Rajkumar Das

Bio: Rajkumar Das is an academic researcher from Federation University Australia. The author has contributed to research in topics: Public opinion & Computer science. The author has an hindex of 5, co-authored 17 publications receiving 114 citations. Previous affiliations of Rajkumar Das include Monash University, Clayton campus & Monash University.

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

Abstract: Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle’s operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

IoT Sensor Numerical Data Trust Model Using Temporal Correlation

Abstract: Internet of Things (IoT) applications are increasingly being adopted for innovative and cost-effective services. However, the IoT devices and data are susceptible to various attacks, including cyberattacks, which emphasizes the need for pervasive security measure like trust evaluation on the fly. There exist several IoT numerical data trustworthiness measures which are based on the quality of information (QoI) and correlations. The QoI measurement techniques excessively exploit heuristics, while the correlation-based approaches predict temporal correlation using an average or moving average, which limits their efficacy. To improve accuracy and reliability, we propose a model for assessing trust of IoT sensor numerical data by representing the temporal correlation using temporal relationship. We represent the temporal relationship between data within a time window in two ways: first, using the discrete cosine transform (DCT) coefficients of daily data; and second, to obtain the impact of shuttle variation, we further divide the daily data into some time windows and calculate the average of each DCT coefficient over all time windows. These two feature sets are then used to develop two independent deep neural network models. The model outcomes are fused by the Dempster–Shepard theory to calculate trust scores. The strength of our model is evaluated using both trustworthy and untrustworthy data—the former are collected from sensors under controlled supervision in a smart city project in Melbourne, Australia and the latter are generated either by simulating breached sensors or perturbing real data. Our proposed approach outperforms a contemporary correlation-based approach in terms of trust score accuracy and consistency.

Assessing Trust Level of a Driverless Car Using Deep Learning

Abstract: The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people’s main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car.

Opinion Formation in Online Social Networks: Exploiting Predisposition, Interaction, and Credibility

Abstract: The challenging but intriguing problem of modeling opinion formation dynamics in online social networks (OSNs) has attracted many researchers in recent years because the inherent complexities present in human opinion update process are yet to be clearly understood. Although the existing works adopt the distance-based homophily principle to model the neighbors’ influences on the formation of an agent’s opinion, they ignore several other key factors that govern the update process. Explicitly, we consider two essential aspects of the real-world opinion formation process that were not explored previously. First, we consider the predisposition of agents that leads to selective exposure to information when presented with different opinion sources. Second, we explicitly consider an agent’s past interaction experience with others and how opinions encountered in the past interactions influence future opinion update process of that agent. Although the confidence level of an agent on the expressed opinion was previously used to distinguish an expert, we propose the concept of the relative credibility of the opinion sources for such distinction. For this, we take into account an agent’s perceived credibility about others and the relative nature of human judgment when exposed to many opinion sources with different credibility. In addition, for the first time, the credibility of sources external to an OSN is considered in the opinion formation model proposed in this paper. We validate our model by analyzing its performance in capturing the real-world opinion formation dynamics using traces collected from an OSN, specifically Twitter. On the other hand, through simulation, various scenarios are created to observe the steady-state outcomes of the dynamics under various influences of our model parameters and network characteristics. Finally, different compelling and practical applications with social and economic values can be built based on our model.

A Review on Autonomous Vehicles: Progress, Methods and Challenges

Abstract: Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.

Enhanced fuzzy logic‐based spray and wait routing protocol for delay tolerant networks

Abstract: SUMMARY Delay tolerant networks are a class of ad hoc networks that enable data delivery even in the absence of end-to-end connectivity between nodes, which is the basic assumption for routing in ad hoc networks. Nodes in these networks work on store-carry and forward paradigm. In addition, such networks make use of message replication as a strategy to increase the possibility of messages reaching their destination. As contact opportunities are usually of short duration, it is important to prioritize scheduling of messages. Message replication may also lead to buffer congestion. Hence, buffer management is an important issue that greatly affects the performance of routing protocols in delay tolerant networks. In this paper, Spray and Wait routing protocol, which is a popular controlled replication-based protocol for delay tolerant networks, has been enhanced using a new fuzzy-based buffer management strategy Enhanced Fuzzy Spray and Wait Routing, with the aim to achieve increased delivery ratio and reduced overhead ratio. It aggregates three important message properties namely number of replicas of a message, its size, and remaining time-to-live, using fuzzy logic to determine the message priority, which denotes its importance with respect to other messages stored in a node's buffer. It then intelligently selects messages to schedule when a contact opportunity occurs. Because determination of number of replicas of a message in the network is a difficult task, a new method for estimation of the same has been proposed. Simulation results show improved performance of enhanced fuzzy spray and wait routing in terms of delivery ratio and resource consumption. Copyright © 2014 John Wiley & Sons, Ltd.