Detection In Internet Of Things Research Articles

Malicious Traffic Flow Detection in IOT Using Ml Based Algorithms

Identifying the malicious traffic flows in Internet of things (IOT) is very important to monitor and avoid unwanted errors or the unwanted flows in the network. So, for a security to this network various machine learning algorithms (ML) has been introduced by various analyst to avoid this flow of error in the network. But, owing to the unsuitable selection of features, the ML models which introduced previously suffer from misclassify errors. So, there arises a need to study the problem of feature selection more depth to predict the accurate traffic flow observation in the network. To overcome this problem, a new structure in machine learning (ML) is introduced. So, for thisa novel features selection metric CorrAUC is suggested. So, based on this metric approach, a new feature selection algorithm CorrAUC is develop and design, it is based on wrapper technique to get features accurately by filtering to predict flow of traffic is suggested. Then, we applied multicriteria decision method called VIKOR which is used for validating the features selected for recognition the flow of traffic errors in the network. We estimate our approach by using the NSL-KDD dataset and three different ML algorithms.

AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

Reliable critical nodes detection for Internet of Things (IoT)

The 3D critical node (C-N) detection can play a vital role in algorithm development of security, surveillance, monitoring, topology detection, and situation-aware emergency navigation for the Internet of Things (IoT). However, 3D C-N detection problem in IoT raises some issues and also introduces new challenges. The existing state of the art in 3D C-N detection shows that rely on prior known anchor node, known coordinate, embedding of the 3D situation on a 2D geometrical structure like circles and presence of unreliable node and ignores the energy constraint in Low Power and Lossy Networks IoT. In this paper, we present a practical, distributed, and energy-efficient algorithm for reliable 3D C-N detection. The goal of the proposed mechanism is twofold, firstly a 3D critical nodes (C-N) detection algorithm is proposed which uses only Received Signal Strength Indicator information of neighbor. Secondly, a correlation-based algorithm for the reliability approach is proposed to increases the node resilience against malicious IoT nodes. The complexity of our proposed algorithms has a time complexity of $$\mathcal {O}(\log (N))$$ and computation cost $$\mathcal {O}(\delta (\log N))$$ where N is the number of nodes in networks, and $$\delta $$ is the total number of forward and the backward message from an individual node. To validate our work, we implemented our proposed approach with the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) based IoT routing protocol compare it with RPL and cryptographic approach Version Number and Rank Authentication (VeRa). The result shows that the proposed approach can detect 10–15% more C-N nodes. Result also shows that our proposed algorithm has better PDR than RPL based approach by 12% and less than VeRa (cryptographic approach) by 8% however our proposed approach consumes almost 50% less power than the VeRa.

Analysis of deep learning models for network anomaly detection in Internet of Things

Introduction: The article discusses the problem of choosing deep learning models for detecting anomalies in Internet of Things (IoT) network traffic. This problem is associated with the necessity to analyze a large number of security events in order to identify the abnormal behavior of smart devices. A powerful technology for analyzing such data is machine learning and, in particular, deep learning. Purpose: Development of recommendations for the selection of deep learning models for anomaly detection in IoT network traffic. Results: The main results of the research are comparative analysis of deep learning models, and recommendations on the use of deep learning models for anomaly detection in IoT network traffic. Multilayer perceptron, convolutional neural network, recurrent neural network, long short-term memory, gated recurrent units, and combined convolutional-recurrent neural network were considered the basic deep learning models. Additionally, the authors analyzed the following traditional machine learning models: naive Bayesian classifier, support vector machines, logistic regression, k-nearest neighbors, boosting, and random forest. The following metrics were used as indicators of anomaly detection efficiency: accuracy, precision, recall, and F-measure, as well as the time spent on training the model. The constructed models demonstrated a higher accuracy rate for anomaly detection in large heterogeneous traffic typical for IoT, as compared to conventional machine learning methods. The authors found that with an increase in the number of neural network layers, the completeness of detecting anomalous connections rises. This has a positive effect on the recognition of unknown anomalies, but increases the number of false positives. In some cases, preparing traditional machine learning models takes less time. This is due to the fact that the application of deep learning methods requires more resources and computing power. Practical relevance: The results obtained can be used to build systems for network anomaly detection in Internet of Things traffic.

Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications

Presently, a green Internet of Things (IoT) based energy aware network plays a significant part in the sensing technology. The development of IoT has a major impact on several application areas such as healthcare, smart city, transportation, etc. The exponential rise in the sensor nodes might result in enhanced energy dissipation. So, the minimization of environmental impact in green media networks is a challenging issue for both researchers and business people. Energy efficiency and security remain crucial in the design of IoT applications. This paper presents a new green energy-efficient routing with DL based anomaly detection (GEER-DLAD) technique for IoT applications. The presented model enables IoT devices to utilize energy effectively in such a way as to increase the network span. The GEER-DLAD technique performs error lossy compression (ELC) technique to lessen the quantity of data communication over the network. In addition, the moth flame swarm optimization (MSO) algorithm is applied for the optimal selection of routes in the network. Besides, DLAD process takes place via the recurrent neural network-long short term memory (RNN-LSTM) model to detect anomalies in the IoT communication networks. A detailed experimental validation process is carried out and the results ensured the betterment of the GEER-DLAD model in terms of energy efficiency and detection performance.

Harmony search Hawks optimization-based Deep reinforcement learning for intrusion detection in IoT using nonnegative matrix factorization

With the advent of the Internet of Things (IoT), the security of the network layer in IoT is getting more attention in recent decades. Various intrusion detection methods were developed in the existing research works, but the capability to detect malicious and intrusion activities in the complex Internet environment poses a challenging task in IoT. Hence, an effective and optimal intrusion detection mechanism, named Harmony Search Hawks Optimization-based Deep Reinforcement Learning (HSHO-based Deep RL), is proposed in this research to detect malicious network activities. The proposed Harmony Search Hawks Optimization (HSHO) algorithm is designed by integrating Harmony Search (HS) with the Harris Hawks Optimization (HHO) algorithm. However, the optimal detection result that is effectively achieved through the fitness measures such that the minimum fitness value is only declared as the optimal solution. The Deep Reinforcement Learning (Deep RL) classifier effectively detects the malicious or intruder behaviors and generates a satisfactory result. By reducing the dimensionality of data using nonnegative matrix factorization, the data is optimally fit to perform intrusion detection process in the IoT environment. The proposed HSHO-based Deep RL obtained better performance in terms of the metrics like accuracy (96.925%), True Positive Rate (TPR; 96.90%), and True Negative Rate (TNR; 97.920%) with respect to [Formula: see text]-fold.

Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set

Internet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute power, a Network Intrusion Detection System is typically employed to detect and remove malicious packets from entering the network. In the same context, we propose feature clusters in terms of Flow, Message Queuing Telemetry Transport (MQTT) and Transmission Control Protocol (TCP) by using features in UNSW-NB15 data-set. We eliminate problems like over-fitting, curse of dimensionality and imbalance in the data-set. We apply supervised Machine Learning (ML) algorithms, i.e., Random Forest (RF), Support Vector Machine and Artificial Neural Networks on the clusters. Using RF, we, respectively, achieve 98.67% and 97.37% of accuracy in binary and multi-class classification. In clusters based techniques, we achieved 96.96%, 91.4% and 97.54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters. Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.

Optimally Configured Deep Convolutional Neural Network for Attack Detection in Internet of Things: Impact of Algorithm of the Innovative Gunner

Nowadays, the internet of things (IoT) has gained significant research attention. It is becoming critically imperative to protect IoT devices against cyberattacks with the phenomenal intensification. The malicious users or attackers might take control of the devices and serious things will be at stake apart from privacy violation. Therefore, it is important to identify and prevent novel attacks in the IoT context. This paper proposes a novel attack detection system by interlinking the development and operations framework. This proposed detection model includes two stages such as proposed feature extraction and classification. The preliminary phase is feature extraction, the data from every application are processed by integrating the statistical and higher-order statistical features together with the extant features. Based on these extracted features the classification process is evolved for this, an optimized deep convolutional neural network (DCNN) model is utilized. Besides, the count of filters and filter size in the convolution layer, as well as the activation function, are optimized using a new modified algorithm of the innovative gunner (MAIG), which is the enhanced version of the AIG algorithm. Finally, the proposed work is compared and proved over other traditional works concerning positive and negative measures as well. The experimental outcomes show that the proposed MAIG algorithm for application 1 under the GAF-GYT attack achieves higher accuracy of 64.52, 2.38 and 3.76% when compared over the methods like DCNN, AIG and FAE-GWO-DBN, respectively.

