Internet of Things (IoT) and its applications are the most popular research areas at present. The characteristics of IoT on one side make it easily applicable to real-life applications, whereas on the other side expose it to cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks against IoT. In this paper, we investigate the prospects of using machine learning classification algorithms for securing IoT against DoS attacks. A comprehensive study is carried on the classifiers which can advance the development of anomaly-based intrusion detection systems (IDSs). Performance assessment of classifiers is done in terms of prominent metrics and validation methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze the significant differences among classifiers statistically. In addition, Raspberry Pi is used to evaluate the response time of classifiers on IoT specific hardware. We also discuss a methodology for selecting the best classifier as per application requirements. The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier’s performance.

Machine Learning Based Intrusion Detection Systems for IoT Applications

Nowadays, the Internet of Things (IoT) research domain attracts the researchers, due to its extensive collection of applications and ease in deploying in several domains of real life, particularly for environments that are considered critical such as E-health, smart homes, and smart cities. Things in smart cities are intractable via the Internet. These things are naturally deployed in a distributed environment wirelessly. They become vulnerable to the diverse security attacks that can adversely influence their proper functionalities at any time. The stated problem severity even becomes higher when they are deployed in smart cities. In addition, it is very likely to compromise data while transferring from one source to another until reaching the destination during data routing. Existing Routing Protocols for Low Power and Lossy Networks (RPL) are considered lightweight and secure routing protocols for IoT devices, which offer a slight safeguard against innumerable forms of RPL routing attacks. Based on the nature of the IoT network, being resource constraints, the conventional routing techniques do not suit them at all. The IoT routing security is therefore, a challenging task. This review aims to elaborate on the current research literature, opportunities and research gaps of secure RPL routing protocols. Where mainly considering the Rank and Version number attacks types for IoT applications. Further, the review extravagant the need for a new secure RPL protocol to address the security issues of IoT applications for smart cities particularly based on available literature.

Proposing a Secure RPL based Internet of Things Routing Protocol: A Review

A basic course in statistics, by G. M. Clarke and D. Cooke. Pp xvi, 368. £6·50. 1978. SBN 0 7131 2672 8 (Edward Arnold)

REATO: REActing TO Denial of Service attacks in the Internet of Things

Internet of Things (IoT) is a network of “things,” connected via Internet, to collect and exchange data. These “things” can be sensors, actuators, smartphones, wearables, computers, or any object that is interconnected to provide specific services. Similarly, wireless sensor network (WSN), as a part of IoT, forwards the gathered data after sensing any event. The scalability and heterogeneity of IoT offer limited protection and is prone to diverse attacks, including WSN-inherited attacks. Moreover, IPv6 routing protocol for low power and lossy networks (RPL), a de facto routing protocol for IoT networks, also suffers from certain vulnerabilities based on its features and functionalities. Researchers have proposed various mitigation mechanisms for secure networks and routing in IoT. Recently, trust-based approaches have gained tremendous interest from the research community to embed security in IoT networks and routing protocols. In the existing literature, several trust models have been introduced according to the security needs of the IoT system, such as SecTrust, DCTM-IoT, CTRUST, etc. In this research, security issues and requirements of IoT networks and RPL routing protocol are studied with respect to various attacks, such as Blackhole, Spoofing, Rank, etc. Additionally, various mitigation methods and significance of trust models in IoT for secure routing are analyzed. Further, trust metrics in IoT environments, including the open issues and research challenges, as well as the implication of trust as a security paradigm in IoT networks and routing protocols are discussed.

