A Novel MegaBAT Optimized Intelligent Intrusion Detection System in Wireless Sensor Networks

Wireless Sensor Network(WSN),whichfinds as one of the major components of modern electronic and wireless systems.A WSN consists of numerous sensor nodes for the discovery of sensor networks to leverage features like data sensing,data processing,and communication.In thefield of medical health care,these network plays a very vital role in transmitting highly sensitive data from different geographic regions and collecting this information by the respective network.But the fear of different attacks on health care data typically increases day by day.In a very short period,these attacks may cause adversarial effects to the WSN nodes.Furthermore,the existing Intrusion Detection System(IDS)suffers from the drawbacks of limited resources,low detection rate,and high computational overhead and also increases the false alarm rates in detecting the different attacks.Given the above-mentioned problems,this paper proposes the novel MegaBAT optimized Long Short Term Memory(MBOLT)-IDS for WSNs for the effective detection of different attacks.In the proposed framework,hyperpara-meters of deep Long Short-Term Memory(LSTM)were optimized by the meta-heuristic megabat algorithm to obtain a low computational overhead and high performance.The experimentations have been carried out using(Wireless Sensor NetworkDetection System)WSN-DS datasets and performance metrics such as accuracy,recall,precision,specificity,and F1-score are calculated and compared with the other existing intelligent IDS.The proposed framework provides outstanding results in detecting the black hole,gray hole,scheduling,flooding attacks and significantly reduces the time complexity,which makes this system suitable for resource-constraint WSNs.

A High-level Architecture for Intrusion Detection on Heterogeneous Wireless Sensor Networks: Hierarchical, Scalable and Dynamic Reconfigurable

Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure;i.e. it is designed and applicable, in one, two or three levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Sensor-based Intrusion Detection System (SIDS) on sensor nodes, Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical, distributed and clustering structure along with clusters' overlapping. Finally, this paper has been designed a questionnaire to verify the proposed idea;then it analyzed and evaluated the acquired results from the questionnaires.

Deep Learning and Entity Embedding-Based Intrusion Detection Model for Wireless Sensor Networks

Wireless sensor networks(WSNs)are considered promising for applications such as military surveillance and healthcare.The security of these networks must be ensured in order to have reliable applications.Securing such networks requires more attention,as they typically implement no dedicated security appliance.In addition,the sensors have limited computing resources and power and storage,which makes WSNs vulnerable to various attacks,especially denial of service(DoS).The main types of DoS attacks against WSNs are blackhole,grayhole,flooding,and scheduling.There are two primary techniques to build an intrusion detection system(IDS):signature-based and data-driven-based.This study uses the data-driven approach since the signature-based method fails to detect a zero-day attack.Several publications have proposed data-driven approaches to protect WSNs against such attacks.These approaches are based on either the traditional machine learning(ML)method or a deep learning model.The fundamental limitations of these methods include the use of raw features to build an intrusion detection model,which can result in low detection accuracy.This study implements entity embedding to transform the raw features to a more robust representation that can enable more precise detection and demonstrates how the proposed method can outperform state-of-the-art solutions in terms of recognition accuracy.

Designing an Agent-Based Intrusion Detection System for Heterogeneous Wireless Sensor Networks: Robust, Fault Tolerant and Dynamic Reconfigurable

Protecting networks against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete architecture of Intrusion Detection System (IDS). The main contribution of this architecture is its modularity and flexibility;i.e. it is designed and applicable, in four steps on intrusion detection process, consistent to the application domain and its required security level. Focus of this paper is on the heterogeneous WSNs and network-based IDS, by designing and deploying the Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the base station (sink). Finally, this paper has been designed a questionnaire to verify its idea, by using the acquired results from analyzing the questionnaires.

