A cryptographic accumulator is a compact data structure for representing a set of elements coming from some domain. It allows for a compact proof of membership and, in the case of a universal accumulator, non-membership of an element x in the data structure. A dynamic accumulator, furthermore, allows elements to be added to and deleted from the accumulator.

Group Key Exchange (GKE) is an important tool to develop secure multi-user applications such as group text messages, ad-hoc networks, and so on. Most of the currently deployed GKE schemes are synchronous, i.e., they require all the participants to be online during their execution. However, with more battery-powered devices being used in such applications, the synchronicity requirement is challenging to fulfill. To fill the gaps, asynchronous GKE schemes have been introduced in the literature. Nevertheless, the currently available asynchronous and synchronous GKE schemes rely on Trusted Third Parties (TTPs) for key establishment and management. To this end, reliance on TTPs is a serious shortcoming since TTPs are well known to be the single point of failure. Furthermore, the existing GKE schemes require participants to perform all computations, which can degrade the performance of resource-constrained devices such as Internet of Things (IoT) devices. To solve these problems, in this paper, we propose an asynchronous GKE scheme that uses blockchain and smart contracts to store the security keys-related material and reduce the computational load of the participants. Furthermore, our proposed scheme provides Perfect Forward Secrecy (PFS) and Post-Compromised Security (PCS). Our implementation on Ethereum shows that the proposed scheme can scale to more than 100 participants when combined with a distributed storage system.

MPEG-DASH is a video streaming standard that outlines protocols for sending audio and video content from a server to a client over HTTP. However, it creates an opportunity for an adversary to invade users’ privacy. While a user is watching a video, information is leaked in the form of meta-data, the size of data and the time the server sent the data to the user. After a fingerprint of this data is created, the adversary can use this to identify whether a target user is watching the corresponding video. Only one defense strategy has been proposed to deal with this problem: differential privacy that adds sufficient noise in order to muddle the attacks. However, that strategy still suffers from the trade-off between privacy and efficiency. This paper proposes a novel defense strategy against the attacks with rigorous privacy and performance goals creating a private, scalable solution. Our algorithm, “No Data are Alone” (NDA), is highly efficient. The experimental results show that our scheme is more than two times efficient in terms of excess downloaded video (represented as waste) compared to the most efficient differential privacy-based scheme. Additionally, no classifier can achieve an accuracy above 7.07% against videos obfuscated with our scheme.

Network anomalies are correlated to activities that deviate from regular behavior patterns in a network, and they are undetectable until their actions are defined as malicious. Current work in network anomaly detection includes network-based and host-based intrusion detection systems. However, most of them suffer from high false detection rates due to the base rate fallacy. To overcome such a drawback, this paper proposes a superior behavior-based anomaly detection system (SuperB) that defines legitimate network behaviors of authorized users in order to identify unauthorized accesses. We define the network behaviors of the authorized users by training the proposed deep learning model with time-series data extracted from network packets of each of the users. Then, the trained model is used to classify all other behaviors (we define these as anomalies) from the defined legitimate behaviors. As a result, SuperB effectively detects all anomalies of network behaviors. Our simulation results show that the proposed algorithm needs at least five end-to-end conversations to achieve over 95% accuracy and over 93% recall rate. Some simulations show 100% accuracy and recall rate. Our simulations use live network data combined with the CICIDS2017 data set. The performance has an average of less than 1.1% false-positive rate with some simulations showing 0%. The execution time to process each conversation is 85.20±0.60 milliseconds (ms), and thus it takes about only 426 ms to process five conversations to identify anomaly.

Group Key Agreement (GKA) mechanisms play a crucial role in realizing various applications in different networks, such as sensor networks and the Internet of Things (IoT). To be suitable for IoT, a GKA must satisfy several critical requirements. First, a GKA must be robust against a compromised device attack and satisfy essential secrecy definitions without the existence of a Trusted Third Party (TTP). TTP is often used by IoT devices to establish ad hoc networks securely, and usually, these devices are resource-constrained. Second, the GKA must be able to distribute session keys successfully, even with offline devices. Third, a GKA must reduce the burden of heavy cryptographic computations for IoT devices. Based on these observations, we propose a new GKA scheme that satisfies all the requirements above. The proposed scheme leverages smart contracts to alleviate the computational and storage overheads on IoT devices induced by cryptographic functions. It also brings the advantage of asynchronism such that offline devices will be able to compute the group key once they are online.