Detection In Internet Of Things Research Articles

Energy-Efficient Deep CNN for Smoke Detection in Foggy IoT Environment

Smoke detection in Internet of Things (IoT) environment is a primary component of early disaster-related event detection in smart cities. Recently, several smoke and fire detection methods are presented with reasonable accuracy and running time for normal IoT environment. However, these methods are unable to detect smoke in foggy IoT environment, which is a challenging task. In this paper, we propose an energy-efficient system based on deep convolutional neural networks for early smoke detection in both normal and foggy IoT environments. Our method takes advantage of VGG-16 architecture, considering its sensible stability between the accuracy and time efficiency for smoke detection compared to the other computationally expensive networks, such as GoogleNet and AlexNet. Experiments performed on benchmark smoke detection datasets and their results in terms of accuracy, false alarms rate, and efficiency reveal the better performance of our technique compared to state-of-the-art and verifies its applicability in smart cities for early detection of smoke in normal and foggy IoT environments.

Intrusion Detection in IoT based Smart Networks using Fuzzy Brain Storm Optimization Technique

The Internet of Things (IoT) is characterized as an approach where objects are outfitted with sensors, processors, and actuators which include design of hardware board and development, protocols, web APIs, and software systems, which combined to make an associated architecture of embedded systems. This connected environment enables technologies to get associated with different networks, platforms, and devices, making a web of communication which is reforming the manner in which we communicate with the world digitally. These connected embedded systems are changing behaviour and interactions with our environment, networks, and homes, and also with our own bodies in terms of smart devices. Security and privacy are the most significant consideration in the field of real-world communication and mainly on IoTs. With the evolution of IoT the network layer security in the IoT has drawn greater focus. The security vulnerabilities in the IoT system could make security risks based on any application. Therefore there is an essential requirement for IDS for the IoT based systems for avoiding security attacks based on security vulnerabilities. This paper proposed a fuzzy c-means clustering with brain storm optimization algorithm (FBSO) for IDS based on IoT system. The NSL-KDD dataset is utilized to evaluate and simulate the proposed algorithm. The results demonstrate that the proposed technique efficiently recognize intrusion attacks and decrease the network difficulties.

A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

Internet of Things (IoT) is a network which connects different communication devices with the internet to attain quick, robust and real-time information transfer and communication, achieving intelligent management. IoT is still in its infancy so it faces numerous challenges varying from data management to security concerns. Sensors generate enormous quantities of data that need to be handled efficiently to have successful deployment of IoT applications. Concerning data management, a great challenge that faces the IoT environment is the detection of contextual anomalies. Contextual anomaly detection is a sophisticated task because the context has to be taken into consideration in the anomaly detection process rather than checking only the deviation of the data value as in point anomaly detection. As a result, in this paper, a novel clustering based algorithm is proposed to detect contextual anomalies in Internet of Things. Attributes were separated into two different categories, namely contextual attributes and behavioral attributes. K-Means clustering technique was applied on the contextual and behavioral attributes separately, then the intersection between the contextual and behavioral clusters was used to detect the contextual anomalies. Moreover, the algorithm was applied on a real room occupation dataset of size around 20,000 records and the experiments showed promising results.

Efficient physical intrusion detection in Internet of Things: A Node deployment approach

In Internet of Things (IoT), intrusion detection plays an important role in many applications for detecting malicious intruders. The intruder can be, an unexpected physically moving entity, invading an area under surveillance, or an adversary in a battlefield. Node deployment strategy plays a crucial role in determining the intrusion detection capability of an IoT network. With uniform deployment, the detection probability is the same for any location in the network area. Nevertheless, different applications may need diverse levels of detection probability at key areas within the network. For example, a battlefield surveillance application needs improved detection probability around the headquarter. On the contrary, a Gaussian deployment strategy provides improved detection probability to the key areas due to differentiated node density. However, it is neither energy-efficient nor provides a quick detection of the physical intruder.In this work, we introduce a novel deployment strategy to overcome the above said limitations of both uniform and Gaussian deployments for energy-efficient and quick detection. Initially, we investigate the problem of physical intrusion detection in our introduced deployment strategy considering a realistic sensing model. Furthermore, we examine the effects of different network parameters on the detection probability in details. We also derive the relationship between the different network parameters and connectivity to ensure fast detection. We perform exhaustive experiments on real datasets, primarily, in order to validate the correctness of modeling and analyses. Next, we examine the effects of different network parameters on the detection probability. The results clearly demonstrate that our approach improves the detection probability by more than 25% when compared to two well-known deployment strategies under various network parameters.

A Deep Similarity Metric Method Based on Incomplete Data for Traffic Anomaly Detection in IoT

In recent years, with the development of the Internet of Things (IoT) technology, a large amount of data can be captured from sensors for real-time analysis. By monitoring the traffic video data from the IoT, we can detect the anomalies that may occur and evaluate the security. However, the number of traffic anomalies is extremely limited, so there is a severe over-fitting problem when using traditional deep learning methods. In order to solve the problem above, we propose a similarity metric Convolutional Neural Network (CNN) based on a channel attention model for traffic anomaly detection task. The method mainly includes (1) A Siamese network with a hierarchical attention model by word embedding so that it can selectively measure similarities between anomalies and the templates. (2) A deep transfer learning method can automatically annotate an unlabeled set while fine-tuning the network. (3) A background modeling method combining spatial and temporal information for anomaly extraction. Experiments show that the proposed method is three percentage points higher than deep convolutional generative adversarial network (DCGAN) and five percentage points higher than AutoEncoder on the accuracy. No more time consumption is needed for the annotation process. The extracted candidates can be classified correctly through the proposed method.

Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach

The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods.

A Comprehensive Analysis of Machine Learning Models for Real Time Anomaly Detection in Internet of Things

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Intrusion Detection in Internet of Things: The Review of Taxonomy

Intrusion Detection in Internet of Things: The Review of Taxonomy

Design of cognitive fog computing for intrusion detection in Internet of Things

Internet of things (IoT) is penetrating into every aspect of our lives including our body, our home and our living environment along with numerous security challenges. With rapidly growing number of connected devices in IoT, the scope for cyber-attack also increases exponentially. Therefore an effective intrusion detection system (IDS) is needed to efficiently detect the attack at faster rate in highly scalable and dynamic IoT environment. In this paper, a novel intrusion detection technique is proposed based on fog computing using Online Sequential Extreme Learning Machine (OS-ELM) which can intelligently interpret the attacks from the IoT traffic. In the proposed system, the existing centralized cloud intelligence in detecting the attack is distributed to local fog nodes to detect the attack at faster rate for IoT application. The distributed architecture of fog computing enables distributed intrusion detection mechanism with scalability, flexibility and interoperability. The analysis of the proposed system proves to be efficient in terms of response time and detection accuracy.

GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things

The objective of any anomaly detection system is to efficiently detect several types of malicious traffic patterns that cannot be detected by conventional firewall systems. Designing an efficient intrusion detection system has three primary challenges that include addressing high dimensionality problem, choice of learning algorithm, and distance or similarity measure used to find the similarity value between any two traffic patterns or input observations. Feature representation and dimensionality reduction have been studied and addressed widely in the literature and have also been applied for the design of intrusion detection systems (IDS). The choice of classifiers is also studied and applied widely in the design of IDS. However, at the heart of IDS lies the choice of distance measure that is required for an IDS to judge an incoming observation as normal or abnormal. This challenge has been understudied and relatively less addressed in the research literature both from academia and from industry. This research aims at introducing a novel distance measure that can be used to perform feature clustering and feature representation for efficient intrusion detection. Recent studies such as CANN proposed feature reduction techniques for improving detection and accuracy rates of IDS that used Euclidean distance. However, accuracies of attack classes such as U2R and R2L are not significantly promising. Our approach GARUDA is based on clustering feature patterns incrementally and then representing features in different transformation space through using a novel fuzzy Gaussian dissimilarity measure. Experiments are conducted on both KDD and NSL-KDD datasets. The accuracy and detection rates of proposed approach are compared for classifiers such as kNN, J48, naive Bayes, along with CANN and CLAPP approaches. Experiment results proved that proposed approach resulted in the improved accuracy and detection rates for U2R and R2L attack classes when compared to other approaches.

INTRUSION DETECTION IN INTERNET OF THINGS (IOT)

The Internet of Things (IoT) is an ever-growing network of smart objectswhere one physical object can exchange information with another physical object. Security and privacy of data are one of the prime concerns in today’s Internet of Things (IoT). The intrusion detection in internet of things exposes the extent with which the internet of things is vulnerable to attacks and how such attack can be detected to prevent extreme damage. It emphasises on threats, vulnerability, attacks and possible methods of detecting intruders in order to prevent the system from further damage.

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

The Internet-of-Things (IoT) is rapidly becoming ubiquitous. However the heterogeneous nature of devices and protocols in use, the sensitivity of the data contained within, as well as the legal and privacy issues, make security for the IoT a growing research priority and industry concern. With many security practices being unsuitable due to their resource intensive nature, it is deemed important to include second line defences into IoT networks. These systems will also need to be assessed for their efficacy in a variety of different network types and protocols. To shed light on these issues, this paper is concerned with advancements in intrusion detection practices in IoT. It provides a comprehensive review of current intrusion detection systems (IDSs) for IoT technologies, focusing on architecture types. A proposal for future directions in IoT based IDS are then presented and evaluated. We show how traditional practices are unsuitable due to their inherent features providing poor coverage of the IoT domain. In order to develop a secure, robust and optimized solution for these networks, the current research for intrusion detection in IoT will need to move in a different direction. An example of which is proposed in order to illustrate how malicious nodes might be passively detected.

