scholarly journals Examining Modern Data Security and Privacy Protocols in Autonomous Vehicles

2021 ◽  
Vol 13 (5) ◽  
pp. 01-19
Author(s):  
Mingfu Huang ◽  
Rushit Dave ◽  
Nyle Siddiqui ◽  
Naeem Seliya
Keyword(s):  
Data Security ◽  
Intrusion Detection System ◽  
Autonomous Vehicles ◽  
Data Privacy ◽  
Detection System ◽  
Security And Privacy ◽  
Interconnected Network ◽  
Cognitive Engine ◽  
Different Types ◽  
Context Specific

A fully automated, self-driving car can perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Connected autonomous vehicles (CAVs) have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. Such technology includes the use of several sensors, various algorithms, interconnected network connections, and multiple auxiliary systems. CAVs have been subjected to attacks by malicious users to gain/deny control of one or more of its various systems. Data security and data privacy is one such area of CAVs that has been targeted via different types of attacks. The scope of this study is to present a good background knowledge of issues pertaining to different attacks in the context of data security and privacy, as well present a detailed review and analysis of eight very recent studies on the broad topic of security and privacy related attacks. Methodologies including Blockchain, Named Data Networking, Intrusion Detection System, Cognitive Engine, Adversarial Objects, and others have been investigated in the literature and problem- and context-specific models have been proposed by their respective authors.

scholarly journals Different Dimensions of IOT Security

2020 ◽  
Vol 8 (5) ◽  
pp. 860-863
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Attacks ◽  
Security And Privacy ◽  
Security Threats ◽  
Iot Security ◽  
Different Types ◽  
Different Dimensions ◽  
Iot Devices ◽  
Privacy Issues

IOT is wirelessly connecting things to the internet using sensors, RFID’s and remotely accessing and managing them over our phone or through our voice. IOT uses various communication protocols such as Zigbee, 6LowPan, Bluetooth and has bi directional communication for exchange of information. The database for IOT is cloud which is also vulnerable to security threats. The increasing amount of popularity of IoT and its pervasive usage has made it more recurrent to prominent cyber-attacks such as botnet attack, IoT ransom ware, DOS attack, RFID hack. The challenges faced by IoT are to stop hackers from stealing data, having unattended access to the device and performing malicious activities. There are many techniques which can be used to secure IoT devices such as using a secure encrypted Wi-Fi network, using digital signature for authenticity, updating to latest patches, installing Intrusion Detection System. We’ll also be assessing various IoT devices and threats associated with them in real time environment and the level of harm these threats can cause to the device if they are not properly mitigated or eradicated. In this paper we’ll also be addressing different types of risks associated with different IOT devices and approaches to solve the security and privacy issues

scholarly journals INTELLIGENT MULTI-AGENT INFORMATION SECURITY SYSTEM

2014 ◽  
pp. 35-39
Author(s):  
N. Kussul ◽  
A. Shelestov ◽  
A. Sidorenko ◽  
S. Skakun ◽  
V. Pasechnyk
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Intelligent Agents ◽  
Intrusion Detection System ◽  
User Behavior ◽  
Detection System ◽  
System Behavior ◽  
Agent Based ◽  
Different Types ◽  
Multi Agent

It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute.

Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles

2021 ◽  
Author(s):  
Jennifer Dukarski ◽  
Keyword(s):  
Autonomous Vehicles ◽  
Data Privacy ◽  
Data Use ◽  
Legal Issues ◽  
Security And Privacy ◽  
Use Of Data ◽  
Vehicle Data ◽  
Legal Challenges ◽  
The Us ◽  
Infrastructure Services

Modern automobiles collect around 25 gigabytes of data per hour and autonomous vehicles are expected to generate more than 100 times that number. In comparison, the Apollo Guidance Computer assisting in the moon launches had only a 32-kilobtye hard disk. Without question, the breadth of in-vehicle data has opened new possibilities and challenges. The potential for accessing this data has led many entrepreneurs to claim that data is more valuable than even the vehicle itself. These intrepid data-miners seek to explore business opportunities in predictive maintenance, pay-as-you-drive features, and infrastructure services. Yet, the use of data comes with inherent challenges: accessibility, ownership, security, and privacy. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles examines some of the pressing questions on the minds of both industry and consumers. Who owns the data and how can it be used? What are the regulatory regimes that impact vehicular data use? Is the US close to harmonizing with other nations in the automotive data privacy? And will the risks of hackers lead to the “zombie car apocalypse” or to another avenue for ransomware? This report explores a number of these legal challenges and the unsettled aspects that arise in the world of automotive data.

