Ultrasonic testing and microscopic analysis on concrete under sulfate attack and cyclic environment

The damage process of concrete exposed to sodium sulfate attack and drying-wetting cycles was investigated. The water to binder(W/B) ratio and the concentration of sulfate solution were taken as variable parameters. Through the experiment, visual change, relative dynamic modulus of elasticity(RDME) and the surface damage layer thickness of concrete were measured.Furthermore, SEM and thermal analysis were used to investigate the changing of microstructure and corrosion products of concrete.The test results show that the ultrasonic velocity is related to the damage layer of concrete. It approves that an increase in damage layer thickness reduces the compactness and the ultrasonic velocity. The deterioration degree of concrete could be estimated effectively by measuring the surface damage layer and the RDME of concrete. It is also found that the content of gypsum in concrete is less than that of ettringite in test, and some gypsum is checked only after a certain corrosion extent. When the concrete is with high W/B ratio or exposed to high concentration of sulfate solution, the content of ettringite first increases and then decreases with corrosion time. However, the content of gypsum increases at a steady rate. The content of corrosion products does not correspond well with the observations of RDME change, and extensive amount of corrosion products can be formed before obvious damage occurs.

A Secure DHCP Protocol to Mitigate LAN Attacks

Network security has become more of a concern with the rapid growth and expansion of the Internet. While there are several ways to provide security in the application, transport, or network layers of a network, the data link layer (Layer 2) security has not yet been adequately addressed. Data link layer protocols used in local area networks (LANs) are not designed with security features. Dynamic host configuration protocol (DHCP) is one of the most used network protocols for host configuration that works in data link layer. DHCP is vulnerable to a number of attacks, such as the DHCP rouge server attack, DHCP starvation attack, and malicious DHCP client attack. This work introduces a new scheme called Secure DHCP (S-DHCP) to secure DHCP protocol. The proposed solution consists of two techniques. The first is the authentication and key management technique that is used for entities authentication and management of security key. It is based on using Diffie-Hellman key exchange algorithm supported by the difficulty of Elliptic Curve Discrete Logarithm Problem (ECDLP) and a strong cryptographic one-way hash function. The second technique is the message authentication technique, which uses the digital signature to authenticate the DHCP messages exchanged between the clients and server.

A network security situation awareness method based on layered attack graph

The real-time of network security situation awareness(NSSA)is always affected by the state explosion problem.To solve this problem,a new NSSA method based on layered attack graph(LAG)is proposed.Firstly,network is divided into several logical subnets by community discovery algorithm.The logical subnets and connections between them constitute the logical network.Then,based on the original and logical networks,the selection of attack path is optimized according to the monotonic principle of attack behavior.The proposed method can sharply reduce the attack path scale and hence tackle the state explosion problem in NSSA.The experiments results show that the generation of attack paths by this method consumes 0.029 s while the counterparts by other methods are more than 56 s.Meanwhile,this method can give the same security strategy with other methods.

A Survey of Security Issues in Cognitive Radio Networks

In the last decade,cognitive radio(CR) has emerged as a major next generation wireless networking technology,which is the most promising candidate solution to solve the spectrum scarcity and improve the spectrum utilization.However,there exist enormous challenges for the open and random access environment of CRNs,where the unlicensed secondary users(SUs) can use the channels that are not currently used by the licensed primary users(PUs) via spectrum-sensing technology.Because of this access method,some malicious users may access the cognitive network arbitrarily and launch some special attacks,such as primary user emulation attack,falsifying data or denial of service attack,which will cause serious damage to the cognitive radio network.In addition to the specifi c security threats of cognitive network,CRNs also face up to the conventional security threats,such as eavesdropping,tampering,imitation,forgery,and noncooperation etc..Hence,Cognitive radio networks have much more risks than traditional wireless networks with its special network model.In this paper,we considered the security threats from passive and active attacks.Firstly,the PHY layer security is presented in the view of passive attacks,and it is a compelling idea of using the physical properties of the radio channel to help provide secure wireless communications.Moreover,malicious user detection is introduced in the view of active attacks by means of the signal detection techniques to decrease the interference and the probabilities of false alarm and missed detection.Finally,we discuss the general countermeasures of security threats in three phases.In particular,we discuss the far reaching effect of defensive strategy against attacks in CRNs.

Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

Deep 3D mesh watermarking with self-adaptive robustness

Robust 3D mesh watermarking is a traditional research topic in computer graphics,which provides an efficient solution to the copyright protection for 3D meshes.Traditionally,researchers need manually design watermarking algorithms to achieve suffcient robustness for the actual application scenarios.In this paper,we propose the first deep learning-based 3D mesh watermarking network,which can provide a more general framework for this problem.In detail,we propose an end-to-end network,consisting of a watermark embedding sub-network,a watermark extracting sub-network and attack layers.We employ the topology-agnostic graph convolutional network(GCN)as the basic convolution operation,therefore our network is not limited by registered meshes(which share a fixed topology).For the specific application scenario,we can integrate the corresponding attack layers to guarantee adaptive robustness against possible attacks.To ensure the visual quality of watermarked 3D meshes,we design the curvature consistency loss function to constrain the local geometry smoothness of watermarked meshes.Experimental results show that the proposed method can achieve more universal robustness while guaranteeing comparable visual quality.