Devices In Internet Of Things Environment Research Articles

A Systematic Investigation on Botnet Intrusion Detection Using Various Machine Learning Techniques

The Internet of Things (IoT) is growing rapidly in an exponential manner due to its versatility in technology. This has led to many challenges in securing the IoT environment. Devices in IoT environments are vulnerable to various cyberattacks. Botnet-based attacks are predominant and widespread in nature. Due to insufficient memory and computational power, the IoT environment cannot handle the botnet attack that affects security. Identifying intrusions in IoT environments is another challenge for researchers. Finding unknown patterns in the data generated through IoT networks helps improve security in the IoT environment. Machine learning (ML) is a platform that helps identify patterns in the provided data. In this study, we present our research on classifying incoming data from the IoT as malicious or benign using machine learning techniques. We propose an ML-based botnet attack detection framework for nine commercial IoT devices that primarily target BASHLITE and Mirai botnet attacks. Rigorous pragmatic research was conducted on the N-BaIoT dataset, which was extracted from realtime IoT devices connected to a network. Using this framework, the results have been depicted, which can efficiently detect botnet attacks and can also be applied to any other types of attacks.

Forward Secrecy Attack on Privacy Preserving Machine Authenticated Key Agreement for Internet of Things

Internet of things (IoT) is to connect billions of devices and machines via Internet and to have a smart system. Sensors and devices in IoT environment are connected and communicated together. Connecting such a huge number of devices requires high level of security and privacy. A crucial characteristic of ubiquitous IoT devices is their limited resources. In recent times, a scheme for privacy-preserving machine authenticated key agreement scheme (PPMAKA) has been introduced for the IoT environment. It was argued that PPMAKA provides security and privacy at the same time including forward secrecy. Nevertheless, this paper will demonstrate that PPMAKA lacks forward secrecy, a crucial security and privacy feature in the IoT environment. We use Cannetti and Krawzyck threat model for the detailed analysis of PPMAKA. Furthermore, we provide remarks for the future research as it is recommendable to design any security and privacy schemes over IoT environments with lightweight operation and communication property, authenticated key agreement with forward secrecy, anonymity and unlinkability.

Mirai Botnet Attack Detection in Low-Scale Network Traffic

The Internet of Things (IoT) has aided in the development of new products and services. Due to the heterogeneity of IoT items and networks, traditional techniques cannot identify network risks. Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity. While the use of artificial intelligence eliminates the need to define rules, the training and retraining processes require additional processing power. This study proposes a methodology for analyzing constrained devices in IoT environments. We examined the relationship between different sized samples from the Kitsune dataset to simulate the Mirai attack on IoT devices. The training and retraining stages for the Mirai attack were also evaluated for accuracy. Various approaches are evaluated in smaller sample sizes to minimize training time on low-resource devices. Cross-validation was used to avoid overfitting classification methods during the learning process. We used the Bootstrapping technique to generate 1000, 10000, and 100000 samples to examine the performance metrics of different-sized variations of the dataset. In this study, we demonstrated that a sample size of 10000 is sufficient for 99,56% accuracy and learning in the detection of Mirai attacks in IoT devices.

An effective routing algorithm for spectrum allocations in cognitive radio based internet of things

SummaryThe Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad‐hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR‐based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc on‐demand distance vector routing protocol is proposed for CR‐based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed.

Hybrid deep-learning model to detect botnet attacks over internet of things environments

In recent years, the use of the internet of things (IoT) has increased dramatically, and cybersecurity concerns have grown in tandem. Cybersecurity has become a major challenge for institutions and companies of all sizes, with the spread of threats growing in number and developing at a rapid pace. Artificial intelligence (AI) in cybersecurity can to a large extent help face the challenge, since it provides a powerful framework and coordinates that allow organisations to stay one step ahead of sophisticated cyber threats. AI provides real-time feedback, helping rollover daily alerts to be investigated and analysed, effective decisions to be made and enabling quick responses. AI-based capabilities make attack detection, security and mitigation more accurate for intelligence gathering and analysis, and they enable proactive protective countermeasures to be taken to overwhelm attacks. In this study, we propose a robust system specifically to help detect botnet attacks of IoT devices. This was done by innovatively combining the model of a convolutional neural network with a long short-term memory (CNN-LSTM) algorithm mechanism to detect two common and serious IoT attacks (BASHLITE and Mirai) on four types of security camera. The data sets, which contained normal malicious network packets, were collected from real-time lab-connected camera devices in IoT environments. The results of the experiment showed that the proposed system achieved optimal performance, according to evaluation metrics. The proposed system gave the following weighted average results for detecting the botnet on the Provision PT-737E camera: camera precision: 88%, recall: 87% and F1 score: 83%. The results of system for classifying botnet attacks and normal packets on the Provision PT-838 camera were 89% for recall, 85% for F1 score and 94%, precision. The intelligent security system using the advanced deep learning model was successful for detecting botnet attacks that infected camera devices connected to IoT applications.

Robust voice user interface for internet-of-things

Internet of things (IoT) plays significant role in the fourth industrial revolution and attracts an increasing interest due to the rapid development of smart devices. IoT comprises factors of twofold. Firstly, a set of things (i.e., appliances, devices, vehicles, etc.) connected together via network. Secondly, human-device interaction to communicate with these things. Speech is the most natural methodology of interaction that can enrich user experience. In this paper, we propose a novel and effective approach for building customized voice interaction for controlling smart devices in IoT environments (i.e., Smart home). The proposed approach is based on extracting customized tiny decoding graph from a large graph constructed using weighted finite sates transducers. Experimental results showed that tiny decoding graphs are very efficient in terms of computational resources and recognition accuracy in clean and noisy conditions. To emphasize the effectiveness of the proposed approach, the standard Resources Management (RM1) dataset was employed and promising results were achieved when compared with four competitive approaches.

