BlackWatch:increasing attack awareness within web applications

Web applications are relied upon by many for the services they provide. It is essential that applications implement appropriate security measures to prevent security incidents. Currently, web applications focus resources towards the preventative side of security. Whilst prevention is an essential part of the security process, developers must also implement a level of attack awareness into their web applications. Being able to detect when an attack is occurring provides applications with the ability to execute responses against malicious users in an attempt to slow down or deter their attacks. This research seeks to improve web application security by identifying malicious behaviour from within the context of web applications using our tool BlackWatch. The tool is a Python-based application which analyses suspicious events occurring within client web applications, with the objective of identifying malicious patterns of behaviour. This approach avoids issues typically encountered with traditional web application firewalls. Based on the results from a preliminary study, BlackWatch was effective at detecting attacks from both authenticated, and unauthenticated users. Furthermore, user tests with developers indicated BlackWatch was user friendly, and was easy to integrate into existing applications. Future work seeks to develop the BlackWatch solution further for public release

Management and Security of IoT systems using Microservices

Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and fault tolerance. We aim to solve the above issues in a microservice architecture. We build a data pipeline for IoT devices to send data through a messaging platform Kafka and monitor the devices using the collected data by making real time dashboards and a machine learning model to give better insights of the data. For proof of concept, we test the proposed solution on a heterogeneous cluster, including Raspberry Pi’s and IoT devices from different vendors. We validate our design by presenting some simple experimental results

Ransomware in High-Risk Environments

In today’s modern world, cybercrime is skyrocketing globally, which impacts a variety of organizations and endpoint users. Hackers are using a multitude of approaches and tools, including ransomware threats, to take over targeted systems. These acts of cybercrime lead to huge damages in areas of business, healthcare systems, industry sectors, and other fields. Ransomware is considered as a high risk threat, which is designed to hijack the data. This paper is demonstrating the ransomware types, and how they are evolved from the malware and trojan codes, which is used to attack previous incidents, and explains the most common encryption algorithms such as AES, and RSA, ransomware uses them during infection process in order to produce complex threats. The practical approach for data encryption uses python programming language to show the efficiency of those algorithms in real attacks by executing this section on Ubuntu virtual machine. Furthermore, this paper analyzes programming languages, which is used to build ransomware. An example of ransomware code is being demonstrated in this paper, which is written specifically in C sharp language, and it has been tested out on windows operating system using MS visual studio. So, it is very important to recognize the system vulnerability, which can be very useful to prevent the ransomware. In contrast, this threat might sneak into the system easily, allowing for a ransom to be demanded. Therefore, understanding ransomware anatomy can help us to find a better solution in different situations. Consequently, this paper shows a number of outstanding removal techniques to get rid from ransomware attacks in the system

Ransomware in High-Risk Environments

In today’s modern world, cybercrime is skyrocketing globally, which impacts a variety of organizations and endpoint users. Hackers are using a multitude of approaches and tools, including ransomware threats, to take over targeted systems. These acts of cybercrime lead to huge damages in areas of business, healthcare systems, industry sectors, and other fields. Ransomware is considered as a high risk threat, which is designed to hijack the data. This paper is demonstrating the ransomware types, and how they are evolved from the malware and trojan codes, which is used to attack previous incidents, and explains the most common encryption algorithms such as AES, and RSA, ransomware uses them during infection process in order to produce complex threats. The practical approach for data encryption uses python programming language to show the efficiency of those algorithms in real attacks by executing this section on Ubuntu virtual machine. Furthermore, this paper analyzes programming languages, which is used to build ransomware. An example of ransomware code is being demonstrated in this paper, which is written specifically in C sharp language, and it has been tested out on windows operating system using MS visual studio. So, it is very important to recognize the system vulnerability, which can be very useful to prevent the ransomware. In contrast, this threat might sneak into the system easily, allowing for a ransom to be demanded. Therefore, understanding ransomware anatomy can help us to find a better solution in different situations. Consequently, this paper shows a number of outstanding removal techniques to get rid from ransomware attacks in the system