Pavan Pongle

Bio: Pavan Pongle is an academic researcher from Sinhgad College of Engineering. The author has contributed to research in topics: Routing protocol & The Internet. The author has an hindex of 2, co-authored 2 publications receiving 308 citations.

A survey: Attacks on RPL and 6LoWPAN in IoT

Abstract: 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard allows heavily constrained devices to connect to IPv6 networks. 6LoWPAN is novel IPv6 header compression protocol, it may go easily under attack. Internet of Things consist of devices which are limited in resource like battery powered, memory and processing capability etc. for this a new network layer routing protocol is designed called RPL (Routing Protocol for low power Lossy network). RPL is light weight protocol and doesn't have the functionality like of traditional routing protocols. This rank based routing protocol may goes under attack. Providing security in Internet of Things is challenging as the devices are connected to the unsecured Internet, limited resources, the communication links are lossy and set of novel technologies used such as RPL, 6LoWPAN etc. This paper focus on possible attacks on RPL and 6LoWPAN network, counter measure against them and consequences on network parameters. Along with comparative analysis of methods to mitigate these attacks are done and finally the research opportunities in network layer security are discussed.

Real Time Intrusion and Wormhole Attack Detection in Internet of Things

Abstract: There are currently more objects connected to the Internet than people in the world. This gap will continue to grow, as more objects gain the ability to directly interface with the Internet. Providing security in IoT is challenging as the devices are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL and 6LoWPAN. Due to this it is easy to attack in IoT network. The proposed system is a novel intrusion detection system for the IoT, which is capable of detecting Wormhole attack and attacker. The proposed methods uses the location information of node and neighbor information to identify the Wormhole attack and received signal strength to identify attacker nodes. Design of such system will help in securing the IoT network and may prevents such attacks. This method is very energy efficient and only takes fixed number of UDP packets for attack detection, hence it is beneficial for resource constrained environment.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Abstract: The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices

Abstract: The Internet of Things (IoT) is rapidly spreading, reaching a multitude of different domains, including personal health care, environmental monitoring, home automation, smart mobility, and Industry 4.0. As a consequence, more and more IoT devices are being deployed in a variety of public and private environments, progressively becoming common objects of everyday life. It is hence apparent that, in such a scenario, cybersecurity becomes critical to avoid threats like leakage of sensible information, denial of service (DoS) attacks, unauthorized network access, and so on. Unfortunately, many low-end IoT commercial products do not usually support strong security mechanisms, and can hence be target of—or even means for—a number of security attacks. The aim of this article is to provide a broad overview of the security risks in the IoT sector and to discuss some possible counteractions. To this end, after a general introduction to security in the IoT domain, we discuss the specific security mechanisms adopted by the most popular IoT communication protocols. Then, we report and analyze some of the attacks against real IoT devices reported in the literature, in order to point out the current security weaknesses of commercial IoT solutions and remark the importance of considering security as an integral part in the design of IoT systems. We conclude this article with a reasoned comparison of the considered IoT technologies with respect to a set of qualifying security attributes, namely integrity, anonymity, confidentiality, privacy, access control, authentication, authorization, resilience, self organization.