A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

In the last decade,IoT has been widely used in smart cities,autonomous driving and Industry 4.0,which lead to improve efficiency,reliability,security and economic benefits.However,with the rapid development of new technologies,such as cognitive communication,cloud computing,quantum computing and big data,the IoT security is being confronted with a series of new threats and challenges.IoT device identification via Radio Frequency Fingerprinting(RFF)extracting from radio signals is a physical-layer method for IoT security.In physical-layer,RFF is a unique characteristic of IoT device themselves,which can difficultly be tampered.Just as people’s unique fingerprinting,different IoT devices exhibit different RFF which can be used for identification and authentication.In this paper,the structure of IoT device identification is proposed,the key technologies such as signal detection,RFF extraction,and classification model is discussed.Especially,based on the random forest and Dempster-Shafer evidence algorithm,a novel ensemble learning algorithm is proposed.Through theoretical modeling and experimental verification,the reliability and differentiability of RFF are extracted and verified,the classification result is shown under the real IoT device environments.

Intelligent Dynamic Heterogeneous Redundancy Architecture for IoT Systems

The conventional dynamic heterogeneous redundancy(DHR)architecture suffers from the security threats caused by the stability differences and similar vulnerabilities among the executors.To overcome these challenges,we propose an intelligent DHR architecture,which is more feasible by intelligently combining the random distribution based dynamic scheduling algorithm(RD-DS)and information weight and heterogeneity based arbitrament(IWHA)algorithm.In the proposed architecture,the random distribution function and information weight are employed to achieve the optimal selection of executors in the process of RD-DS,which avoids the case that some executors fail to be selected due to their stability difference in the conventional DHR architecture.Then,through introducing the heterogeneity to restrict the information weights in the procedure of the IWHA,the proposed architecture solves the common mode escape issue caused by the existence of multiple identical error output results of similar vulnerabilities.The experimental results characterize that the proposed architecture outperforms in heterogeneity,scheduling times,security,and stability over the conventional DHR architecture under the same conditions.

A Lightweight IoT Malware Detection and Family Classification Method

A lightweight malware detection and family classification system for the Internet of Things (IoT) was designed to solve the difficulty of deploying defense models caused by the limited computing and storage resources of IoT devices. By training complex models with IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Additionally, the multi-teacher knowledge distillation method is employed to train KD-LMDNet, which focuses on classifying malware families. The results indicate that the model’s identification speed surpasses that of traditional methods by 23.68%. Moreover, the accuracy achieved on the Malimg dataset for family classification is an impressive 99.07%. Furthermore, with a model size of only 0.45M, it appears to be well-suited for the IoT environment. By training complex models using IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Thus, the presented approach can address the challenges associated with malware detection and family classification in IoT devices.

Fast and Efficient Security Scheme for Blockchain-Based IoT Networks

Internet of Things(IoT)has become widely used nowadays and tremendous increase in the number of users raises its security requirements as well.The constraints on resources such as low computational capabilities and power requirements demand lightweight cryptosystems.Conventional algorithms are not applicable in IoT network communications because of the constraints mentioned above.In this work,a novel and efficient scheme for providing security in IoT applications is introduced.The scheme proposes how security can be enhanced in a distributed IoT application by providing multilevel protection and dynamic key generation in the data uploading and transfer phases.Existing works rely on a single key for communication between sensing device and the attached gateway node.In proposed scheme,this session key is updated after each session and this is done by applying principles of cellular automata.The proposed system provides multilevel security by using incomparable benefits of blockchain,dynamic key and random number generation based on cellular automata.The same was implemented and tested with the widely known security protocol verification tool called Automated Validation of Internet Security Protocols and Applications(AVISPA).Results show that the scheme is secure against various attacks.The proposed scheme has been compared with related schemes and the result analysis shows that the new scheme is fast and efficient also.

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)network.The followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem.Here,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency.Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

Blockchain-Based Decentralized Authentication Model for IoT-Based E-Learning and Educational Environments

In recent times,technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners.Integrating the Internet of Things(IoT)into education can facilitate the teaching and learning process and expand the context in which students learn.Nevertheless,learning data is very sensitive and must be protected when transmitted over the network or stored in data centers.Moreover,the identity and the authenticity of interacting students,instructors,and staff need to be verified to mitigate the impact of attacks.However,most of the current security and authentication schemes are centralized,relying on trusted third-party cloud servers,to facilitate continuous secure communication.In addition,most of these schemes are resourceintensive;thus,security and efficiency issues arise when heterogeneous and resource-limited IoT devices are being used.In this paper,we propose a blockchain-based architecture that accurately identifies and authenticates learners and their IoT devices in a decentralized manner and prevents the unauthorized modification of stored learning records in a distributed university network.It allows students and instructors to easily migrate to and join multiple universities within the network using their identity without the need for user re-authentication.The proposed architecture was tested using a simulation tool,and measured to evaluate its performance.The simulation results demonstrate the ability of the proposed architecture to significantly increase the throughput of learning transactions(40%),reduce the communication overhead and response time(26%),improve authentication efficiency(27%),and reduce the IoT power consumption(35%)compared to the centralized authentication mechanisms.In addition,the security analysis proves the effectiveness of the proposed architecture in resisting various attacks and ensuring the security requirements of learning data in the university network.

