Computational intelligence-enabled cybersecurity for the Internet of Things

The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies

Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation

Deep reinforcement learning (DRL) has been proven its efficiency in capturing users' dynamic interests in recent literature. However, training a DRL agent is challenging, because of the sparse environment in recommender systems (RS), DRL agents could spend times either exploring informative user-item interaction trajectories or using existing trajectories for policy learning. It is also known as the exploration and exploitation trade-off which affects the recommendation performance significantly when the environment is sparse. It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems. As a step to address this issue, We design a novel intrinsically ,otivated reinforcement learning method to increase the capability of exploring informative interaction trajectories in the sparse environment, which are further enriched via a counterfactual augmentation strategy for more efficient exploitation. The extensive experiments on six offline datasets and three online simulation platforms demonstrate the superiority of our model to a set of existing state-of-the-art methods

An LSH-based offloading method for IoMT services in integrated cloud-edge environment

© 2021 ACM. Benefiting from the massive available data provided by Internet of multimedia things (IoMT), enormous intelligent services requiring information of various types to make decisions are emerging. Generally, the IoMT devices are equipped with limited computing power, interfering with the process of computation-intensive services. Currently, to satisfy a wide range of service requirements, the novel computing paradigms, i.e., cloud computing and edge computing, can potentially be integrated for service accommodation. Nevertheless, the private information (i.e., location, service type, etc.) in the services is prone to spilling out during service offloading in the cloud-edge computing. To avoid privacy leakage while improving service utility, including the service response time and energy consumption for service executions, a Locality-sensitive-hash (LSH)-based offloading method, named LOM, is devised. Specifically, LSH is leveraged to encrypt the feature information for the services offloaded to the edge servers with the intention of privacy preservation. Eventually, comparative experiments are conducted to verify the effectiveness of LOM with respect to promoting service utility

An IoT-oriented data placement method with privacy preservation in cloud environment

© 2018 Elsevier Ltd IoT (Internet of Things) devices generate huge amount of data which require rich resources for data storage and processing. Cloud computing is one of the most popular paradigms to accommodate such IoT data. However, the privacy conflicts combined in the IoT data makes the data placement problem more complicated, and the resource manager needs to take into account the resource efficiency, the power consumption of cloud data centers, and the data access time for the IoT applications while allocating the resources for the IoT data. In view of this challenge, an IoT-oriented Data Placement method with privacy preservation, named IDP, is designed in this paper. Technically, the resource utilization, energy consumption and data access time in the cloud data center with the fat-tree topology are analyzed first. Then a corresponding data placement method, based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is designed to achieve high resource usage, energy saving and efficient data access, and meanwhile realize privacy preservation of the IoT data. Finally, extensive experimental evaluations validate the efficiency and effectiveness of our proposed method

Privacy Preservation for Federated Learning with Robust Aggregation in Edge Computing

Benefiting from the powerful data analysis and prediction capabilities of artificial intelligence (AI), the data on the edge is often transferred to the cloud center for centralized training to obtain an accurate model. To resist the risk of privacy leakage due to frequent data transmission between the edge and the cloud, federated learning (FL) is engaged in the edge paradigm, uploading the model updated on the edge server (ES) to the central server for aggregation, instead of transferring data directly. However, the adversarial ES can infer the update of other ESs from the aggregated model and the update may still expose some characteristics of data of other ESs. Besides, there is a certain probability that the entire aggregation is disrupted by the adversarial ESs through uploading a malicious update. In this paper, a privacy-preserving FL scheme with robust aggregation in edge computing is proposed, named FL-RAEC. First, the hybrid privacy-preserving mechanism is constructed to preserve the integrity and privacy of the data uploaded by the ESs. For the robust model aggregation, a phased aggregation strategy is proposed. Specifically, anomaly detection based on autoencoder is performed while some ESs are selected for anonymous trust verification at the beginning. In the next stage, via multiple rounds of random verification, the trust score of each ES is assessed to identify the malicious participants. Eventually, FL-RAEC is evaluated in detail, depicting that FL-RAEC has strong robustness and high accuracy under different attacks

Molecular Cause and Functional Impact of Altered Synaptic Lipid Signaling Due to a \u3cem\u3eprg-1\u3c/em\u3e Gene SNP

Loss of plasticity‐related gene 1 (PRG‐1), which regulates synaptic phospholipid signaling, leads to hyperexcitability via increased glutamate release altering excitation/inhibition (E/I) balance in cortical networks. A recently reported SNP in prg‐1 (R345T/mutPRG‐1) affects ~5 million European and US citizens in a monoallelic variant. Our studies show that this mutation leads to a loss‐of‐PRG‐1 function at the synapse due to its inability to control lysophosphatidic acid (LPA) levels via a cellular uptake mechanism which appears to depend on proper glycosylation altered by this SNP. PRG‐1+/− mice, which are animal correlates of human PRG‐1+/mut carriers, showed an altered cortical network function and stress‐related behavioral changes indicating altered resilience against psychiatric disorders. These could be reversed by modulation of phospholipid signaling via pharmacological inhibition of the LPA‐synthesizing molecule autotaxin. In line, EEG recordings in a human population‐based cohort revealed an E/I balance shift in monoallelic mutPRG‐1 carriers and an impaired sensory gating, which is regarded as an endophenotype of stress‐related mental disorders. Intervention into bioactive lipid signaling is thus a promising strategy to interfere with glutamate‐dependent symptoms in psychiatric diseases

Effect of Sr on the properties of Ce-Zr-La mixed oxides

Ce–Zr–La–Sr mixed oxides, with different Sr contents, were prepared by the sol–gel method. In a flow-system microreactor, the reduction properties and the oxygen storage capacity (OSC) of the Ce–Zr–La–Sr mixed oxides were investigated by a temperature programmed reduction (TPR) and a pulse technique. It was shown that the properties of the Ce–Zr–La mixed oxides depend on the Sr content and that the optimum Sr content in the Ce–Zr–La–Sr mixed oxide is 3 mol%. The Ce–Zr–La–Sr mixed oxides doped with 3 mol% Sr (Ce0.52Zr0.4La0.05Sr0.03O1.945) has the largest specific surface area and better reduction properties and oxygen storage capacity in comparison to the other investigated samples. The XRD results of the Ce–Zr–La–Sr mixed oxides showed that their X-ray diffraction patterns are well in agreement with that of fluorite-type CeO2 with Sr ions incorporated into the Ce–Zr–La mixed oxide structures. With increasing calcination temperature, the intensity of the X-ray diffraction peaks increased, but no new peaks were observed. All of these indicate that the synthesized samples had good thermal stability

China's oil reserve forecast and analysis based on peak oil models

In order to forecast future oil production it is necessary to know the size of the reserves and use models. In this article, we use the typical Peak Oil models, the Hu-Chen-Zhang model usually called HCZ model and the Hubbert model, which have been used commonly for forecasting in China and the world, to forecast China's oil Ultimate Recovery (URR). The former appears to give more realistic results based on an URR for China of 15.64 billion tons. The study leads to some suggestions for new policies to meet the unfolding energy situation.Ultimate Recovery Peak oil models Energy policy