Advanced Border Intrusion Detection and Surveillance Using Wireless Sensor Network Technology

Wireless Sensor Network (WSN) has been emerging in the last decade as a powerful tool for connecting physical and digital world. WSN has been used in many applications such habitat monitoring, building monitoring, smart grid and pipeline monitoring. In addition, few researchers have been experimenting with WSN in many mission-critical applications such as military applications. This paper surveys the literature for experimenting work done in border surveillance and intrusion detection using the technology of WSN. The potential benefits of using WSN in border surveillance are huge;however, up to our knowledge very few attempts of solving many critical issues about this application could be found in the literature.

A Compact Trust Computation and Management Approach for Defending against Derailed Attacks for Wireless Sensor Networks and Its Applications

One of the most effective measurements of intercommunication and collaboration in wireless sensor networks which leads to provide security is Trust Management. Most popular decision making systems used to collaborate with a stranger are tackled by two different existing trust management systems: one is a policy-based approach which verifies the decision built on logical properties and functionalities;the other approach is reputation-based approach which verifies the decision built on physical properties and functionalities of WSN. Proofless authorization, unavailability, vagueness and more complexity cause decreased detection rate and spoil the efficacy of the WSN in existing approaches. Some of the integrated approaches are utilized to improve the significance of the trust management strategies. In this paper, a Compact Trust Computation and Management (CTCM) approach is proposed to overcome the limitations of the existing approaches, also it provides a strong objective security with the calculability and the available security implications. Finally, the CTCM approach incorporates the optimum trust score for logical and physical investigation of the network resources. The simulation based experiment results show that the CTCM compact trust computation and management approach can provide an efficient defending mechanism against derailing attacks in WSN.

Anomaly Detection Based on Data-Mining for Routing Attacks in Wireless Sensor Networks

With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wireless sensor networks.The schemeuses the Apriori algorithm to extract traffic patternsfrom both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model.Through thecombination of these two algorithms,routing attackscan be detected effectively and automatically.Themain advantage of the proposed approach is that it isable to detect new attacks that have not previouslybeen seen.Moreover,the proposed detection schemeis based on no priori knowledge and then can beapplied to a wide range of different sensor networksfor a variety of routing attacks.

An emerging technology-wearable wireless sensor networks with applications in human health condition monitoring

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task,which involves anatomy,artificial intelligence,and sensing and computing networks.A wearable wireless sensor network(WWSN)turns into an emerging technology,which is capable of acquiring dynamic data related to a human body’s physiological conditions.The collected data can be applied to detect anomalies in a patient,so that he or she can receive an early alert about the adverse trend of the health condition,and doctors can take preventive actions accordingly.In this paper,a new WWSN for anomaly detections of health conditions has been proposed,system architecture and network has been discussed,the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN.The novelty of the detected model lies in its relevance to chronobiology.Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of the collected data,but also based on mutual relations of the data streams from different sources of sensors.A new algorithm is proposed to identify anomalies using the following procedure:(1)collected raw data is preprocessed and transferred into a set of directed graphs to represent the correlations of data streams from different sensors;(2)the directed graphs are further analyzed to identify dissimilarities and frequency patterns;(3)health conditions are quantified by a coefficient number,which depends on the identified dissimilarities and patterns.The effectiveness and reliability of the proposed WWSN has been validated by experiments in detecting health anomalies including tachycardia,arrhythmia and myocardial infarction.

Improved Data Discrimination in Wireless Sensor Networks

In Wireless Sensors Networks, the computational power and storage capacity is limited. Wireless Sensor Networks are operated in low power batteries, mostly not rechargeable. The amount of data processed is incremental in nature, due to deployment of various applications in Wireless Sensor Networks, thereby leading to high power consumption in the network. For effectively processing the data and reducing the power consumption the discrimination of noisy, redundant and outlier data has to be performed. In this paper we focus on data discrimination done at node and cluster level employing Data Mining Techniques. We propose an algorithm to collect data values both at node and cluster level and finding the principal component using PCA techniques and removing outliers resulting in error free data. Finally a comparison is made with the Statistical and Bucket-width outlier detection algorithm where the efficiency is improved to an extent.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

