Research Papers - Dept of Computer Systems Engineering

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Author: search.filters.author.Perera, AHas files: Yes

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    The Next Gen Security Operation Center
    (IEEE, 2021-04-02) Perera, A; Rathnayaka, S; Che, C; Madushanka, W. W; Senarathne, A. N
    Due to the evolving Cyber threat landscape, Cyber criminals have found new and ingenious ways of breaching defenses in networks. Due to the sheer destruction these threat actors can cause to an organization, most modern-day organizations have focused their attention towards protecting their critical infrastructure and sensitive information through multiple methods. The main defense against both internal and external threats to an organization has been the implementation of the Security Operations Center (SOC) which is responsible for monitoring, analyzing and mitigating incoming threats. At the heart of the Security Operations Center, lies the Security Information and Event Management system (SIEM) which is utilized by SOC analysts as the centralized point where all security notifications from various security technologies including firewalls, IPS/IDS and Anti-Virus logs are collected and visualized. The effective operation of SOC in an organization is dependent on how well the SIEM filters log events and generates actual alerts. Here lies the major problem faced by SOC analysts in detecting threats. If proper alert correlation is not accomplished, analysts would have to deal with too much alert noise due to a high false positive count. This would ultimately cause analysts to miss critical security incidents, thus causing severe implications to the organization's security. The performance of a SIEM can be enhanced through adding various functionalities such as Threat Hunting, Threat Intelligence and malware identification and prevention in order to reduce false positive alarms, threat framework and machine learning which would increase the accuracy and efficiency of the overall Security Operations process of an organization. Even though many products which provide these additional functionalities exist in the current market, they can be too expensive for smaller scale organizations to handle. Our aim is to make security operations deliverable to any organization regardless of the size and scale without any financial implications and enhance its functionalities with the aid of Advanced Machine Learning Techniques.
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    Ai based greenhouse farming support system with robotic monitoring
    (IEEE, 2020-11-16) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. W
    Greenhouses plays a major role in today's agriculture since farmers can grow plants under controlled climatic conditions and can optimize production. The greenhouses are usually built in areas where the climatic conditions for the growth of plants are not optimal so requires some artificial setups to bring about productivity. Automating process of a greenhouse requires monitoring and controlling of the climatic parameters. This paper is an attempt to minimize the cost of maintaining greenhouse environments using new technologies. The end goal of this research an automated system to optimally monitor and control the environmental factors inside greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect unhealthy plants using image processing and machine learning. The mobile robot navigates through a predefined map of greenhouse. Database server has created to store gathered real-time data. And the necessary accurate data represent by using proper application for analyzing.
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    Intelligent disease detection system for greenhouse with a robotic monitoring system
    (IEEE, 2020-12-10) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. W
    Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attention-grabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning.