Attacks and Preventive Measures on Video Surveillance Systems: A Review

Vennam, Preethi; Pramod, T. C.; Thippeswamy, B. M.; Kim, Yong-Guk; N, Pavan Kumar B

doi:10.3390/app11125571

Cited by 34 publications

(17 citation statements)

References 42 publications

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“…IMS being a public monitoring system, it also suffers from the same concern. To secure personal information, several countries have imposed severe restrictions on facial recognition techniques in public [42][43][44][45][46][47][48][49][50][51]. Facial anonymization serves to effectively overcome this issue, as the anonymous face protects personal information during in-cabin monitoring.…”

Section: Face Anonymization: Privacy-preserved Monitoring In Publicmentioning

confidence: 99%

In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Lee

Kim

et al. 2022

Sensors

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In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

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Section: Face Anonymization: Privacy-preserved Monitoring In Publicmentioning

confidence: 99%

In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Lee

Kim

et al. 2022

Sensors

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“…Intelligent monitoring and surveillance systems are widely used to ensure safety and security. Popular applications of monitoring in public are video surveillance cameras (closedcircuit television); monitoring in intelligent transportation systems, including in-cabin monitoring and road tra c monitoring; and video monitoring for data generation and navigational tasks around city centers, airports, and public roads [1]. Driving automation also requires public visual information for multiple tasks [2].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this is an abnormal situation; however, in-cabin monitoring with real faces is not a solution to this problem. e breach of facial information leads to multiple consequences, such as misuse of facial data and banking and financial fraud [1,6,7,13,14]. One of our motivations for this work was to provide an approach that can protect against such problems in public monitoring systems, particularly the IMS.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Cha

Kim

2022

Computational Intelligence and Neuroscience

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Fully autonomous vehicles (FAVs) lack monitoring inside the cabin. Therefore, an in-cabin monitoring system (IMS) is required for surveilling people causing irregular or abnormal situations. However, monitoring in the public domain allows disclosure of an individual’s face, which goes against privacy preservation. Furthermore, there is a contrary demand for privacy in the IMS of AVs. Therefore, an intelligent IMS must simultaneously satisfy the contrary requirements of personal privacy protection and person identification during abnormal situations. In this study, we proposed a privacy-preserved IMS, which can reidentify anonymized virtual individual faces in an abnormal situation. This IMS includes a step for extracting facial features, which is accomplished by the edge device (onboard unit) of the AV. This device anonymizes an individual’s facial identity before transmitting the video frames to a data server. We created different abnormal scenarios in the vehicle cabin. Further, we reidentified the involved person by using the anonymized virtual face and the reserved feature vectors extracted from the suspected individual. Overall, the proposed approach preserves personal privacy while maintaining security in surveillance systems, such as for in-cabin monitoring of FAVs.

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“…WVSS has the advantages of remote and continuous monitoring [4]. The use of internet protocol (IP) based wireless closed-circuit television (CCTV) cameras is growing popular in the current context [5]- [8] due to technical aspects such as flexibility, ease of use, and cost. WVSS employed in a variety of applications all over the world.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1. Overview of wireless video surveillance system [8] These systems are vulnerable to unauthorized access attacks resulting in privacy and security issues; French web host OVH was exposed to distributed denial of service (DDoS) attack-Mirai [11] in September 2016, which was the most significant DDoS attack ever recorded. The source of the junk traffic was a botnet made up of 145,607 hacked digital video recorders and IP cameras, with the ability to generate traffic of 1 mbps to 30 mbps from every single IP address.…”

Section: Introductionmentioning

confidence: 99%

An IoT-fuzzy based password checker system for wireless video surveillance system

Jasim

Atia

2022

Bulletin EEI

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Wireless video surveillance systems (WVSS) are deployed in large environments for use in strategic places such as town centers, public streets, and airports and play an essential role in protecting critical infrastructure. However, WVSSs are vulnerable to unauthorized access due to weak login credentials, which leads to their exploitation to launch cyberattacks on other systems, such as distributed denial-of-service attacks. Hence, it is essential to secure these systems from unauthorized access. This paper proposes the Mamdani fuzzy inference system (FIS)-based password checker algorithm to estimate the password strength ratio (PSR) of internet protocol (IP) cameras and internet of things (IoT) devices. This algorithm composes three stages, the password extraction stage, which evaluates the input parameters of FIS from the real-time streaming protocol (RTSP) protocol using a counter of password characters. Then, the processing stage uses Mamdani FIS to optimize the input parameters to calculate the PSR. Finally, the alarm stage will notify the system administrator about weak IoT nodes. Unlike the existing approaches, this algorithm improves detection accuracy by informing the system administrator about threatened nodes. Extensive experiments are carried out to determine the efficiency of the proposed algorithm. The results confirm the efficiency of the proposed algorithm with high accuracy, which outperforms existing schemes.

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Attacks and Preventive Measures on Video Surveillance Systems: A Review

Cited by 34 publications

References 42 publications

In-Cabin Monitoring System for Autonomous Vehicles

In-Cabin Monitoring System for Autonomous Vehicles

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

An IoT-fuzzy based password checker system for wireless video surveillance system

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