An Isolation Principle Based Distributed Anomaly Detection Method in Wireless Sensor Networks

Anomaly detection plays an important role in ensuring the data quality in wireless sensor networks(WSNs).The main objective of the paper is to design a light-weight and distributed algorithm to detect the data collected from WSNs effectively.This is achieved by proposing a distributed anomaly detection algorithm based on ensemble isolation principle.The new method offers distinctive advantages over the existing methods.Firstly,it does not require any distance or density measurement,which reduces computational burdens significantly.Secondly,considering the spatial correlation characteristic of node deployment in WSNs,local sub-detector is built in each sensor node,which is broadcasted simultaneously to neighbor sensor nodes.A global detector model is then constructed by using the local detector model and the neighbor detector model,which possesses a distributed nature and decreases communication burden.The experiment results on the labeled dataset confirm the effectiveness of the proposed method.

基于反向传播算法的网络安全态势感知仿真

随着互联网技术的广泛应用,网络信息传输的数量日益提升,网络安全态势感知的需求也逐渐增加。针对当前网络安全态势感知算法检测准确率率低,误差较大等问题,提出了基于反向传播算法的网络安全态势感知模型。首先采用大数据分析方法对入侵信息的特征按节点分解并进行分段分析;其次通过切换检测信道和空间节点的分布式融合方法对关键节点进行分析,提取入侵数据的特征;然后通过反向传播算法对基本的感知原理进行优化,以减小模型检测过程中的误差;最后基于信息融合的结果进行优化,通过模糊识别的方法对入侵行为进行检测,达到安全态势感知的效果。实验结果表明,相比其它算法,所提模型将平均绝对误差缩小近5%,预测精确度提升至少7%,有最佳的实验效果,推动了网络安全态势感知技术的发展和应用。

基于特征学习的无线传感网络入侵行为检测方法

传统的机器学习算法在无线传感网络入侵行为检测中应用效果不理想,Recall(召全率)和F1-score(召全率与精准率的调和平均数)较低,针对现行方法存在的不足和缺陷,提出基于特征学习的无线传感网络入侵行为检测方法。利用时间戳马尔可夫模型对无线传感网络进行分段,实现对网络入侵数据局部特征编码,利用深度学习网络,学习网络入侵行为特征,对网络数据标签进行数值化和归一化处理,根据入侵特征对网络行为进行分类,识别检测入侵行为,以此实现基于特征学习的无线传感网络入侵行为检测。经实验证明,设计方法Recal在95%以上,F1-score在90%以上,检测精度较高,在无线传感网络入侵行为检测方面具有良好的应用前景。

基于移动边缘计算和SSA算法的无线传感网络入侵检测方法

针对远距离传输和集中式处理方式带来的问题,为降低密集传感节点入侵任务检测的难度,减少时延开销和网络负荷,进一步消除隐私泄露风险,利用更靠近传感网络节点的移动边缘计算的明显优势,提出一种基于移动边缘计算和SSA算法的无线传感网络入侵检测方法。通过将有计算能力的边缘服务器部署于靠近传感节点的位置,采集无线传感网络数据并提取网络安全特征,大幅缩短采集时间。通过麻雀搜索算法调整节点采集中的搜索策略,结合梯度变化降维避免陷入最优,以普通节点位置和节点特征为依据,进行分布式入侵检测,实现无线传感网络的入侵检测。实验证明:本方法节点入侵检测平均准确率达98.52%,检测平均延时为1.67 ms,具有出色的应用性能。

