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Implantable medical devices, such as pacemakers, cardiac defibrillators, and insulin pumps, play a crucial role in monitoring patients’ vital signs within healthcare systems. However, these networked devices are susceptible to external attacks and breaches of trust, hindering the potential innovation and social benefits of eHealth services. To address these concerns, we propose a novel ECG-based key generation scheme and blockchain-based authentication protocol to build a trustworthy healthcare service under any situation. The key will be extracted in a single heartbeat using fiducial features. Compared with the existing works, the proposed key generation achieves the most efficient and secure method by introducing newly designed techniques to identify the unique features based on the time differences within a small window of the ECG signals. In our key generation process, we utilized three distinct fiducial features: amplitude peak differences, time differences between peaks, and slope between each pair of peaks. After obtaining the distinct fiducial features, each set of features denoted as F undergoes an encoding process, resulting in 16-bit vectors. To ensure randomness, the most significant bits of the encoded vectors are discarded due to their low entropy and least significant bits, which offer a greater degree of variability has been used. To validate our key generation method, we conducted the NIST statistical suite test. Our key generation process successfully passed all the necessary criteria and requirements set by the NIST suite test for ensuring the security and reliability of cryptographic systems. The proposed authentication protocol for the interaction between a patient and a doctor consists of three parts, addressing different scenarios that may arise including a patient visits a new doctor and emergencies which may be necessary for emergency medical services (EMS) personnel to immediately access the IMD. Experimental results demonstrate the efficiency and effectiveness of our key generation, as it produces a key of the same length within a second while maintaining a high level of randomness. Furthermore, the communication overhead for providing authentication services on the Internet is minimal. To evaluate the vulnerability of an authentication protocol, we performed a thorough security analysis, with a specific focus on the adversary model within the IMD (Implantable Medical Device) and DP (Device Programmer) interaction. Additionally, we implemented the proposed methods on a hardware setup by considering several factors, including time, key bit size, and memory usage. Furthermore, the proposed biometric key generation is tested using the NIST standard suite, where it successfully satisfied all the major requirements 

The number of engineering doctoral degrees awarded to foreign-born (international) students has increased in the past three decades. It has resulted in an increase in the foreign-born professoriate across the USA. To address gender parity and address the needs of a diverse student population, many universities are increasingly recruiting foreign-born women faculty. While their immigration status is their transitional identity, they do have a distinctive racial/ethnic identity. Unfortunately, issues of foreign-born faculty, especially women, are understudied. A pilot study was carried out at the California State University System to assess the needs of women faculty. A survey was administered to the engineering faculty (all identity groups) across California State University System. This paper examines the tenure navigation of foreign-born women faculty to identify their needs to attain tenure and promotion successfully. Resource satisfaction between the populations based on gender and foreign-born status are compared. Based on the evidence presented, the authors argue that foreign-born status should be considered as one of the parameters in planning retention programs and addressing intersectionality for engineering academics. Some ongoing efforts at the California State University System are also reported. 

Network intrusion detection systems (IDS) has efficiently identified the profiles of normal network activities, extracted intrusion patterns, and constructed generalized models to evaluate (un)known attacks using a wide range of machine learning approaches. In spite of the effectiveness of machine learning-based IDS, it has been still challenging to reduce high false alarms due to data misclassification. In this paper, by using multiple decision mechanisms, we propose a new classification method to identify misclassified data and then to classify them into three different classes, called a malicious, benign, and ambiguous dataset. In other words, the ambiguous dataset contains a majority of the misclassified dataset and is thus the most informative for improving the model and anomaly detection because of the lack of confidence for the data classification in the model. We evaluate our approach with the recent real-world network traffic data, Kyoto2006+ datasets, and show that the ambiguous dataset contains 77.2% of the previously misclassified data. Re-evaluating the ambiguous dataset effectively reduces the false prediction rate with minimal overhead and improves accuracy by 15%. 

Software-Defined Networking (SDN) represents a major transition from traditional hardware-based networks to programmable software-based networks. While SDN brings visibility, elasticity, flexibility, and scalability, it also presents security challenges. This paper describes some of the hands-on labs we developed for teaching SDN security using the CloudLab platform. The hands-on labs have been used in a graduate level course on SDN/NFV related technologies. Our teaching experience of the hands-on labs is discussed. The hands-on labs can be adopted by other instructors to teach SDN security. 

Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain’s security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform.