Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security

With the construction of smart cities, the number of Internet of Things (IoT) devices is growing rapidly, leading to an explosive growth of malware designed for IoT devices. These malware pose a serious threat to the security of IoT devices. The traditional malware classification methods mainly rely on feature engineering. To improve accuracy, a large number of different types of features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve these issues, a malware classification method based on Word2Vec and Multilayer Perception (MLP) is proposed in this article. First, for one malware sample, Word2Vec is used to calculate a word vector for all bytes of the binary file and all instructions in the assembly file. Second, we combine these vectors into a 256x256x2-dimensional matrix. Finally, we designed a deep learning network structure based on MLP to train the model. Then the model is used to classify the testing samples. The experimental results prove that the method has a high accuracy of 99.54%.This work was supported in part by the Key-Area Research and Development Program of Guangdong Province (2019B010136001), the Basic and Applied Basic Research Major Program for Guangdong Province (2019B030302002), and the Science and Technology Planning Project of Guangdong Province (LZC0023 and LZC0024). Authors’ addresses: Y. Qiao, Cyberspace Security Research Center, Peng Cheng Laboratory, No. 2 Xingke 1st Street, Shen-zhen, China, 518000; email: [email protected]; W. Zhang, School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin, China, 150001, Cyberspace Security Research Center, Peng Cheng Laboratory, No. 2 Xingke 1st Street, Nanshan District, Shenzhen, China, 518000; email: [email protected]; X. Du, Department of Computer and Information Sciences, Temple University, 1801 N. Broad Street, Philadelphia, USA, PA 19122; email: [email protected]; M. Guizani, Department of Compute Science and Engineering, Qatar University, University Street, Doha, Qatar; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2021 Association for Computing Machinery. 1533-5399/2021/09-ART10 $15.00 https://doi.org/10.1145/343675

Experimental and Numerical Study on the Penetration Performance of a Shaped Charge

In guided ammunition, because a shaped energy jet warhead is located behind the control cabin (including the guidance cabin, the steering gear cabin, and the flight control cabin), the penetration order of a shaped energy jet is the control cabin and the target plate. In order to obtain maximum penetration depth by a shaped energy jet into a Q235 steel plate, the penetration performance of shaped energy jets was studied by numerical simulation and experimental verification. Firstly, the penetration performance of a warhead under different conditions at a certain explosion height is studied, which is the penetration performance of a Q235 steel plate with and without the control cabin. Secondly, the numerical simulation results are verified by experimental method. The numerical simulation and experimental results showed that, after penetration of the shaped energy jet warhead into the control cabin, it continued to penetrate the 20 mm-thick Q235 steel plate. At a certain explosion height, the maximum penetration depth of the shaped energy jet warhead into the Q235 steel plate was about 80 mm. Alongside the numerical simulation and experiment, the armor-breaking process of the shaped charge jet was analyzed theoretically. The results show that when the shaped energy jet warhead is located behind the control cabin, although the control cabin will have a certain impact on the penetration ability of shaped energy jet, the penetration performance of the residual jet still has the ability to penetrate light armor

Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach

Vehicular Ad hoc Network (VANET) is an enabling technology to provide a variety of convenient services in intelligent transportation systems, and yet vulnerable to various intrusion attacks. Intrusion detection systems (IDSs) can mitigate the security threats by detecting abnormal network behaviours. However, existing IDS solutions are limited to detect abnormal network behaviors under local sub-networks rather than the entire VANET. To address this problem, we utilize deep learning with generative adversarial networks and explore distributed SDN to design a collaborative intrusion detection system (CIDS) for VANETs, which enables multiple SDN controllers jointly train a global intrusion detection model for the entire network without directly exchanging their sub-network flows. We prove the correctness of our CIDS in both IID (Independent Identically Distribution) and non-IID situations, and also evaluate its performance through both theoretical analysis and experimental evaluation on a real-world dataset. Detailed experimental results validate that our CIDS is efficient and effective in intrusion detection for VANETs.This work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010136001, in part by the Natural Science Foundation of China under Grant 61732022 and Grant 61672195, and in part by the Peng Cheng Laboratory Project of Guangdong Province under Grant PCL2018KP004 and Grant PCL2018KP005. The Associate Editor for this article was N. Kumar. (Corresponding author: Weizhe Zhang.) Jiangang Shu is with the Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen 518000, China (e-mail: [email protected])

RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything

While the widespread use of ubiquitously connected devices in Internet of Everything (IoE) offers enormous benefits, it also raises serious privacy concerns. Federated learning, as one of the promising solutions to alleviate such problems, is considered as capable of performing data training without exposing raw data that kept by multiple devices. However, either malicious attackers or untrusted servers can deduce users' privacy from the local updates of each device. Previous studies mainly focus on privacy-preserving approaches inside the servers, which require the framework to be built on trusted servers. In this article, we propose a privacy-enhanced federated learning scheme for IoE. Two mechanisms are adopted in our approach, namely the randomized response (RR) mechanism and the local adaptive differential privacy (LADP) mechanism. RR is adopted to prevent the server from knowing whose updates are collected in each round. LADP enables devices to add noise adaptively to its local updates before submitting them to the server. Experiments demonstrate the feasibility and effectiveness of our approach

Take chicks as an example: Rummeliibacillus stabekisii CY2 enhances immunity and regulates intestinal microbiota by degrading LPS to promote organism growth and development

Human and animal organisms are in a state of low-grade inflammation caused by low concentrations of LPS. The novel potential probiotic Rummeliibacillus stabekisii CY2 was pre-proven in vitro to reduce LPS. Explored its effect in vivo, chicks were fed in different groups (0, CY2L, and CY2H). Results showed that CY2H had significantly lower serum levels of LPS and the inflammatory factors IL-1 and IL-6, reduced low-grade inflammatory levels, and significantly higher levels of the inflammatory inhibitory factor IL-10. The structure of the chicks’ intestinal microbiota was improved. The relative abundance of the potentially probiotic Lactobacillales increased significantly. Importantly, the growth performance of chicks has been significantly improved. In conclusion, CY2 supplementation can improve immunity through the degradation of LPS, alleviate low-grade inflammation, improve intestinal microbiota, and thus promote the growth of the chicks

Systematic Understanding of the Mechanism of Baicalin against Ischemic Stroke through a Network Pharmacology Approach

Ischemic stroke is accompanied by high mortality and morbidity rates. At present, there is no effective clinical treatment. Alternatively, traditional Chinese medicine has been widely used in China and Japan for the treatment of ischemic stroke. Baicalin is a flavonoid extracted from Scutellaria baicalensis that has been shown to be effective against ischemic stroke; however, its mechanism has not been fully elucidated. Based on network pharmacology, we explored the potential mechanism of baicalin on a system level. After obtaining baicalin structural information from the PubChem database, an approach combined with literature mining and PharmMapper prediction was used to uncover baicalin targets. Ischemic stroke-related targets were gathered with the help of DrugBank, Online Mendelian Inheritance in Man (OMIM), Genetic Association Database (GAD), and Therapeutic Target Database (TTD). Protein-protein interaction (PPI) networks were constructed through the Cytoscape plugin BisoGenet and analyzed by topological methods. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out via the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server. We obtained a total of 386 potential targets and 5 signaling pathways, including mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT), hypoxia-inducible factor-1 (HIF-1), nuclear factor kappa B (NF-κB), and forkhead box (FOXO) signaling pathways. GO analysis showed that these targets were associated with antiapoptosis, antioxidative stress, anti-inflammation, and other physiopathological processes that are involved in anti-ischemic stroke effects. In summary, the mechanism of baicalin against ischemic stroke involved multiple targets and signaling pathways. Our study provides a network pharmacology framework for future research on traditional Chinese medicine