基于双向循环生成对抗网络的无线传感网入侵检测方法

针对无线传感器网络(WSN)入侵检测方法在离散高维特征的不平衡数据集上检测精度低和泛化能力差的问题,提出一种基于双向循环生成对抗网络的WSN入侵检测方法 BiCirGAN。首先,引入对抗学习异常检测(ALAD)通过潜在空间合理地表示高维、离散的原始特征,提高对原始特征的可理解性。其次,采用双向循环对抗的结构确保真实空间和潜在空间双向循环的一致性,从而保证生成对抗网络(GAN)训练的稳定性,并提高异常检测的性能。同时,引入Wasserstein距离和谱归一化优化方法改进GAN的目标函数,以进一步解决GAN的模式崩坏与生成器缺乏多样性的问题。最后,由于入侵攻击数据的统计属性随时间以不可预见的方式变化,建立带有Dropout操作的全连接层网络对异常检测结果进行优化。实验结果表明,在KDD99、UNSW-NB15和WSN_DS数据集上,相较于AnoGAN、BiGAN、MAD-GAN以及ALAD方法,BiCirGAN在检测精确度上提高了3.9%~33.0%,且平均推断速度是ALAD方法的4.67倍。

基于FW-PSO的大区域无线传感网络流量异常检测算法

针对大区域无线传感网络流量特征维度较高,现有神经网络算法只能假设所有区域特征一致,导致一旦网络规模过大,会存在较大误差的问题,采用烟花算法优化粒子群算法的搜索能力,设计了一种烟花算法-粒子群优化(Fireworks Algorithm-Particle Swarm Optimization,FW-PSO)算法,提升了全局搜索能力及收敛速度,并将之应用于大区域无线传感网络流量异常检测。首先采用时间滑动窗口处理大区域无线传感网络数据流信息,通过正态Grubbs法则剔除数据中粗大误差值。然后引入主成分分析法对传感数据特征降维,分段提取有价值的特征。设计FW-PSO算法,提升粒子群算法的搜索能力,实现流量异常检测。实验结果表明,所提方法的无线传感网络流量异常检测率准确率平均为94.8%,训练及检测耗时平均值分别为3.75 s及0.25 s。

云框架超大规模资源处理下无线传感网络数据异常检测

为解决无线传感网络数据异常检测方法精度低、网络能量消耗大等问题,提出基于云框架的大规模无线传感网络数据异常检测方法。在云框架超大规模资源处理下,通过自适应过采样法消除冗余数据;运用数据集成、标准化等预处理数据样本,计算协方差矩阵,从高到低排列特征矢量贡献率;采用二进制粒子群方法优化初始森林中的孤立树,挑选初始森林中精度最高、差异度最大的孤立树组建最佳孤立森林,通过异常分数法检测数据特征异常情况,输出对应样本异常概率。实验部分对所提方法、图信号处理法和分层聚合法进行检测率、虚警率、接电能耗、耗时等指标对比,结果表明,所提方法有效降低了节点能耗,数据异常检测速率快、正确率高,具备优秀的适用性与可靠性。

IWSN中基于属性变化率的全局信任入侵检测

为提高工业无线传感器网络的入侵检测的精度和降低检测能耗,设计了基于属性变化率的全局信任入侵检测算法。通过属性变化率获取节点的全局信任值以检测攻击;通过人工蜂群算法选择最优簇头集以确保簇头的高可信度和降低网络整体的能耗;通过信任恢复机制恢复临时故障节点的信任,以降低虚警率。实验证明本方案具有较高的检测率和较低的虚警率,并能延长网络的生命周期。

基于演化博弈的无线传感器网络入侵检测研究

针对无线传感器网络易遭受内部节点攻击的问题,本文提出了一种基于演化博弈的无线传感器网络入侵检测方法。将传感器网络的攻防对抗映射到博弈过程中,建立恶意节点和簇头节点之间的攻防博弈模型,改进传统复制动态方程,使得簇头节点在演化博弈过程中考虑到其他节点的历史策略,预测恶意节点的攻击策略;同时将改进复制动态方程应用于入侵检测算法,提高算法响应时间。实验表明,与传统方法的复制动态方程相比,采用该算法使得演化博弈能够快速达到均衡,收敛速度相比传统方法提高了80%,保障了网络安全性的同时避免了传感器网络检测能量的消耗。