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Abstract SYN flood attacks (half‐open attacks) have been proven a serious threat to software‐defined networking (SDN)‐enabled infrastructures. A variety of intrusion detection and prevention systems (IDPS) have been introduced for identifying and preventing such security threats, but they often result in significant performance overhead and response time. Therefore, those existing approaches are inflexible for large‐scale networks and real‐time applications. For this reason, a novel and adaptive threshold‐based kernel‐level intrusion detection and prevention system by leveraging SDN capabilities are proposed. The proposed systems to detect and mitigate the aforementioned threats within an SDN over widely used traditional IDPS technologies, Snort and Zeek, are comparatively examined. The approach is evaluated using a mixture of fundamental adverse attacks and SDN‐specific threats on a real‐world testbed. The experimental results demonstrate the efficacy of the mechanism to detect and mitigate SYN flood attacks within an SDN environment.

Cloud and fog computing along with network function virtualization technology have significantly shifted the development of network architectures. They yield in reduced capital and operating expenditures, while achieving network flexibility and scalability to accommodate the massive growth in data traffic volumes from user terminals requesting various services and applications. Now cloud solutions here offer abundant computing and storage resources, at the detriment of high end-to-end delays, hence limiting quality of service for delay-sensitive applications. Meanwhile, fog solutions offer reduced delays, at the detriment of limited resources. Existing efforts focus on merging the two solutions and propose multi-tier hybrid fog-cloud architectures to leverage their both saliencies. However, these approaches can be inefficient when the applications are delay-sensitive and require high resources. Hence this work proposes a novel standalone heterogeneous fog architecture that is composed of high-capacity and low-capacity fog nodes, both located at the terminals proximity. Thereby, realizing a substrate network that offers reduced delays and high resources, without the need to relay to the cloud nodes. Moreover, the work here leverages and deploys a deep learning network to propose a service function chain provisioning scheme implemented on this architecture. The scheme predicts the popular network functions, and maps them on the high-capacity nodes, whereas it predicts the unpopular network functions and maps them on the low-capacity nodes. The goal is to predict the next incoming function and prefetch it on the node. Hence, when a future request demands the same function, it can be cached directly from the node, at reduced resources consumption, processing times, cost, and energy consumption. Also, this yields in higher number of satisfied requests and increased capacity. The deep learning network yields reduced loss model and high success rates.

Network function virtualization (NFV) technology enables service providers to implement software-based network processing functionalities on standard computing servers (nodes). As such, this approach mandates the need for mapping virtual network functions (VNFs) in service function chains (SFCs) on these nodes for incoming service requests. Now traditional VNF mapping schemes use cloud nodes for its abundant available resources, at the detriment of prolonged network delays. Alternative schemes use fog nodes that return reduced delays, at the detriment of limited resources. Hence in this work, a novel SFC provisioning scheme is proposed for a hybrid fog-cloud architecture of various resources. The architecture is composed of a single fog and single cloud layer, in order to accommodate both delay-sensitive and delay-tolerant requirements for large number of incoming requests, respectively. This scheme yields a tradeoff between standalone cloud and fog solutions when implemented on the proposed hybrid architecture, in terms of the number of satisfied requests, network delay, resources consumption, energy consumption, and realization cost at large traffic volumes. The proposed scheme achieves 15-40% higher traffic capacity than fog solutions, 21-43% reduced delay, 45-52% less energy consumption levels and 28-30% less cost as compared to cloud solutions.

The continuing increase in air traffic, along with airline operators gradually adopting IP-based network technologies, has led to the transformational concept of e-Enabled or “connected” aircraft. This new framework envisions a single aeronautical communications architecture connecting across the entire spectrum of the aviation sector. However, due to the complex and multidimensional nature of aviation operations, no single technology can achieve the above goal. Instead, building an integrated system which uses multiple communication protocols and architectures, as well as cloud computing and big data analytics, is the most promising way forward. Hence this paper surveys the latest trends in emerging network communication systems for commercial aviation. A range of cyber-threats is then identified for the e-Enabled aircraft paradigm, followed by discussions on related solution methodologies. Note that the topics related to military aviation security are not considered here.

The concept of Bitcoin was first introduced by an unknown individual (or a group of people) named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is a peer-to-peer cryptocurrency and a decentralized worldwide payment system for digital currency where transactions take place among users without any intermediary. Bitcoin transactions are performed and verified by network nodes and then registered in a public ledger called blockchain, which is maintained by network entities running Bitcoin software. To date, this cryptocurrency is worth close to U.S. $150 billion and widely traded across the world. However, as Bitcoin's popularity grows, many security concerns are coming to the forefront. Overall, Bitcoin security inevitably depends upon the distributed protocols-based stimulant-compatible proof-of-work that is being run by network entities called miners, who are anticipated to primarily maintain the blockchain (ledger). As a result, many researchers are exploring new threats to the entire system, introducing new countermeasures, and therefore anticipating new security trends. In this survey paper, we conduct an intensive study that explores key security concerns. We first start by presenting a global overview of the Bitcoin protocol as well as its major components. Next, we detail the existing threats and weaknesses of the Bitcoin system and its main technologies including the blockchain protocol. Last, we discuss current existing security studies and solutions and summarize open research challenges and trends for future research in Bitcoin security.

A major challenge for intrusion prevention system (IPS) sensors in today’s Internet is the amount of traffic these devices have to inspect. Hence this paper presents a linear program (LP) for traffic scheduling in multi-sensor environments that alleviates inspection loads at IPS sensors. The model discriminates traffic flows so that the amount of inspected suspicious traffic is maximized. While the LP is not constrained to integral solutions, traffic belonging to a flow is mostly scheduled for inspection to a single sensor, which facilitates the collection of state information. An analysis of how the Simplex algorithm solves the model and numerical results demonstrate that state information can be preserved without imposing integral constraints. This benefit also prevents the LP from becoming an integer LP, and this is essential for efficiently implementing the proposed model. The paper also shows that the ratio of the total number of flows integrally inspected by a single sensor to the total number of flows inspected in a multi-sensor environment depends upon the ratio of IPS sensor capacity to flow traffic rate. Finally, some practical deployment observations are also presented.