Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.     

基于序列模型的无线传感网入侵检测系统

随着物联网(IoT)的快速发展，越来越多的IoT节点设备被部署，但伴随而来的安全问题也不可忽视。IoT的网络层节点设备主要通过无线传感网进行通信，其相较于互联网更开放也更容易受到拒绝服务等网络攻击。针对无线传感网面临的网络层安全问题，提出了一种基于序列模型的网络入侵检测系统，对网络层入侵进行检测和报警，具有较高的识别率以及较低的误报率。另外，针对无线传感网节点设备面临的节点主机设备的安全问题，在考虑节点开销的基础上，提出了一种基于简单序列模型的主机入侵检测系统。实验结果表明，针对无线传感网的网络层以及主机层的两个入侵检测系统的准确率都达到了99%以上，误报率在1%左右，达到了工业需求，这两个系统可以全面有效地保护无线传感网安全。     

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL-CTD model with increased accuracy of 99.47%.     

Cyber Secure Framework for Smart Containers Based on Novel Hybrid DTLS Protocol

The Internet of Things (IoTs) is apace growing, billions of IoT devices are connected to the Internet which communicate and exchange data among each other. Applications of IoT can be found in many fields of engineering and sciences such as healthcare, traffic, agriculture, oil and gas industries, and logistics. In logistics, the products which are to be transported may be sensitive and perishable, and require controlled environment. Most of the commercially available logistic containers are not integrated with IoT devices to provide controlled environment parameters inside the container and to transmit data to a remote server. This necessitates the need for designing and fabricating IoT based smart containers. Due to constrained nature of IoT devices, these are prone to different cyber security attacks such as Denial of Service (DoS), Man in Middle (MITM) and Replay. Therefore, designing efficient cyber security framework are required for smart container. The Datagram Transport Layer Security (DTLS) Protocol has emerged as the de facto standard for securing communication in IoT devices. However, it is unable to minimize cyber security attacks such as Denial of Service and Distributed Denial of Service (DDoS) during the handshake process. The main contribution of this paper is to design a cyber secure framework by implementing novel hybrid DTLS protocol in smart container which can efficiently minimize the effects of cyber attacks during handshake process. The performance of our proposed framework is evaluated in terms of energy efficiency, handshake time, throughput and packet delivery ratio. Moreover, the proposed framework is tested in IoT based smart containers. The proposed framework decreases handshake time more than 9% and saves 11% of energy efficiency for transmission in compare of the standard DTLS, while increases packet delivery ratio and throughput by 83% and 87% respectively.     

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection from the cloud and IoT environments. The proposed MFO-RELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms. To accomplish this, the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format. Besides, the proposed models will receive the pre-processing data and carry out the classifier method. For boosting the efficiency of the proposed models, the MFO technique was utilized to it. The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset, and the outcomes are examined under distinct aspects.     

Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment. The presented MFO-RELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment. For accomplishing this, the MFO-RELM model pre-processes the actual IoT data into a meaningful format. In addition, the RELM model receives the pre-processed data and carries out the classification process. In order to boost the performance of the RELM model, the MFO algorithm has been employed to it. The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.     

Optimized Tuning of LOADng Routing Protocol Parameters for IoT

Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things (IoT), making it possible to accomplish tasks with less human interaction. However, it faces many problems, including lower capacity links, energy utilization, enhancement of resources and limited resources due to its openness, heterogeneity, limited resources and extensiveness. It is challenging to route packets in such a constrained environment. In an IoT network constrained by limited resources, minimal routing control overhead is required without packet loss. Such constrained environments can be improved through the optimal routing protocol. It is challenging to route packets in such a constrained environment. Thus, this work is motivated to present an efficient routing protocol for enhancing the lifetime of the IoT network. Lightweight On-demand Ad hoc Distance-vector Routing Protocol—Next Generation (LOADng) protocol is an extended version of the Ad Hoc On-Demand Distance Vector (AODV) protocol. Unlike AODV, LOADng is a lighter version that forbids the intermediate nodes on the route to send a route reply (RREP) for the route request (RREQ), which originated from the source. A resource-constrained IoT network demands minimal routing control overhead and faster packet delivery. So, in this paper, the parameters of the LOADng routing protocol are optimized using the black widow optimization (BWO) algorithm to reduce the control overhead and delay. Furthermore, the performance of the proposed model is analyzed with the default LOADng in terms of delay, delivery ratio and overhead. Obtained results show that the LOADng-BWO protocol outperforms the conventional LOADng protocol.     

Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices. This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification (SCADL-RWDC) method in an IoT environment. In the presented SCADL-RWDC technique, the major intention exists in recognizing and classifying ransomware attacks in the IoT platform. The SCADL-RWDC technique uses the SCA feature selection (SCA-FS) model to improve the detection rate. Besides, the SCADL-RWDC technique exploits the hybrid grey wolf optimizer (HGWO) with a gated recurrent unit (GRU) model for ransomware classification. A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique. The comparison study reported the enhancement of the SCADL-RWDC technique over other models.     

A lightweight attribute-based encryption scheme for the Internet of Things

Internet of Things (IoT) is an emerging network paradigm, which realizes the interconnections among the ubiquitous things and is the foundation of smart society. Since IoT are always related to user’s daily life or work, the privacy and security are of great importance. The pervasive, complex and heterogeneous properties of IoT make its security issues very challenging. In addition, the large number of resources-constraint nodes makes a rigid lightweight requirement for IoT security mechanisms. Presently, the attribute-based encryption (ABE) is a popular solution to achieve secure data transmission, storage and sharing in the distributed environment such as IoT. However, the existing ABE schemes are based on expensive bilinear pairing, which make them not suitable for the resources-constraint IoT applications. In this paper, a lightweight no-pairing ABE scheme based on elliptic curve cryptography (ECC) is proposed to address the security and privacy issues in IoT. The security of the proposed scheme is based on the ECDDH assumption instead of bilinear Diffie–Hellman assumption, and is proved in the attribute based selective-set model. By uniformly determining the criteria and defining the metrics for measuring the communication overhead and computational overhead, the comparison analyses with the existing ABE schemes are made in detail. The results show that the proposed scheme has improved execution efficiency and low communication costs. In addition, the limitations and the improving directions of it are also discussed in detail.     

Design,code generation and simulation of IoT environments with mobility devices by using model-driven development: SimulateIoT-Mobile

Systems based on the Internet of Things (IoT) are continuously growing in many areas such as smart cities, home environments, buildings, agriculture, industry, etc. Device mobility is one of the key aspects of these IoT systems, but managing it could be a challenge. Mobility exposes the IoT environment or Industrial IoT (IIoT) to situations such as packet loss, increased delay or jitter, dynamism in the network topology, new security threats, etc. In addition, there is no standard for mobility management for the most commonly used IoT protocols, such as MQTT or CoAP. Consequently, managing IoT mobility is a hard, error-prone and tedious task. However, increasing the abstraction level from which the IoT systems are designed helps to tackle the underlying technology complexity. In this regard, Model-driven development approaches can help to both reduce the IoT application time to market and tackle the technological complexity to develop IoT applications. In this paper, a Domain-Specific Language based on SimulateIoT is proposed for the design, code generation and simulation of IoT systems with mobility management for the MQTT protocol. The IoT systems generated integrate the sensors, actuators, fog nodes, cloud nodes and the architecture that supports mobility, which are deployed as microservices on Docker containers and composed suitability. Finally, two case studies focused on animal tracking and a Personal mobility device (PMD) based on bicycles IoT systems are presented to show the IoT solutions deployed.     

Energy Efficient Cluster Head Selection for Internet of Things

Recently, Internet of Things (IoT) devices are highly utilized in diverse fields such as environmental monitoring, industries, and smart home, among others. Under such instances, a cluster head is selected among the diverse IoT devices of wireless sensor network (WSN) based IoT network to maintain a reliable network with efficient data transmission. This article proposed a novel method with the combination of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. This method considers the distance, energy, delay, load, and temperature of the IoT devices during the operation of the cluster head selection process. Furthermore, the performance of the proposed method is analyzed by comparing with conventional methods such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and GSO algorithms. The analysis related to the existence of the number of alive nodes, convergence estimation, and performance in terms of normalized energy, load, and temperature of the IoT devices are determined. Thus the analysis of our implementation reveals the superior performance of the proposed method.     

Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

Recently, energy harvesting wireless sensor networks (EHWSN) have increased significant attention among research communities. By harvesting energy from the neighboring environment, the sensors in EHWSN resolve the energy constraint problem and offers lengthened network lifetime. Clustering is one of the proficient ways for accomplishing even improved lifetime in EHWSN. The clustering process intends to appropriately elect the cluster heads (CHs) and construct clusters. Though several models are available in the literature, it is still needed to accomplish energy efficiency and security in EHWSN. In this view, this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks (CROC-SEHWSN) model. The presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in EHWSN. The CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization (RO) algorithm. Besides, the CROC-SEHWSN model derives a fitness function (FF) involving seven distinct parameters connected to WSN. To accomplish security, trust factor and link quality metrics are considered in the FF. The design of RO algorithm for secure clustering process shows the novelty of the work. In order to demonstrate the enhanced performance of the CROC-SEHWSN approach, a wide range of simulations are carried out and the outcomes are inspected in distinct aspects. The experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.     

Intrusion Detection System for Big Data Analytics in IoT Environment

In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.     

区块链技术在矿山物联网中的应用研究

针对矿山物联网中数据传输和存储过程中存在易丢失和被篡改等问题,将区块链技术应用于矿山物联网的数据传输与存储中。构建了以数据层、传输层、共识层为核心的矿山私有区块链架构;设计了基于共识模块和数据区块的矿山物联网数据传输与存储防护方案;应用实用拜占庭容错(PBFT)算法设计了数据共识过程,通过在分布式结构化P2P网络每2个节点间设置共识模块并优化P2P协议,实现了矿山数据安全性共识。测试结果表明,私有区块链的应用保证了矿山物联网数据的准确传输和可靠存储。     

Impact of Transmission Power Control in multi-hop networks

Many Transmission Power Control (TPC) algorithms have been proposed in the past, yet the conditions under which they are evaluated do not always reflect typical Internet-of-Things (IoT) scenarios. IoT networks consist of several source nodes transmitting data simultaneously, possibly along multiple hops. Link failures are highly frequent, causing the TPC algorithm to kick-in quite often. To this end, in this paper we study the impact that frequent TPC actions have across different layers. Our study shows how one node’s decision to scale its transmission power can affect the performance of both routing and MAC layers of multiple other nodes in the network, generating cascading packet retransmissions and forcing far too many nodes to consume more energy. We find that crucial objectives of TPC such as conserving energy and increasing network capacity are severely undermined in multi-hop networks.     

Energy-efficient and Secure Wireless Communication for Telemedicine in IoT

The Internet of Things (IoT) represents a radical shifting paradigm for technological innovations as it can play critical roles in cyberspace applications in various sectors, such as security, monitoring, medical, and environmental sectors, and also in control and industrial applications. The IoT in E-medicine unleashed the design space for new technologies to give instant treatment to patients while also monitoring and tracking health conditions. This research presents a system-level architecture approach for IoT energy efficiency and security. The proposed architecture includes functional components that provide privacy management and system security. Components in the security function group provide secure communications through Multi-Authority Ciphertext-Policy Attributes-Based Encryption (MA-CPABE). Because MA-CPABE is assigned to unlimited devices, presuming that the devices are reliable, the user encodes data with Advanced Encryption Standard (AES) and protects the ABE approach using the solutions of symmetric key. The Johnson’s algorithm with a new computation measure is used to increase network lifetime since an individual sensor node with limited energy represents the inevitable constraints for the broad usage of wireless sensor networks. The optimal route from a source to destination turns out as the cornerstone for longevity of network and its sustainability. To reduce the energy consumption of networks, the evaluation measures consider the node’s residual energy, the number of neighbors, their distance, and the link dependability. The experiment results demonstrate that the proposed model increases network life by about 12.25% (27.73%) compared to Floyd–Warshall’s, Bellman–Ford’s, and Dijkstra’s algorithms, lowering consumption of energy by eliminating the necessity for re-routing the message as a result of connection failure.     