IoT Security Situational Awareness Based on Q-Learning and Bayesian Game

IoT security is very crucial to IoT applications,and security situational awareness can assess the overall security status of the IoT.Traditional situational awareness methods only consider the unilateral impact of attack or defense,but lackconsideration of joint actions by both parties.Applying gametheory to security situational awareness can measure the impact of the opposition and interdependence of the offensive and defensive parties.This paper proposes an IoT security situational awareness method based on Q-Learning and Bayesian game.Through Q-Learning update,the long-term benefits of action strategies in specific states were obtained,and static Bayesian game methods were used to solve the Bayesian Nash Equilibrium of participants of different types.The proposed method comprehensively considers offensive and defensive actions,obtains optimal defense decisions in multi-state and multi-type situations,and evaluates security situation.Experimental results prove the effectiveness of this method.

An Anti-Physical Attack Scheme of ARX Lightweight Algorithms for IoT Applications

The lightweight encryption algorithm based on Add-Rotation-XOR(ARX)operation has attracted much attention due to its high software affinity and fast operation speed.However,lacking an effective defense scheme for physical attacks limits the applications of the ARX algorithm.The critical challenge is how to weaken the direct dependence between the physical information and the secret key of the algorithm at a low cost.This study attempts to explore how to improve its physical security in practical application scenarios by analyzing the masking countermeasures of ARX algorithms and the leakage causes.Firstly,we specify a hierarchical security framework by quantitatively evaluating the indicators based on side-channel attacks.Then,optimize the masking algorithm to achieve a trade-off balance by leveraging the software-based local masking strategies and non-full-round masking strategies.Finally,refactor the assembly instruction to improve the leaks by exploring the leakage cause at assembly instruction.To illustrate the feasibility of the proposed scheme,we further conducted a case study by designing a software-based masking method for Chaskey.The experimental results show that the proposed method can effectively weaken the impact of physical attacks.

Enhanced IDS with Deep Learning for IoT-Based Smart Cities Security

Cyberattacks against highly integrated Internet of Things (IoT) servers, apps, and telecoms infrastructure are rapidly increasing when issues produced by IoT networks go unnoticed for an extended period. IoT interface attacks must be evaluated in real-time for effective safety and security measures. This study implements a smart intrusion detection system (IDS) designed for IoT threats, and interoperability with IoT connectivity standards is offered by the identity solution. An IDS is a common type of network security technology that has recently received increasing interest in the research community. The system has already piqued the curiosity of scientific and industrial communities to identify intrusions. Several IDSs based on machine learning (ML) and deep learning (DL) have been proposed. This study introduces IDS-SIoDL, a novel IDS for IoT-based smart cities that integrates long shortterm memory (LSTM) and feature engineering. This model is tested using tensor processing unit (TPU) on the enhanced BoT-IoT, Edge-IIoT, and NSL-KDD datasets. Compared with current IDSs, the obtained results provide good assessment features, such as accuracy, recall, and precision, with approximately 0.9990 recording time and calculating times of approximately 600 and 6 ms for training and classification, respectively.

Two-Stage High-Efficiency Encryption Key Update Scheme for LoRaWAN Based IoT Environment

Secure data communication is an essential requirement for an Internet of Things(IoT)system.Especially in Industrial Internet of Things(IIoT)and Internet of Medical Things(IoMT)systems,when important data are hacked,it may induce property loss or life hazard.Even though many IoTrelated communication protocols are equipped with secure policies,they still have some security weaknesses in their IoT systems.LoRaWAN is one of the low power wide-area network protocols,and it adopts Advanced Encryption Standard(AES)to provide message integrity and confidentiality.However,LoRaWAN’s encryption key update scheme can be further improved.In this paper,a Two-stage High-efficiency LoRaWAN encryption key Update Scheme(THUS for short)is proposed to update LoRaWAN’s root keys and session keys in a secure and efficient way.The THUS consists of two stages,i.e.,the Root Key Update(RKU)stage and the Session Key Update(SKU)stage,and with different update frequencies,the RKU and SKU provide higher security level than the normal LoRaWAN specification does.A modified AES encryption/decryption process is also utilized in the THUS for enhancing the security of the THUS.The security analyses demonstrate that the THUS not only protects important parameter during key update stages,but also satisfies confidentiality,integrity,and mutual authentication.Moreover,The THUS can further resist replay and eavesdropping attacks.

Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

Security Analysis of Smart Speaker: Security Attacks and Mitigation

The speech recognition technology has been increasingly common in our lives.Recently,a number of commercial smart speakers containing the personal assistant system using speech recognition came out.While the smart speaker vendors have been concerned about the intelligence and the convenience of their assistants,but there have been little mentions of the smart speakers in security aspects.As the smart speakers are becoming the hub for home automation,its security vulnerabilities can cause critical problems.In this paper,we categorize attack vectors and classify them into hardware-based,network-based,and software-based.With the attack vectors,we describe the detail attack scenarios and show the result of tests on several commercial smart speakers.In addition,we suggest guidelines to mitigate various attacks against smart speaker ecosystem.

Multi-Attack Intrusion Detection System for Software-Defined Internet of Things Network

Currently,the Internet of Things(IoT)is revolutionizing communi-cation technology by facilitating the sharing of information between different physical devices connected to a network.To improve control,customization,flexibility,and reduce network maintenance costs,a new Software-Defined Network(SDN)technology must be used in this infrastructure.Despite the various advantages of combining SDN and IoT,this environment is more vulnerable to various attacks due to the centralization of control.Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service(DDoS)attacks,but they often lack mechanisms to mitigate their severity.This paper proposes a Multi-Attack Intrusion Detection System(MAIDS)for Software-Defined IoT Networks(SDN-IoT).The proposed scheme uses two machine-learning algorithms to improve detection efficiency and provide a mechanism to prevent false alarms.First,a comparative analysis of the most commonly used machine-learning algorithms to secure the SDN was performed on two datasets:the Network Security Laboratory Knowledge Discovery in Databases(NSL-KDD)and the Canadian Institute for Cyberse-curity Intrusion Detection Systems(CICIDS2017),to select the most suitable algorithms for the proposed scheme and for securing SDN-IoT systems.The algorithms evaluated include Extreme Gradient Boosting(XGBoost),K-Nearest Neighbor(KNN),Random Forest(RF),Support Vector Machine(SVM),and Logistic Regression(LR).Second,an algorithm for selecting the best dataset for machine learning in Intrusion Detection Systems(IDS)was developed to enable effective comparison between the datasets used in the development of the security scheme.The results showed that XGBoost and RF are the best algorithms to ensure the security of SDN-IoT and to be applied in the proposed security system,with average accuracies of 99.88%and 99.89%,respectively.Furthermore,the proposed security scheme reduced the false alarm rate by 33.23%,which is a significant improvement over prevalent schemes.Finally,tests of the algorithm for dataset selection showed that the rates of false positives and false negatives were reduced when the XGBoost and RF algorithms were trained on the CICIDS2017 dataset,making it the best for IDS compared to the NSL-KDD dataset.

Malicious Traffic Compression and Classification Technique for Secure Internet of Things

With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to the IoT environment is challenging.Therefore,this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment.The first method,compressed sensing and learning(CSL),compresses an event log in a bitmap format to quickly detect attacks.Then,the attack log is detected using a machine-learning classification model.The second method,precise re-learning after CSL(Ra-CSL),comprises a two-step training.It uses CSL as the 1st step analyzer,and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step analyzer.In the experiment,the bitmap rule was set based on the boundary value,which was 99.6%true positive on average for the attack and benign data found by analyzing the training data.Experimental results showed that the CSL was effective in reducing the training and detection time,and Ra-CSL was effective in increasing the detection rate.According to the experimental results,the data compression technique reduced the memory size by up to 20%and the training and detection times by 67%when compared with the conventional technique.In addition,the proposed technique improves the detection accuracy;the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.

A Novel Attack on Complex APUFs Using the Evolutionary Deep Convolutional Neural Network

As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.

