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—Exactly-once data processing/delivery can be guaranteed in traditional big data processing systems, e.g. Apache Flink. Checkpoint is commonly used as the solution. Each operator in these systems can restart from the last successfully saved state whenever a failure happens. It is not necessary to restore the logical job graph onto the same device(s) in traditional datacentre scenarios with powerful servers close to each other. However, the datacentre oriented solutions are not suitable for IoT collaborative edge computing scenarios. The logical job graph is tightly coupled to the physical topology in IoT networks. Data processing task(s) cannot be placed at a random edge device to recover from a network failure as it needs to evaluate the benefits of transmitting data versus processing/aggregating the data. To address the above challenges, this paper proposes an Information Centric Networking based solution and correspondent protocols to provide Exactly-oncecomputation for the Collaborative Edge in IoT (ECE). It contains a job execution scheme to deliver IoT jobs with exactly once data computation guarantee and a recovery procedure to dynamically change the IoT job execution graph while experiencing link failures. The protocol also provides a checking procedure on data state (received/un-received and computed/un-computed) to prevent any data loss or duplicated data processing due to the updated job graph. A data identification approach based on the job graph is devised to support the ECE functionality. A testbed has been developed on ndnSIM and the simulation results have verified the feasibility and scalability of ECE design. It also evaluates the overhead incurred by the ECE protocol to guarantee exactly once data computation. yes

The Internet of Things (IoT) is revolutionising nearly every aspect of modern life, playing an ever greater role in both industrial and domestic sectors. The increasing frequency of cyber-incidents is a consequence of the pervasiveness of IoT. Threats are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams must deal with a diverse and complex threat landscape that is constantly evolving. Traditional security solutions cannot protect such sys- tems adequately and so researchers have begun to use Machine Learning algorithms to discover effective defence systems. In this paper, we investigate how one approach from the domain of evolutionary computation - grammatical evolution - can be used to identify cyberattacks in IoT environments. The experiments were conducted on up-to-date datasets and compared with state- of-the-art algorithms. The potential application of evolutionary computation-based approaches to detect unknown attacks is also examined and discussed

Content replication and name-based routing in Named Data Network (NDN) naturally lead to connectionless multi-source and multipath transmissions. Traditional congestion control designed for end-to-end connections cannot well fit this architecture. Explicit congestion notification (ECN) can better support NDN because congestion is detected where it occurs and ECN can timely notify the traffic initiator of congestion. NDN can be deployed as an overlay protocol (sharing the underlying devices with other protocols), which means the congestion may also occur at an underlying device (e.g. a switch). In this case, the NDN nodes cannot access the queue or other link status at a remote underlying device for congestion detection. A promising ECN scheme must be able to detect congestion happening anywhere (at an NDN node or an underlying device) without using underlying link information. This paper proposes Hop-byHop Congestion Measurement (HbHCM) and Practical Active Queue Management (PAQM) to enable detecting congestion and generating ECN at NDN nodes via monitoring the change of transmission delays. HbHCM measures the transmission delay at the hop level and PAQM converts the delay to ECN signals to notify consumers. We compared HbHCM + PAQM with two milestone solutions (router-label and ECN-based). The simulation results show that HbHCM + PAQM can accurately detect congestion, improve bandwidth utilisation and better support multipath transmission, no need to rely on route or link information.

Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area.

A calibration method for a high-resolution hybrid MIMO turntable radar imaging system is presented. A line of small metal balls is used in the calibration process to measure the position shift caused by undesired effects of the antennas. The unwanted effects in the near-field antenna response are analysed and significantly mitigated based on the referential features of the MIMO configuration.

Surveillance video recording is a powerful method of deterring unlawful activities. A robust data protection-by-design solution can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered. Similarly, video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing. This article presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos by means of hashing and steganography. The method conforms to the data protection regulations, which are increasingly adopted by governments, and is applicable to network edge storage. Encryption keys are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored steganographically within video frames themselves, independently of the content. Added protection is provided by hiding information within the two least-valued of pixel bitplanes, using a newly introduced technique that randomizes the pixel storage locations on a per video frame and video-capture device basis. Overall, the proposed method has turned out to not only preserve the integrity of stored video data but also results in minimal degradation of the video data resulting from steganography. Despite the inclusion of hidden information, video frames will still be available for common image-processing tasks such as tracking and classification, as their objective video quality is almost unchanged. yes

When a user uploads audio files to a music stream- ing service, these files are subsequently re-encoded to lower bitrates to target different devices, e.g. low bitrate for mobile. To save time and bandwidth uploading files, some users encode their original files using a lossy codec. The metadata for these files cannot always be trusted as users might have encoded their files more than once. Determining the lowest bitrate of the files allows the streaming service to skip the process of encoding the files to bitrates higher than that of the uploaded files, saving on processing and storage space. This paper presents a model that uses quality predictions from ViSQOLAudio, a full reference objective audio quality metric, as features in combination with a multi-class support vector machine classifier. An experiment on twice-encoded files found that low bitrate codecs could be classified using audio quality features. The experiment also provides insights into the implications of multiple transcodes from a quality perspective.