Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

Internet of Things (IoT) is making strong advances in healthcare with the promise of transformation in technological, social and economic prospects, paving the way for a healthy future. Medical devices equipped with wireless communication enable remote monitoring features and are increasingly becoming connected to each other and to the Internet. Such smart and connected medical devices referred to as the Internet of Medical Things have enabled continuous real-time patient monitoring, increase in diagnostic accuracy, and effective treatment. In spite of their numerous benefits, these devices open up newer attack surfaces thereby introducing multitude of security and privacy concerns. Attacks on Internet connected medical devices can potentially cause significant physical harm and life-threatening damage to the patients. In this research, we design and develop a novel mobile agent based intrusion detection system to secure the network of connected medical devices. In particular, the proposed system is hierarchical, autonomous, and employs machine learning and regression algorithms to detect network level intrusions as well as anomalies in sensor data. We simulate a hospital network topology and perform detailed experiments for various subsets of Internet of Medical things including wireless body area networks and other connected medical devices. Our simulation results demonstrate that we are able to achieve high detection accuracy with minimal resource overhead.

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR), and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA) membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine.

Building an efficient Intrusion Detection System (IDS) is a challenging task in wireless ad hoc networks due to the resource constraints and lack of a centralized control. In this work, we present a decentralized monitor-based IDS for detecting jamming type Denial of Service (DoS) attacks at the lower layers of the protocol stack. The varying channel and network dynamics in ad hoc networks can impair service similar to a jamming scenario, resulting in false positives on intrusion detections. To this end, we incorporate a cross-layer design in our IDS to differentiate the malicious jamming behavior from genuine network failures. We validate our design through simulation, and establish the effectiveness of the model. From the simulation results, we observe a significant improvement in the accuracy of detection and lower false positives.

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