Blacksite: human-in-the-loop artificial immune system for intrusion detection in internet of things

Blacksite: human-in-the-loop artificial immune system for intrusion detection in internet of things

Gradient Masking of Label Smoothing in Adversarial Robustness

Deep neural networks (DNNs) have achieved impressive results in several image classification tasks. However, these architectures are unstable for adversarial examples (AEs) such as inputs crafted by a hardly perceptible perturbation with the intent of causing neural networks to make errors. AEs must be considered to prevent accidents in areas such as unmanned car driving using visual object detection in Internet of Things (IoT) networks. Gaussian noise with label smoothing or logit squeezing can be used to increase the robustness against AEs in the training of DNNs. However, from a model interpretability aspect, Gaussian noise with label smoothing does not increase the adversarial robustness of the model. To resolve this problem, we tackle the AE instead of measuring the accuracy of the model against AEs. Considering that a robust model shows a small curvature of the loss surface, we propose a metric to measure the strength of the AEs and the robustness of the model. Furthermore, we introduce a method to verify the existence of the obfuscated gradients of the model based on the black-box attack sanity check method. The proposed method enables us to identify a gradient masking problem wherein the model does not provide useful gradients and exploits false defenses. We evaluate our technique against representative adversarially trained models using the CIFAR10, CIFAR100, SVHN, and Restricted ImageNet datasets. Our results show that the performance of some false defense models decreases by up to 32% compared to the previous evaluation metrics. Moreover, our metric reveals that traditional metrics used to measure the robustness of the model may produce false results.

Collaborative Life-Cycle-Based Botnet Detection in IoT Using Event Entropy

This paper introduces a collaborative and distributed method for botnet detection in massive networks such as internet of things (IoT) and wide area networks (WAN). The method is model-based and designed as a multi-agent system where the agents are situated on IoT devices. Every agent analyzes the events' entropies, then exchanges its decision with its neighbors aiming at establishing global decision if a botnet is ongoing to be installed within the network or not. Decisions spread over the network where a consensual dominant decision can emerge. In previous similar works, it was necessary to use some central hosts in order to compute global decisions. So, scalability is compromised, and the solution is not suited for massive networks such as IoT. The proposed approach does not require any central control, which allows it to be used in IoT and ad hoc networks. Furthermore, the botnet is detected at the early stage of its life-cycle. Conducted experiments have shown that the proposed approach is well suited for botnet detection in IoT and WAN.

TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of Things

In the recent years, the Internet of Things has been becoming a vulnerable target of intrusion attacks. As the academia and industry move towards bringing the Internet of Things (IoT) to every sector of our lives, much attention needs to be given to develop advanced Intrusion Detection Systems (IDS) to detect such attacks. In this work, we propose a novel network-based intrusion detection method which learns patterns of benign flows in a temporal codebook. Based on the temporally learnt codebook, we propose a feature representation method to transform the raw flow-based statistical features into more discriminative representations, called TempoCode-IoT. We develop an ensemble of machine learning-based classifiers optimized to discriminate the malicious flows from the benign ones, based on the proposed TempoCode-IoT. The effectiveness of the proposed method is empirically evaluated on a state-of-the-art realistic intrusion detection dataset as well as on a real botnet-infected IoT dataset, achieving high accuracies and low false positive rates across a variety of intrusion attacks. Moreover, the proposed method outperforms several state-of-the-art works based on the used datasets, proving the effectiveness of Tempo-Code-IoT over raw flow features, both in terms of accuracies and processing speeds.

MDS Clone: Multidimensional Scaling Aided Clone Detection in Internet of Things

MDS Clone: Multidimensional Scaling Aided Clone Detection in Internet of Things - written by Kshama K B Giri , Mamatha B S published on 2020/07/11 download full article with reference data and citations

A Multi-layer Security approach for DDoS detection in Internet of Things

PurposeThe purpose of the paper is to analyze the layer wise security issues in IoT and to obtain the effective security mechanism for jamming attack .Design/methodology/approachIn this study, the authors proposed a multi layer security approach for the detection of DDoS in IoT environment, which protects the smart devices from DDoS, this scheme also reduces the computational cost in the network under mobility condition.FindingsEven though many works have been done for the security of wireless sensor network (WSN), all works have focused on encryption which depends on the key management strategy. In this study, the authors proposed a multilayer approach to analyze the layer wise security issues and also proposed a threshold-based countermeasure (TBC) for replay attack in each layer.Originality/valueThe results indicate that the proposed algorithm lowers the computational costs and energy consumption than in modern schemes. Also, the proposed research work improves the scalability of sensor networks using the TBC.

