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

An Automated and Comprehensive Framework for IoT Botnet Detection and Analysis (IoT-BDA)

The proliferation of insecure Internet-connected devices gave rise to the IoT botnets which can grow very large rapidly and may perform high-impact cyber-attacks. The related studies for tackling IoT botnets are concerned with either capturing or analyzing IoT botnet samples, using honeypots and sandboxes, respectively. The lack of integration between the two implies that the samples captured by the honeypots must be manually submitted for analysis in sandboxes, introducing a delay during which a botnet may change its operation. Furthermore, the effectiveness of the proposed sandboxes is limited by the potential use of anti-analysis techniques and the inability to identify features for effective detection and identification of IoT botnets. In this paper, we propose and evaluate a novel framework, the IoT-BDA framework, for automated capturing, analysis, identification, and reporting of IoT botnets. The framework consists of honeypots integrated with a novel sandbox that supports a wider range of hardware and software configurations, and can identify indicators of compromise and attack, along with anti-analysis, persistence, and anti-forensics techniques. These features can make botnet detection and analysis, and infection remedy more effective. The framework reports the findings to a blacklist and abuse service to facilitate botnet suspension. The paper also describes the discovered anti-honeypot techniques and the measures applied to reduce the risk of honeypot detection. Over the period of seven months, the framework captured, analyzed, and reported 4077 unique IoT botnet samples. The analysis results show that some IoT botnets used anti-analysis, persistence, and anti-forensics techniques typically seen in traditional botnets

Network traffic analysis for threats detection in the Internet of Things

As the prevalence of the Internet of Things (IoT) continues to increase, cyber criminals are quick to exploit the security gaps that many devices are inherently designed with. Users cannot be expected to tackle this threat alone, and many current solutions available for network monitoring are simply not accessible or can be difficult to implement for the average user, which is a gap that needs to be addressed. This article presents an effective signature-based solution to monitor, analyze, and detect potentially malicious traffic for IoT ecosystems in the typical home network environment by utilizing passive network sniffing techniques and a cloud application to monitor anomalous activity. The proposed solution focuses on two attack and propagation vectors leveraged by the infamous Mirai botnet, namely DNS and Telnet. Experimental evaluation demonstrates the proposed solution can detect 98.35 percent of malicious DNS traffic and 99.33 percent of Telnet traffic for an overall detection accuracy of 98.84 percent

Cyber Threat Intelligence : Challenges and Opportunities

The ever increasing number of cyber attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost realtime. In practice, timely dealing with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions, this in essence defines cyber threat intelligence notion. However, such an intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyse, and interpret cyber attack evidences. In this introductory chapter we first discuss the notion of cyber threat intelligence and its main challenges and opportunities, and then briefly introduce the chapters of the book which either address the identified challenges or present opportunistic solutions to provide threat intelligence.Comment: 5 Page

Towards a Virtual Machine Introspection Based Multi-Service, Multi-Architecture, High-Interaction Honeypot for IOT Devices

Internet of Things (IoT) devices are quickly growing in adoption. The use case for IoT devices runs the gamut from household applications (such as toasters, lighting, and thermostats) to medical, battlefield, or Industrial Control System (ICS) applications that are used in life or death situations. A disturbing trend for IoT devices is that they are not developed with security in mind. This lack of security has led to the creation of massive botnets that are used for nefarious acts. To address these issues, it’s important to have a good understanding of the threat landscape that IoT devices face. A commonly used security control to monitor and gain insight into threats is a honeypot. This research explores the creation of a VMI-based high-interaction honeypot for IoT devices that is capable of monitoring multiple services simultaneously