Arm PSA-Certified IoT Chip Security: A Case Study

With the large scale adoption of Internet of Things(IoT)applications in people’s lives and industrial manufacturing processes,IoT security has become an important problem today.IoT security significantly relies on the security of the underlying hardware chip,which often contains critical information,such as encryption key.To understand existing IoT chip security,this study analyzes the security of an IoT security chip that has obtained an Arm Platform Security Architecture(PSA)Level 2 certification.Our analysis shows that the chip leaks part of the encryption key and presents a considerable security risk.Specifically,we use commodity equipment to collect electromagnetic traces of the chip.Using a statistical T-test,we find that the target chip has physical leakage during the AES encryption process.We further use correlation analysis to locate the detailed encryption interval in the collected electromagnetic trace for the Advanced Encryption Standard(AES)encryption operation.On the basis of the intermediate value correlation analysis,we recover half of the 16-byte AES encryption key.We repeat the process for three different tests;in all the tests,we obtain the same result,and we recover around 8 bytes of the 16-byte AES encryption key.Therefore,experimental results indicate that despite the Arm PSA Level 2 certification,the target security chip still suffers from physical leakage.Upper layer application developers should impose strong security mechanisms in addition to those of the chip itself to ensure IoT application security.

Construction and Optimization of TRNG Based Substitution Boxes for Block Encryption Algorithms

Internet of Things is an ecosystem of interconnected devices that are accessible through the internet.The recent research focuses on adding more smartness and intelligence to these edge devices.This makes them susceptible to various kinds of security threats.These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field.In this regard,block cipher has been one of the most reliable options through which data security is accomplished.The strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution Boxes.For the design of S-boxes mainly algebraic and chaos-based techniques are used but researchers also found various weaknesses in these techniques.On the other side,literature endorse the true random numbers for information security due to the reason that,true random numbers are purely non-deterministic.In this paper firstly a natural dynamical phenomenon is utilized for the generation of true random numbers based S-boxes.Secondly,a systematic literature review was conducted to know which metaheuristic optimization technique is highly adopted in the current decade for the optimization of S-boxes.Based on the outcome of Systematic Literature Review(SLR),genetic algorithm is chosen for the optimization of s-boxes.The results of our method validate that the proposed dynamic S-boxes are effective for the block ciphers.Moreover,our results showed that the proposed substitution boxes achieve better cryptographic strength as compared with state-of-the-art techniques.

Intrusion Detection Method of Internet of Things Based on Multi GBDT Feature Dimensionality Reduction and Hierarchical Traffic Detection

The rapid development of Internet of Things(IoT)technology has brought great convenience to people’s life.However,the security protection capability of IoT is weak and vulnerable.Therefore,more protection needs to be done for the security of IoT.The paper proposes an intrusion detection method for IoT based on multi GBDT feature reduction and hierarchical traffic detection model.Firstly,GBDT is used to filter the features of IoT traffic data sets BoT-IoT and UNSW-NB15 to reduce the traffic feature dimension.At the same time,in order to improve the reliability of feature filtering,this paper constructs multiple GBDT models to filter the features of multiple sub data sets,and comprehensively evaluates the filtered features to find out the best alternative features.Then,two neural networks are trained with the two data sets after dimensionality reduction,and the traffic will be detected with the trained neural network.In order to improve the efficiency of traffic detection,this paper proposes a hierarchical traffic detection model,which can reduce the computational cost and time cost of detection process.Experiments show that the multi GBDT dimensionality reduction method can obtain better features than the traditional PCA dimensionality reduction method.Besides,the use of dual data sets improves the comprehensiveness of the IoT intrusion detection system,which can detect more types of attacks,and the hierarchical traffic model improves the detection efficiency of the system.

On the Security of Smart Home Systems:A Survey

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the fastest-growing.However,the rapid development of the smart home has also made smart home systems a target for attackers.Recently,researchers have made many efforts to investigate and enhance the security of smart home systems.Toward a more secure smart home ecosystem,we present a detailed literature review on the security of smart home systems.Specifically,we categorize smart home systems’security issues into the platform,device,and communication issues.After exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security standards.Finally,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

A critical review of intrusion detection systems in the internet of things:techniques,deployment strategy,validation strategy,attacks,public datasets and challenges

The Internet of Things(IoT)has been rapidly evolving towards making a greater impact on everyday life to large industrial systems.Unfortunately,this has attracted the attention of cybercriminals who made IoT a target of malicious activities,opening the door to a possible attack on the end nodes.To this end,Numerous IoT intrusion detection Systems(IDS)have been proposed in the literature to tackle attacks on the IoT ecosystem,which can be broadly classified based on detection technique,validation strategy,and deployment strategy.This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques,deployment Strategy,validation strategy and datasets that are commonly applied for building IDS.We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT.It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure.These purposes help IoT security researchers by uniting,contrasting,and compiling scattered research efforts.Consequently,we provide a unique IoT IDS taxonomy,which sheds light on IoT IDS techniques,their advantages and disadvantages,IoT attacks that exploit IoT communication systems,corresponding advanced IDS and detection capabilities to detect IoT attacks.