A broad learning-based comprehensive defence against SSDP reflection attacks in IoTs

The proliferation of Internet of Things(IoT)rapidly increases the possiblities of Simple Service Discovery Protocol(SSDP)reflection attacks.Most DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance,and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing resources.This paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks,called Broad Learning based Comprehensive Defence(BLCD).The defence strategies work along the attack chain,starting from attack sources to victims.It defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection attacks.BLCD also detects suspicious traffic at bots,service providers and victims by using broad learning,and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS packets.For evaluations,we thoroughly analyze attack traffic when deploying BLCD to different defence locations.Experiments show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service,and detect malicious packets with an accuracy of 99.99%.

An Industrial Internet Platform for Massive Pig Farming (IIP4MPF)

Pig farming is becoming a key industry of China’s rural economy in recent years. The current pig farming is still relatively manual, lack of latest Information and Communication Technology (ICT) and scientific management methods. This paper proposes an industrial internet platform for massive pig farming, namely, IIP4MPF, which aims to leverage intelligent pig breeding, production rate and labor productivity with the use of artificial intelligence, the Internet of Things, and big data intelligence. We conducted requirement analysis for IIP4MPF using software engineering methods, designed the IIP4MPF system for an integrated solution to digital, interconnected, intelligent pig farming. The practice demonstrates that the IIP4MPF platform significantly improves pig farming industry in pig breeding and productivity.

Guest editorial:Special issue on data intelligence for IoT

The Internet of Things(IoT)is increasingly deployed to enable smart applications.Various types of data are accumulated continuously during the running of these applications.Managing and using these IoT data to derive intelligence for making the smart world reality is attracting both industrial and academic efforts.Though quite some progress has been made in this area,there is still a need for high data intelligence in IoT applications.

Revisiting digital twins: Origins, fundamentals, and practices

The digital twins(DT)has quickly become a hot topic since it was proposed.It appears in all kinds of commercial propaganda and is widely quoted by academic circles.However,the term DT has misstatements and is misused in business and academics.This study revisits DT and defines it to be a more advanced system/product/service modeling and simulation environment that combines most modern information communication technologies(ICTs)and engineering mechanism digitization and characterized by system/product/service life cycle management,physically geometric visualization,real-time sensing and measurement of system operating conditions,predictability of system performance/safety/lifespan,and complete engineering mechanisms-based simulations.The idea of DT originates from modeling and simulation practices of engineering informatization,including virtual manufacturing(VM),model predictive control,and building information modeling(BIM).On the basis of the two-element VM model,we propose a three-element model to represent DT.DT does not have its unique technical characteristics.The existing practices of DT are extensions of the engineering informatization embracing modern ICTs.These insights clarify the origin of DT and its technical essentials.

Analytical Determination of Interwell Connectivity Based on Interwell Influence

Interwell connectivity, an important element in reservoir characterization, especially for water flooding,is used to make decisions for better oil production. The existing methods in literature directly use related data of wells to infer interwell connectivity, but they ignore the influence between different wells. The connection of one well to more than two wells(as is often true in the oil field well pattern) will impact the accuracy of the connectivity analysis. To address this challenge, this paper proposes the Particle Swarm Optimization-based CatBoost for Interwell Connectivity(PSOC4IC) based on relative features to analyze interwell connectivity with the combination of joint mutual information maximization-based denoising sparse autoencoder for inter-feature construction and extraction and PSO-based CatBoost(PSO-CatBoost) for connectivity prediction with high-dimensional noise data.The experimental results show that the PSOC4IC improves analysis accuracy.