Non-parametric sequence-based learning approach for outlier detection in IoT

Although study on outlier detection techniques has long been an area of much research, few of those works relate to an Internet of Things (IoT) environment. In the last few years, with the advent of IoT and its numerous smart objects, data generated from sensors have increased exponentially. Since on the basis of these data many critical decisions are taken, it is therefore necessary to absolutely ensure its accuracy, correctness and integrity before any processing starts. Most algorithms in the past assumes outliers to be always an Error, that is caused by a malfunctioning sensor. However, this is not always true because an outlier could also be an important event that should not be neglected. This stresses the need to devise algorithms that is suited for an IoT environment which considers both Error, that is a result of faulty sensors or an Event, which is an indication of an abnormal phenomenon. This paper proposes a sequence based learning approach for outlier detection that works for both Error and Event. Simulations are performed on few benchmark datasets, a medical dataset and a real world dataset obtained through an experimental test bed. The results reveal exceptionally high accuracies with up to 99.65% for Error detection and 98.53% for Event detection.

Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring

As a novel cyber-physical-social network paradigm, the Internet of Things (IoT) provides a powerful tool to monitor the hazardous fields of interest. Due to the uneven random deployment, sensor energy depletion, and external attacks, the emergence of coverage holes would remarkably degrade the network performance and quality of service. For overcoming the drawbacks resulting from the coverage holes, this paper focuses on how to locally detect coverage holes by exploiting one-hop neighboring sensors’ cooperation based on the novel confident information coverage model (CIC), which is formulated as the localized confident information coverage hole detection (LCICHD) problem. For handling the CICHD problem, we devise a family of heuristic CIC holes detection schemes including the LCHD, LCHDRL, random and randomRL. Both the LCHD and LCHDRL schemes locally determine coverage status of each subregion and take the sensor communication ability into consideration. While the LCHDRL considers not only the sensor remaining energy but also the residual lifetime during the CIC hole detection. After acquiring the coverage status of each partitioned local subregion, the coverage hole boundary will be extracted by image processing techniques. For comparison, both the Random and RandomRL schemes arbitrarily select sensors within the sensing field to detect CIC holes, and the RandomRL scheme takes the sensors’ residual lifetime into consideration during the hole detection process. Experimental simulations show that the proposed schemes can efficiently detect the emerged coverage holes including the locations and the number, and the LCHDRL algorithm is more practical and efficient compared with the other three peer solutions.

ISMA: Intelligent Sensing Model for Anomalies Detection in Cross Platform OSNs With a Case Study on IoT

In the recent years, the user activities over online social networks (OSNs) have increased tremendously. A large number of users share information across the different social networking platforms. The information across the OSNs is easy to access, and thus, can be easily used by the fraudulent users for misleading the entire community. Such fraudulent users are termed anomalies. In this paper, a problem of cross-platform anomalies is considered, which possesses different behaviors by an individual with different users across the multiple OSNs. The variation in the behavior and activity makes it difficult to identify such anomalies. A solution to this problem is proposed on the basis of cognitive tokens, which provide an intelligent sensing model for anomalies detection (ISMA) by deliberately inducing faulty data to attract the anomalous users. A common login system for different OSNs is also suggested as a part of collaborative anomaly identification across different OSNs. A fair play point approach is used for the determination of anomalies. Both simulations and email-based real data sets are used to measure the performance of the proposed approach. Furthermore, as an example of implementation, a case study is presented for anomaly detection in Internet of Things. The proposed approach is able to provide the highest accuracy at the rate of 99.2%; this is 25.1% higher as compared with the SVM-RBF and sigmoid approach, and 22% higher than that of the k-nearest neighbor approach. Furthermore, the proposed ISMA also caused less error in detecting the anomalies, which were within the range of 0.1% to 2.8%. The error in identification is reduced up to 96.6% in comparison with the SVM and k-nearest neighbor approaches. The gains in comparative results validate the efficiency of the ISMA in identification and classification of anomalies in cross-platform OSNs.

Geo-Spatial Location Spoofing Detection for Internet of Things

We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called Enhanced Location Spoofing Detection using Audibility (ELSA) which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the user's device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit available audibility information to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the device's location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.

An Artificial Immune-Based Distributed Intrusion Detection Model for the Internet of Things

Traditional detection technology for network attacks is difficult to adapt the complicated and changeful environment of the Internet of Things (IoT). In the interest of resolving the distributed intrusion detection problem of IoT, this paper proposes an artificial immune-based theory model for distributed intrusion detection in IoT. Artificial immune principles are used to solve the problem of IoT intrusion detection. Antigen, self, non-self and detector in the IoT environment are defined. Good immune mechanisms are simulated. Detector is evolved dynamically to make the proposed model have self-learning and self-adaptation. The outstanding detectors which have accepted training are shared in the whole IoT to adapt the local IoT environment and improve the ability of global intrusion detection in IoT. The proposed model is expected to realize detecting intrusion of IoT in distribution and parallelity.