Secure efficient signature for internet of things over near-ring

Prediction of air quality is a topic of great interest in air quality research due to direct With rapid progression in remote innovation and inescapable computerized innovation have given in expanding prevalence and enthusiasm of Internet of Things (IoT) procedure, universally giving comfort and knowledge to our day by day exercises. In IoT based framework situation, shrewd parts are associated wherever as all inclusive things connected in an inescapable model. Guaranteeing security for intersection among brilliant items is fundamentally progressively significant, in this paper, we propose a novel signature scheme which is utilized for carrying communication amongst devices in IoT environment. Moreover we revealed different scheme that are vulnerable. The significance of the proposed scheme over other existing scheme are analyzed in terms of the summary which is illustrated using performance and security comparison.

Encryption Based on Cellular Automata for Wireless Devices in IoT Environment

A large number of physical objects are interconnected and accessed through the internet along with the existing technology like cloud computing, mobile computing, wireless sensor networks and big data forms a big paradigm called the Internet of Things (IoT). Data from remote and neglected areas are collected via wireless sensors and stored in the cloud. Growth in the number of the sensing device and the server to which these are connected leads to many security issues and malicious attacks. With mirai botnet and jeep hack about 150 million user’s information from MyFitnessPal nutrition app was stolen. This paper mainly focuses on security challenges for transmitting the data in the wireless sensor network in the IoT environment. To avoid the malicious attack, eavesdropping, algebraic attacks and other attacks a new security algorithm is proposed based on Cellular Automata (CA) which has a key length of 80 bits and the text size is 64 bits. Reversible rules of CA are used in this algorithm to achieve reversibility, parallelism, stability, randomness, and uniformity. The process of encryption and decryption is performed for 15 rounds to avoid dependency between the ciphertext and the plain text. Finally, we compare the execution time, throughput and the avalanche effect of the proposed algorithm with the existing algorithm like Advanced Encryption Standard (AES), Height, Present algorithm. The proposed algorithm is verified to be a better choice for lost cost and resource-restricted devices.

Deep Reinforcement Learning for Resource Protection and Real-Time Detection in IoT Environment

With the fast advancements of electronic chip technologies in the Internet of Things (IoT), it is urgent to address the copyright protection issue of intellectual property (IP) circuit resources of the electronic devices in IoT environments. In this article, a fast deep-reinforcement-learning (DRL)-based detection algorithm for virtual IP watermarks is proposed by combining the technologies of mapping function and DRL to preprocess the ownership information of the IP circuit resource. The deep $Q$ -learning (DQN) algorithm is used to generate the watermarked positions adaptively, making the watermarked positions secure yet close to the original design, turning the watermarked positions secure. An artificial neural network (ANN) algorithm is utilized for training the position distance characteristic vectors of the IP circuit, in which the characteristic function of the virtual position for IP watermark is generated after training. In IP ownership verification, the DRL model can quickly locate the range of virtual watermark positions. With the characteristic values of the virtual positions in each lookup table (LUT) area and surrounding areas, the mapping position relationship can be calculated in a supervised manner in the neural network, as the algorithm realizes the fast location of the real ownership information in an IP circuit. The experimental results show that the proposed algorithm can effectively improve the speed of watermark detection as also reducing the resource overhead. Besides, it also achieves excellent performance in security.

Advanced lightweight multi-factor remote user authentication scheme for cloud-IoT applications

With the ongoing revolution of Internet-enabled devices, Internet of Things (IoT) has emerged as the most popular networking paradigm. The enormous amount of data generated from smart devices in IoT environment is one of the biggest concerns. Cloud computing has emerged as a key technology to process the generated data. The confidential data of user from IoT devices is stored in cloud server and the remote user can access this data anytime, anywhere and at any place from the cloud server. This makes remote user authentication a critical issue. This paper proposes a lightweight remote user authentication scheme for cloud-IoT applications. The formal security analysis using BAN logic and random oracle model confirms that the scheme is resilient to known security attacks. Furthermore, the scheme is formally verified using AVISPA tool which confirms the security against multiple security attacks.

사물인터넷 디바이스를 위한 DNS 네임 자동설정의 설계 및 구현

As one of the most spotlighted research areas, these days, the Internet of Things (IoT) aims to provide users with various services through many devices. Since there exist so many devices in IoT environments, it is inefficient to manually configure the domain name system (DNS) names of such devices. Thus, for IPv6-based IoT environments, this paper proposes a scheme called the DNS Name Autoconfiguration (DNSNA) that autoconfigures an IoT device's DNS name and manages it. In the procedure for generating and registering an IoT device's DNS name, the standard protocols of the Internet Engineering Task Force (IETF) are used. Since the proposed scheme resolves an IoT device's DNS name into an IPv6 address in unicast through a DNS server, it generates less traffic than multicast-based mDNS (Multicast DNS) which is a legacy DNS application for the DNS name service in the smart home. Thus, the proposed scheme is more appropriate in multi-hop IoT networks than mDNS. This paper explains the design of the proposed scheme and its service scenarios, such as smart home and smart road. It also explains the implementation and testing of the proposed scheme in the smart grid.