Redundancy Management and Host Intrusion Detection in WSN

2020 ◽  
pp. 153-167 ◽  
Author(s):  
Vijander Singh ◽  
Ramesh C. Poonia ◽  
Linesh Raja ◽  
Gourav Sharma ◽  
Narendra Kumar Trivedi ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Access Point ◽  
Brute Force ◽  
Software Application ◽  
Redundancy Management ◽  
Different Types ◽  
Network Event

Intrusion detection system (IDS) is a software application that gives the facility to monitor the traffic of network, event, or activities on networks and finds if any malicious operation occurs. Hackers use different types of attacks to capture the information and use brute force attacks to match the authenticated key with the key, which the hacker has in its stable. When there is a match, the hacker gets the authenticated key through which he can connect with the hotspot or AP. IDS finds invalid or any other misbehavior in the system. The protocol will take care of it; protocol checks the MAC address of the device which wishes to connect with the hotspot or AP, and if any device repeatedly enters a wrong password, the protocol will gives a pop up on the administrator system. The objective of this chapter is to provide information about the protocol that behaves like IDS and is pre-implemented in the routers, which gives the alert to the administrator if any intruder tries to connect with the hotspot or AP (access point) with the rapid wrong key.

scholarly journals Internet of Things: Evolution, Concerns and Security Challenges

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1809
Author(s):  
Parushi Malhotra ◽  
Yashwant Singh ◽  
Pooja Anand ◽  
Deep Kumar Bangotra ◽  
Pradeep Kumar Singh ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Security And Privacy ◽  
Research Assessment ◽  
The Internet ◽  
Digital World ◽  
Network Intrusion ◽  
Depth Analysis

The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.

scholarly journals Integration of Blockchain with Connected and Autonomous Vehicles: Vision and Challenge

2022 ◽  
Vol 14 (1) ◽  
pp. 1-10
Author(s):  
Tooska Dargahi ◽  
Hossein Ahmadvand ◽  
Mansour Naser Alraja ◽  
Chia-Mu Yu
Keyword(s):  
Data Protection ◽  
Intelligent Transportation Systems ◽  
Data Security ◽  
Autonomous Vehicles ◽  
Personal Data ◽  
Transportation Systems ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Blockchain Technology ◽  
Wide Range

Connected and Autonomous Vehicles (CAVs) are introduced to improve individuals’ quality of life by offering a wide range of services. They collect a huge amount of data and exchange them with each other and the infrastructure. The collected data usually includes sensitive information about the users and the surrounding environment. Therefore, data security and privacy are among the main challenges in this industry. Blockchain, an emerging distributed ledger, has been considered by the research community as a potential solution for enhancing data security, integrity, and transparency in Intelligent Transportation Systems (ITS). However, despite the emphasis of governments on the transparency of personal data protection practices, CAV stakeholders have not been successful in communicating appropriate information with the end users regarding the procedure of collecting, storing, and processing their personal data, as well as the data ownership. This article provides a vision of the opportunities and challenges of adopting blockchain in ITS from the “data transparency” and “privacy” perspective. The main aim is to answer the following questions: (1) Considering the amount of personal data collected by the CAVs, such as location, how would the integration of blockchain technology affect transparency , fairness , and lawfulness of personal data processing concerning the data subjects (as this is one of the main principles in the existing data protection regulations)? (2) How can the trade-off between transparency and privacy be addressed in blockchain-based ITS use cases?

scholarly journals Intrusion Detection System using Deep Neural Networks and Principal Component Analysis

2021 ◽  
Vol 10 (5) ◽  
pp. 113-124
Author(s):  
Abdoulwase M. Obaid Al-Azzani ◽  
Ali Mohammed Afif
Keyword(s):  
Intrusion Detection ◽  
Data Security ◽  
Intrusion Detection System ◽  
Deep Neural Networks ◽  
Detection System ◽  
Principal Component ◽  
Superior Performance ◽  
Learning Approach ◽  
Abnormal Behaviour ◽  
Machine Learning Approach

The amount of data on the internet is growing daily, because of using the smart phones, social network, and IoT. This growing had impact on data security. Therefore security become the biggest challenge for researchers and developers, moreover, most of security tools (firewall, IDS, IPS, etc.) have limitations to detect all threats. Deep Learning is one of Machine Learning approach, it is an efficient artifice that can be applied to intrusion detection, to ascertain a new outline from the massive network data, as well as it used to reduce the strain of the manual compilations of the normal and abnormal behaviour patterns. In this paper we build a model for detect threats based on Principal Component Analysis (PCA) for reduction dimensions of dataset, and deep neural network for the classification. We used KDD99 dataset and 10_ percentage of KDD99 dataset to train and test the model in Spark environment. In experiment DNN of layers ranging from 1 to 3 with 300 number of epotch. The results were compared and concluded that a DNN of 1 layer has superior performance with 0.929 as accuracy.

scholarly journals A Novel Security Architecture Based on Haystack System for HDFS Storage System: Extended Work

2020 ◽  
Vol 9 (4) ◽  
pp. 709-719
Keyword(s):  
Big Data ◽  
Data Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Storage System ◽  
Security Architecture ◽  
Security Issues ◽  
Real Challenge ◽  
Extended Work ◽  
Shed Light

Over the past few years, the digitization of our different daily transactions and our activities have helped to create the new Big Data era. This phenomenon has become more widely used in different fields for many reasons, such as information retrieval, decision making, etc. But keeping this data safe from any attack attempts constitutes a real challenge. Indeed, Big Data security has become very important recently, since all technologies and large organizations have become dependent on these Data. For this reason, we deal with the Big Data security problems, especially those encountered during their management and treatment, and more precisely, those appeared during the processing with Hadoop system. The paper aims to highlight the Big Data system processing situation in terms of security, also it shed light on some existing security solutions used for overcoming these security issues, we will reveal its strengths and its weaknesses according to the security pillars. Subsequently, we will present our new security architecture, based on Haystack intrusion detection system, which serves to improve the inactive data security contained in HDFS, while analyzing its feasibility and its parameters

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