The exponential growth of internet usage poses a challenge in managing the bandwidth and securing the campus network environment. The ability differentiates and profile different type of data traversing in the Internet traffic is essential for ensuring effective bandwidth distribution and safeguarding network security. This work implements unsupervised data mining approach to analyze the network traffic trend and type of traffic in campus network. In this research, Orange tool is used in implementing K-Means Clustering Algorithm to find a network trend pattern of user accessing the Internet and to produce network traffic profiling in high volume traffic. Three clusters have been created based on the network traffic data described as high, medium and low number of hits towards the protocol services and unique IP address. Results shows that 74,869 hits come from DNS (UDP), 40,658 hits from MySQL and 3191 hits from HTTP. These are the high traffic that consume the bandwidth. Data mining process lead to reveal the information gather for profiling purposes and identify type of traffic passing through the campus network. The outcome of this study can be a recommendation of managing or shaping the bandwidth usage and strengthen the security policy of the network.
The popularity of Cloud Computing technology has made it a norm for IT deployment in enterprise, education and government sectors. But technologies in the cloud such as hypervisor or web-based dashboard have vulnerabilities which can potential cause data leakage. The impact of data leakage is huge, data leak incident at a firm such as Exactis cause 340 million of customer record being exposed. Moreover, the incident lead to financial loss, reputational damage, loss of customer trust and compliance issue to the firm. Therefore, there is a need to address the threat of data leakage in cloud computing platform. This paper presents the topic of data leakage detection in cloud computing platform. First we will discuss about the threat of data leakage during VM migration process and web dashboard authentication in cloud computing platform. To detect the data leakage, this paper proposes a method which involves performing packet capture on the platform. To demonstrate the method, this paper will simulate the threats and verify the data leakage. The results show that data leakage can be detected and verified effectively using the method when cloud management traffic is not encrypted.
The global crisis and climate change have resulted in severe food shortages worldwide. One of the solutions is self-farming by using smart farming technology. Smart and efficient agricultural production or smart farming using IoT sensors, big data, and cloud service has proven its value for a decade, but the effect depends on the agricultural environment of the country or society. Hence self-farming is likely the most feasible solution to avoid food scarcity. The smart farming system monitors and maintains essential growth parameters like light, temperature, and humidity to ensure maximum yield. In this paper, we propose a Smart Portable Farming Kit design, which is simple, lightweight, and durable to be placed indoors in an urban area. This prototype design uses the Internet of Things (IoT) based system for cultivating short-duration vegetables and mushrooms in an urban area with minimal user attention. The proposed design proved better than the traditional setup by increasing the mushroom yield. With Smart Portable Farming Kit, urban farming becomes a more viable alternative to increase food security, making oyster mushroom cultivation in the urban area easier and more profitable.
Nowadays most of cloud management software such as OpenStack and CloudStack provide an API to facilitate the communication and exchange of data between users, application, cloud components and infrastructure. Due to complexity of cloud management software implementation, the provided API has vulnerabilities which can be exploited by malicious party. Once exploited, it can cause security issue and disrupt the availability of services running on the cloud infrastructure. Hence; it is importance to address cloud API security by identifying potential threats, demonstrating how such threats could be exploited and how to detect such threat. This paper presents the topic of API Vulnerabilities in Cloud Computing Platform: Attack and Detection. We will discuss the vulnerabilities of the API in cloud management software. Based on these vulnerabilities, this paper will demonstrate how eavesdropping on cloud API authentication services and API exhaustion attack can be initiated. To address the threat due to the vulnerabilities of the API, we need to detect attack which exploits the vulnerabilities. Thus this paper also proposes and demonstrates methods to detect such attack effectively. Method to detect ongoing API exhaustion attack will be based on AD3 algorithm. From the experiments result, it shows that attacks on the cloud platform AP can be detected effectively.
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