面向5G的命名数据网络物联网研究综述

在5G时代,大规模物联网应用对网络架构提出了异构性、可扩展性、移动性和安全性四大挑战。基于TCP/IP的网络架构存在IP标识与位置绑定的二义重载问题,难以应对这四大挑战。命名数据网络(Named Data Networking,NDN)将内容作为第一语义,具有网络层和应用层逻辑拓扑一致性。NDN对这四大挑战的支持分别体现在:网络层命名屏蔽了底层异构细节,端到端解耦及网络层缓存使得NDN天然支持多对多通信和广播,消费者驱动的通信模式为消费者移动性提供原生支持,面向内容的内生安全更轻量可信。文中总结了基于NDN构建物联网亟待解决的问题,并对NDN与边缘计算、软件定义网络和区块链结合来构建边缘存储和计算模型、集中式与分布式结合的控制模型、分布式安全模型提出了展望。     

Security challenges in internet of things: Distributed denial of service attack detection using support vector machine-based expert systems

The rapid development of internet of things (IoT) is to be the next generation of the IoT devices are a simple target for attackers due to the lack of security. Attackers can easily hack the IoT devices that can be used to form botnets, which can be used to launch distributed denial of service (DDoS) attack against networks. Botnets are the most dangerous threat to the security systems. Software-defined networking (SDN) is one of the developing filed, which introduce the capacity of dynamic program to the network. Use the flexibility and multidimensional characteristics of SDN used to prevent DDoS attacks. The DDoS attack is the major attack to the network, which makes the entire network down, so that normal users might not avail the services from the server. In this article, we proposed the DDoS attack detection model based on SDN environment by combining support vector machine classification algorithm is used to collect flow table values in sampling time periods. From the flow table values, the five-tuple characteristic values extracted and based on it the DDoS attack can be detected. Based on the experimental results, we found the average accuracy rate is 96.23% with a normal amount of traffic flow. Proposed research offers a better DDoS detection rate on SDN.     

一种物联网环境下的分布式异常流量检测方案

物联网终端设备数量的急剧增加带来了诸多安全隐患,如何高效地进行异常流量检测成为物联网安全研究中的一项重要任务。现有检测方法存在计算开销大的问题,且不能显式地捕捉流量数据中的关系和结构,难以应对新型网络攻击。考虑网络结构和节点设备之间的复杂通信模式,提出一种基于图神经网络的分布式异常流量检测方案。结合物联网环境对卷积神经网络进行改进,识别节点之间的复杂关系,同时在物联网设备、转发器和雾节点上设计并部署分布式检测单元,通过分布式检测架构实现本地化的异常流量检测,从而降低检测延迟和时间开销。在此基础上,引入注意力模块强化对关键特征的提取,增强模型的可解释性,进一步提高检测精度。在公开数据集CTU-13上的实验结果表明,该方案准确率和AUC值达到99.93%和0.99,只需9.26 s即可完成检测,且带宽消耗仅为845 kb/s。     

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things (IoT) devices. These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers. Cyber-attackers take advantage of such gadgets’ vulnerabilities through various attacks such as injection and Distributed Denial of Service (DDoS) attacks. In this background, Intrusion Detection (ID) is the only way to identify the attacks and mitigate their damage. The recent advancements in Machine Learning (ML) and Deep Learning (DL) models are useful in effectively classifying cyber-attacks. The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition (COADL-FDIAR) model for the IoT environment. The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment. To accomplish this, the COADL-FDIAR model initially pre-processes the input data and selects the features with the help of the Chi-square test. To detect and classify false data injection attacks, the Stacked Long Short-Term Memory (SLSTM) model is exploited in this study. Finally, the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency. The proposed COADL-FDIAR model was experimentally validated using a standard dataset, and the outcomes were scrutinized under distinct aspects. The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.