Network anomaly detection based on selective ensemble algorithm

In order to reduce the loss of information of the majority class samples in the resampling process, combining the distribution of class samples and the characteristics of ensemble learning algorithm, in this paper, a two-level selective ensemble learning algorithm for imbalanced datasets is proposed. Firstly, the algorithm under-samples the majority class samples and constructs multiple training subsets. The training process will generate multiple base classifiers using AdaBoost algorithm, then select some base classifiers according to maximum correlation and minimum redundancy criteria, and form sub-classifiers according to weighted integration. Then, generate multiple sub-classifiers for multiple training subsets, and then, select some sub-classifiers according to maximum correlation and minimum redundancy criteria. Then, the weights of the selected sub-classifiers are calculated by F-means or G-means, and the ensemble classifier is obtained by weighted voting. Finally, the improved algorithm for imbalanced dataset is applied to the network anomaly detection. The experimental results on UCI datasets show that this method can improve the classification performance to a certain extent, especially for imbalanced datasets. Finally, the algorithm is applied to network anomaly detection for Internet of Things. From the simulation data of KDDCUP99 dataset, we can see that TLSE-ID algorithm has a small missing report rate and high precision.

Home Security and Automation Based on Internet of Things: A Comprehensive Review

The current trends of research are focused on the use of the Internet of Things (IoT) for various real-time applications. IoT is a type of “universal global neural network” and very common because of its benefits in smart application trends over traditional communication trends. Information is now accessible much easily because of the internet and inexpensive. So security and automation become a need of every department and organization. The main objective of this study is to overcome the different face detection approaches in the IoT domain and their application in the automation of smart home devices. However, this review concludes the constraints and gaps of existing published work related to the current topic and point towards feasible future research directions. Furthermore, we have discussed possible challenges of large data processing, as well as communication and automation protocols by applying different sensors, other hardware, and their interoperability. Similarities and differences of various key factors are presented in tables in terms of the proposed model, technology, application, software, user interface, algorithm, hardware, and purpose, etc. In this study, we also discussed the pros and cons of various facial detection approaches and gaps of IoT in home automation. We suppose that this review will be a solid base for future researchers in the field of automation and facial detection in IoT.

Applying machine learning and parallel data processing for attack detection in IoT

Internet of Things (IoT) networks are kind of computer networks for which the problem of information security and, in particular, computer attack detection is acute. For solving this task the paper proposes a joint application of methods of machine learning and parallel data processing. The structure of basic classifiers is determined, which are designed for detecting the attacks in IoT networks, and a new approach to their combining is proposed. The statement of classification problem is formed in which the integral indicator of effectiveness is the ratio of accuracy to time of training and testing. For enhancing the speed of training and testing we propose the usage of the distributed data processing system Spark and multi-threaded mode. Moreover, a dataset pre-processing procedure is suggested, which leads to a significant reduction of the training sample volume. An experimental assessment of the proposed approach shows that the attack detection accuracy in IoT networks approaches 100 percent, and the speed of dataset processing increases in proportion to the number of parallel threads.

An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks

With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.

A Modified Multi-objective Particle Swarm Optimizer-Based Lévy Flight: An Approach Toward Intrusion Detection in Internet of Things

The emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high diversity of IoT devices, its protocols and standards, and its limited computational resources have led to the appearance of novel security challenges. Hence, the traditional security countermeasures of encryption and authentication are insufficient. Promoting the network security is a fundamental concern for practitioners for safeguarding their economical and industrial strategies. Intrusion detection systems (IDSs) are the major solutions for protecting Internet-connected frameworks at the network-level. But, more importantly, is how to convert the traditional IDSs into intelligent IDSs that resemble the intelligent IoT. This paper presents a new approach for converting the traditional IDSs into smart, evolutionary, and multi-objective IDSs for IoT networks. Moreover, this article presents a modified algorithm for IDSs that tackles the problem of feature selection. The modified algorithm stands on the integration of multi-objective particle swarm optimization with Levy flight randomization component (MOPSO-Levy); the modified MOPSO-Levy has been tested on real IoT network data that is drawn from UCI repository. MOPSO-Levy has achieved superior performance results when compared with state-of-the-art evolutionary multi-objective algorithms.

An Efficient Spam Detection Technique for IoT Devices Using Machine Learning

The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. In such an environment, machine learning (ML) algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability, and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this article, we propose the security of the IoT devices by detecting spam using ML. To achieve this objective, Spam Detection in IoT using Machine Learning framework is proposed. In this framework, five ML models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. REFIT Smart Home data set is used for the validation of proposed technique. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.