Since its introduction in 1987, the DNS has become one of the core components of the Internet. While it was designed to work with both TCP and UDP, DNS-over-UDP (DoUDP) has become the default option due to its low overhead. As new Resource Records were introduced, the sizes of DNS responses increased considerably. This expansion of the message body has led to truncation and IP fragmentation more often in recent years where large UDP responses make DNS an easy vector for amplifying denial-of-service attacks which can reduce the resiliency of DNS services. This paper investigates the resiliency, responsiveness, and usage of DoTCP and DoUDP over IPv4 and IPv6 for 10 widely used public DNS resolvers. The paper specifically measures the resiliency of the DNS infrastructure in the age of increasing DNS response sizes that lead to truncation and fragmentation. Our results offer key insights into the management of robust and reliable DNS network services. While DNS Flag Day 2020 recommends 1232 bytes of buffer sizes, we find out that 3/10 resolvers mainly announce very large EDNS(0) buffer sizes both from the edge as well as from the core, which potentially causes fragmentation. In reaction to large response sizes from authoritative name servers, we find that resolvers do not fall back to the usage of DoTCP in many cases, bearing the risk of fragmented responses. As the message sizes in the DNS are expected to grow further, this problem will become more urgent in the future. This paper demonstrates the key results (particularly as a consequence of the DNS Flag Day 2020) which may support network service providers make informed choices to better manage their critical DNS services.

Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by >30% over fixed-line and by >50% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet.

Industrial Internet of Things (IIoT) opens up a challenging research area towards improving secure data sharing which currently has several limitations. Primarily, the lack of inbuilt guarantees of honest behavior of participating, such as end-users or cloud behaving maliciously may result in disputes. Given such challenges, we propose a fair, accountable, and secure data sharing scheme, FairShare for IIoT. In this scheme, data collected from IoT devices are processed and stored in cloud servers with intermediate fog nodes facilitating computation. Authorized clients can access this data against some fee to make strategic decisions for improving the operational services of the IIoT system. By enabling blockchain, FairShare prevents fraudulent activities and thereby achieves fairness such that each party gets their rightful outcome in terms of data or penalty/rewards while simultaneously ensuring accountability of the services provided by the parties. Additionally, smart contracts are designed to act as a mediator during any dispute by enforcing payment settlement. Further, security and privacy of data are ensured by suitably applying cryptographic techniques like proxy re-encryption. We prove FairShare to be secure as long as at least one of the parties is honest. We validate FairShare with a theoretical overhead analysis. We also build a prototype in Ethereum to estimate performance and justify comparable results with a state-of-the-art scheme both via simulation and a realistic testbed setup. We observe an additional communication overhead of 256 bytes and a cost of deployment of 1.01 USD in Ethereum which are constant irrespective of file size.

Recently collaborative learning is widely applied to model sensitive data generated in Industrial loT (1IoT). It enables a large number of devices to collectively train a global model by collaborating with a server while keeping the datasets on their respective premises. However, existing approaches are limited by high overheads and may also suffer from falsified aggregated results returned by a malicious server. Hence, we propose a Scal-able, Privacy-preserving and veRIfiable collaboraTive lEarning (SPRITE) algorithm to train linear and logistic regression models for IloT. We aim to reduce burden from resource-constrained IloT devices and trust dependence on cloud by introducing fog as a middleware. SPRITE employs threshold secret sharing to guarantee privacy-preservation and robustness to IloT device dropout whereas verifiable additive homomorphic secret sharing to ensure verifiability during model aggregation. We prove the security of SPRITE in an honest-but-curious setting where the cloud is untrustworthy. We validate SPRITE to be scalable and lightweight through theoretical overhead analysis and extensive testbed experimentation on an IloT use-case with two real-world industrial datasets. For a large-scale industrial setup, SPRITE records 65% and 55% improved performance over its competitor for linear and logistic regressions respectively while reducing communication overhead for an IloT device by 90%.

The advent of Industrial Internet of Things (IIoT) along with cloud computing has brought a huge paradigm shift in manufacturing industries resulting in yet another industrial revolution, Industry 4.0. Huge amounts of delay-sensitive data of diverse nature are being generated, which need to be locally processed and secured because of their sensitivity. However, the low-end Internet of Things devices are unable to handle huge computational overheads. In addition, the semi-trusted nature of cloud introduces several security concerns. To address these issues, this article proposes a secure fog-based IIoT architecture by suitably plugging a number of security features into it and by offloading some of the tasks judiciously to fog nodes. These features secure the system alongside reducing the trust and burden on the cloud and resource-constrained devices, respectively. We validate our proposed architecture through both theoretical overhead analysis and practical experimentation, including simulation study and testbed implementation.