Detection of Wormhole Attack and Secure Path Selection in Wireless Sensor Network

In Wireless Sensor Network (WSN), securable data transmission is one of the most challenges. During the transmission between the source and a destination node, routing information of the particular path may be misbehaved by the particular nodes which are known as wormhole nodes/attackers. The paths which include the wormhole nodes are known as wormhole attacked paths. For improving security in WSN, these wormhole attacked paths should be identified. To achieve this, wormhole attack detection method and optimal or secure path selection are presented in this paper. Initially, ‘K’ paths or multiple paths are generated between source and destination using Ad-hoc On demand Multipath Distance Vector (AOMDV) routing protocol. Then, the source node identifies the wormhole attacked path by verifying the Detection Packet (DP) and Feedback Packet (FP) from the destination. After detecting the wormhole attacked paths, the source node selects the optimal path among the attacker free paths using Particle Swarm Optimization (PSO) algorithm. Simulation results show that the performance of the proposed approach improves energy efficiency and network lifetime of the network.

     

A Lightweight and Scalable Physical Layer Attack Detection Mechanism for the Internet of Things (IoT) Using Hybrid Security Schema

Wireless Personal Communications - The Internet of Things, also known as the IoT, refers to the billions of devices around the world that are now connected to the Internet, collecting and sharing...     

SEPAR: A New Lightweight Hybrid Encryption Algorithm with a Novel Design Approach for IoT

Wireless Personal Communications - This paper presents a new hybrid encryption algorithm with 16-bit block size and a 128-bit initialization vector, referred to as SEPAR, and it is suitable for IoT...     

基于FSM检测的移动自组网攻击分类研究

在移动自组网环境下,由于移动节点可能被攻击截获,导致攻击从内部产生,传统的网络安全措施难以应用,只有通过入侵检测才能发现攻击者。通过分析移动自组网的攻击类型,并构造从恶意节点发起的攻击树,采用有限状态机的思想,设计一个基于FSM的入侵检测算法。采用该算法的入侵检测系统可通过邻居节点的监视,实时地检测到节点的各种攻击行为。     

网络隐写信息传递系统的高效攻击检测

提出了一种基于多路复用波束域约束指向形成的网络隐写信息传递系统的高效攻击检测算法,设计了网络隐写信息传递系统模型,提取信号的本征频率、群延迟和包络等信号特征,对链路层中的加密数据进行块内频率检测,为提高检测概率,对提取得到的多路复用波束域约束指向输出结果进行频分复用分解。根据最小均方误差准则,得到在网络隐写信息传递系统中攻击信号的波束域约束指向形成,实现正交频谱分离,抑制了干扰噪声,实现对攻击信号的高效检测。仿真结果表明,该算法检测出攻击信号波峰明显,抗噪能力强,检测概率高,在网络安全设计和信号检测等领域均具有一定的应用价值。     

Isolation of DDoS Attack in IoT: A New Perspective

Wireless Personal Communications - Internet of Things (IoT) involves all the surrounding with smart devices to Internet and has been gaining more recognition because it provides substantial...     

A Hybrid Approach for Extracting EMG signals by Filtering EEG Data for IoT Applications for Immobile Persons

Wireless Personal Communications - Brain Computer interface (BCI) is an emerging technology which empowers human to regulate the computer or other electronic gadgets with brain signals. This paper...     

Trust Model for IoT Using Cluster Analysis: A Centralized Approach

At lower noise levels, the majority of filter-based impulse noise removal approaches outperform each other. The purpose of this paper is to design an efficient adaptive pulse coupled neural network (APCNN) technique with improved alpha guided grey wolf optimization (IAgGWO) for the elimination of high-density impulse noise. This noise reduction technique is divided into two stages: the detection of noisy pixels and the replacement of a noisy pixel with a data pixel. The IAgGWO technique is utilised to isolate the optimal values for identifying impulse noisy pixels, and the APCNN filtering technique is used to supplant them. This technique provides more accurate and clean filtered images while preserving critical edge pixel information. To demonstrate the IAgGWO-APCNN strategy's efficacy, various degrees of impulse noise were applied to the image and tested. With PSNR of 42 percent, SSIM of 99 percentand STD of 40 percent on satellite pictures, the suggested noise removal model has proved its unshakable consistency in terms of both qualitative and quantitative assessment.

     

A Smart Parking System: An IoT Based Computer Vision Approach for Free Parking Spot Detection Using Faster R-CNN with YOLOv3 Method

Wireless Personal Communications - Internet-of-things has altered pervasive figuring with many applications created around different kinds of sensors/devices. As for these applications lower...     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

On Scalable Attack Detection in the Network

Current intrusion detection and prevention systems seek to detect a wide class of network intrusions (e.g., DoS attacks, worms, port scans) at network vantage points. Unfortunately, even today, many IDS systems we know of keep per-connection or per-flow state to detect malicious TCP flows. Thus, it is hardly surprising that these IDS systems have not scaled to multi-gigabit speeds. By contrast, both router lookups and fair queuing have scaled to high speeds using aggregation via prefix lookups or DiffServ. Thus, in this paper, we initiate research into the question as to whether one can detect attacks without keeping per-flow state. We will show that such aggregation, while making fast implementations possible, immediately causes two problems. First, aggregation can cause behavioral aliasing where, for example, good behaviors can aggregate to look like bad behaviors. Second, aggregated schemes are susceptible to spoofing by which the intruder sends attacks that have appropriate aggregate behavior. We examine a wide variety of DoS and scanning attacks and show that several categories (bandwidth based, claim-and-hold, port-scanning) can be scalably detected. In addition to existing approaches for scalable attack detection, we propose a novel data structure called partial completion filters (PCFs) that can detect claim-and-hold attacks scalably in the network. We analyze PCFs both analytically and using experiments on real network traces to demonstrate how we can tune PCFs to achieve extremely low false positive and false negative probabilities     

全光网络中攻击的检测与定位

从研究攻击的角度出发,提出了全光网络安全管理框架。针对常见网络中的攻击,分析了网络中易受攻击的器件,分别采用参数比较检测法和综合监测器件检测法,准确地检测出带内干扰攻击、带外干扰攻击、窃听和断纤。运用两种新的检测方法,结合攻击的定位算法,就能有效地查找到整个网络的攻击源,且定位于被攻击的器件。     

Attack Detection Application with Attack Tree for Mobile System using Log Analysis

Mobile Networks and Applications - Recently, the use of smart phones has greatly increased because of the development of cheap high-performance hardware. The biggest threat to a smart phone user is...     

Intelligent Ad-Hoc-On Demand Multipath Distance Vector for Wormhole Attack in Clustered WSN

Wireless Personal Communications - In Wireless Senor Networks, security is the most significant issue when sending such an essential message via wireless connection. This helps attackers to access...     

遗传算法优化的混合神经网络入侵检测系统

针对入侵检测系统大都采用单一的检测模式,难以有效地处理漏报、误报和对未知攻击无法有效识别的问题,分析不同类型网络流量的特征,文中提出一种将BP网络、遗传算法和Snort相结合的混合式入侵检测系统,综合了异常检测和误用检测的优点,克服了单一检测模式的不足。实验结果表明,该方法能有效提高入侵检测系统的检测率和准确率。     

ZigBee同频攻击检测抑制模型研究

针对ZigBee通信中易遭受同频攻击导致数据阻塞和失真问题,该文提出一种同频攻击检测模型。该模型利用信号频谱的高斯分布规律和同频攻击对变换域幅值的影响进行同频攻击检测。在此基础上,通过嵌入空闲频带信道跳变机制和基于可变退避周期及接入概率的自适应退避算法,给出了同频攻击检测抑制方案。实验结果表明,该文模型和方案可以有效抵御同频攻击。     

Hybrid System for Robust Faces Detection

The automatic detection of faces is a very important problem. The effectiveness of biometric authentication based on face mainly depends on the method used to locate the face in the image. This paper presents a hybrid system for faces detection in unconstrained cases in which the illumination, pose, occlusion, and size of the face are uncontrolled. To do this, the new method of detection proposed in this paper is based primarily on a technique of automatic learning by using the decision of three neural networks, a technique of energy compaction by using the discrete cosine transform, and a technique of segmentation by the color of human skin. A whole of pictures （faces and no faces） are transformed to vectors of data which will be used for learning the neural networks to separate between the two classes. Discrete cosine transform is used to reduce the dimension of the vectors, to eliminate the redundancies of information, and to store only the useful information in a minimum number of coefficients while the segmentation is used to reduce the space of research in the image. The experimental results have shown that this hybridization of methods will give a very significant improvement of the rate of the recognition, quality of detection, and the time of execution.     

Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network

Wireless Personal Communications - IoT network-connected devices are increasing day by day. It is impossible to allocate a spectrum for all IoT devices. This spectrum scarcity can be solved by...     

推荐系统中基于无监督策略托攻击检测﹡

协同过滤推荐系统中,推荐结果对用户偏好信息的敏感性使得推荐系统易受到人为攻击,即托攻击.恶意用户可以任意使用多重身份,或者是多个人来参与,都能注入恶意信息到推荐系统中.这类攻击严重影响了推荐系统的鲁棒性和准确性.这里深入分析了托攻击,结合主成分分析和变量选择方法,提出一个高精确度鲁棒的协同过滤系统架构,以保护推荐系统抵御用户概貌注入攻击.最后,通过实验验证表明该新型的高精确度的协同过滤系统可以取得更好的检测精度.