A Comprehensive Review on Secure Routing in Internet of Things: Mitigation Methods and Trust-Based Approaches

This paper presents two lightweight mitigation techniques for RPL Version Number Attacks (VNA) which affect the performance of IPv6-connected Low Power and Lossy Networks (LLNs) detrimentally. In RPL VNA, an attacker maliciously changes the version number of the network and thus increases delay and control message overhead and reduces network lifetime and packet delivery ratio. Simple yet effective mitigation mechanisms proposed in this paper promise significant performance improvements in an RPL network under attack. By means of the proposed techniques, the delay caused by the attacker can be shortened up to 87%, the average power consumption can be reduced up to 63%, the control message overhead can be lowered up to 71% and the data packets delivery ratio can be increased up to 86%. The proposed techniques, while allowing the ordinary RPL operation, trade off the mitigation performance against the resource overheads and thus allow network administrators to choose the right scheme for their RPL network.

New lightweight mitigation techniques for RPL version number attacks

Internet of Things (IoT) is a network of sensors, actuators, mobile and wearable devices, simply things that have processing and communication modules and can connect to the Internet. In a few years time, billions of such things will start serving in many fields within the concept of IoT. Self-configuration, autonomous device addition, Internet connection and resource limitation features of IoT causes it to be highly prone to the attacks. Denial of Service (DoS) attacks which have been targeting the communication networks for years, will be the most dangerous threats to IoT networks. This study aims to analyze and classify the DoS attacks that may target the IoT environments. In addition to this, the systems that try to detect and mitigate the DoS attacks to IoT will be evaluated.

Internet-of-Things security: Denial of service attacks

In this paper, security analysis of RSA implementation on a Raspberry Pi against electromagnetic radiation analysis is evaluated. Two different RSA algorithm is implemented on Raspberry Pi. Simple Electromagnetic Analysis and Differential Electromagnetic Analysis attacks are performed. Using Simple Electromagnetic Analysis attack on an unprotected implementation, all key bits are found with one measurement. Also, Differential Electromagnetic Analysis attack is performed against an improved implemantation that has countermeasure against Simple Electromagnetic Analysis attack. One key bit is found using 2500 measurements with a correlation analysis in Differential Electromagnetic Analysis attack.

Electromagnetic radiation analysis of implementation of RSA algorithm on a Raspberry Pi

In this paper, a System on Chip (SoC) was designed to prevent external intervention by using Advanced Encryption Standarts (AES) encryption methods for data exchange between between in-vehicle sensors and electronic control units (ECU) of autonomous vehicles. Secure on-board communication (SecOC) with a layered structure in the literature has been chosen to provide secure communication between in-vehicle electronic control units (ECU). The security algorithm of this softwarebased system has been implemented on Field Programmable Gate Arrays (FPGA) with verilog hardware design language (HDL). After the hardware implementation was run, the synthesized block was run with a program written in C using the Microblaze processor that can be implemented on the FPGA. Finally, an application was written in the application layer with the working blocks and tested.

Designing and Implementing Secure Automotive Network for Autonomous Cars

This paper describes the design and implementation of a driver drowsiness detection (DDD) system using a modified RiscV processor on a field-programmable gate array (FPGA). To detect drowsiness, Convolutional Neural Network (CNN) is implemented on a RiscV processor. The CNN is trained to classify four primary driver’s expressions, including distraction, natural, sleep, and yawn. The trained CNN accuracy is 81.07% on validation data. Furthermore, due to FPGA memory limitations, written C code for the trained CNN is optimized in numerous ways. Optimizations include the usage of dynamic fixed-point data types and dynamic memory allocations. On the other hand, the processor is modified by adding three custom instructions, including custom store, conv2d(2 × 2), and multiply and accumulation (MAC) to enhance the computation rate. As a result, the processor with custom store, conv2d(2 × 2), and MAC as custom instructions achieved the best result in terms of latency, with an improvement factor of 1.7 over the base processor and 1.25 over the processor with only custom store and multiply and accumulation (MAC) in exchange of slight increase in area.

Siddika Berna Ors Yalcin

Papers

New lightweight mitigation techniques for RPL version number attacks

Internet-of-Things security: Denial of service attacks

Electromagnetic radiation analysis of implementation of RSA algorithm on a Raspberry Pi

Designing and Implementing Secure Automotive Network for Autonomous Cars

Instruction Set Extension of a RiscV Based SoC For Driver Drowsiness Detection