizing how information technology resources and services are used and managed and has led to an explosive growth in the cloud computing industry, illustrated by Google’s Cloud Platform and Amazon’s Elastic Cloud. It brings unique security problems such as virtual traffic, denial of service and intrusion, resulting in penetration of virtual machines, which is disastrous for the enterprise, the user and the cloud provider. Virtual traffic between virtual machines may never leave the physical host hardware; making traditional physical firewalls hopeless to monitor and secure it. This paper proposes a virtual firewall which allows managing the network security of the virtual infrastructure per-virtual machine basis, defining network traffic rules, and hardening the security of the virtual environment. A private cloud is designed using open source solutions and to manage the firewall rules, we implement a Tree-Rule firewall technique which filters packets in a tree-like way based on their attributes such as IP address and protocols. The speed of filtering and processing packets on virtual firewall is highly improved to avoid overload of the firewall in the particular case. It permits to log and analyze network traffic logs for each of the monitored virtual machines. The virtual firewall will provide the power to control the bandwidth utilization of each virtual machine in the infrastructure, preventing overutilization and denial of service to critical applications.
  • Thumbnail Image
    Item
    Securing patient data in the cloud using Attribute Based Encryption
    (International Journal of Scientific & Engineering Research, 2015-03-01) Subburaj, R; Hwata, C; Jekese, G
    Cloud computing has attracted attention worldwide in all industries, including the medical field leading to the rise of electronic healthcare systems. Although it has brought about an improvement in the provision of healthcare in terms of information management, it also poses a lot of security and privacy concerns to the patients. This is due to the fact that personal and highly sensitive data is outsourced to a third party (Cloud Service Provider) for processing and storage. This paper seeks to improve security of cloud-based patient data in healthcare organizations by employing a Ciphertext Policy Attribute Based Encryption (CP-ABE) scheme. The proposed scheme provides data confidentiality and allows the patient to control who accesses her personal health data by encrypting it under a specified access policy alongside with her key. It also provides collusion-resistance, flexible and immediate revocation of users who are no longer allowed to access a patient’s data.
  • Thumbnail Image
    Item
    Impact of Object Oriented Design Patterns on Software Development
    (International Journal of Scientific & Engineering Research, 2015-02-02) Subburaj, R.; Jekese, G; Hwata, C
    —Software design patterns are a bonanza for building large Object Oriented (OO) software systems. They provide well-tested and proven solutions to recurring problems that developers address. There are several benefits of using patterns. They can speed up the software development process. Design patterns consolidate learning with an aim to make it easier for designers to use well-known and successful designs developed from expert experience. At the same time software design patterns are too abstract and remain an art that has to be mastered over time with experience. This paper seeks to evaluate the advantages and disadvantages of design patterns.
  • Thumbnail Image
    Item
    Design of Application to Detect Images Embedded with Malicious Programs
    (International Journal of Science and Research (IJSR), 2013-06-14) Shoniwa, Robert T. R; George, Geogen
    In today’s world, malware can be propagated to victim systems in an increasingly diverse number of ways. One of these methods involves the passive distribution of malware by embedding in JPEG images which goes on to highlight that even simple images can be manipulated maliciously by criminals. The aim of this paper is to design an application that partially acts as a steganalysis tool to scan, detect and notify the user of the presence of a payload in either one or a set of selected images.it will then proceed to analyze the payload and verify whether it is a malicious program or not. It will also give a brief summarized file analysis of the detected payload. Ultimately, this will help highlight the need to consider images as a potential attack vector and then also offer a corresponding solution to this problem
  • Thumbnail Image
    Item
    Cloud Based SecuritySolution For Android Smartphones.
    (International Conference on Circuit, Power and Computing Technologies [ICCPCT], 2015-01-13) Marengereke, Munyaradzi. T
    In this paper, we define SIEM and we discuss Android security monitoring as well as recent research in Android security systems. Then, we propose a cloud based security system for collection, visualization, analysis and correlation of application logs, statistics and determining abnormal application and network behavior on the device. If abnormal behavior is detected an appropriate alert is sent back to the device for remedial action. In the case of abnormal network traffic, then firewall rules to be updated on an implementation of an IPTABLES/ NETFILTER firewall to block unwarranted network traffic. Furthermore a web interface is created to enable visualization of logs and all data collected from the device. So it serves as an intrusion mitigation solution coupled with security information audit web portal. This paper highlights the architecture of the proposed system.