Integration of Mobile and Web Application with Accident Detection System

Introduction: Traffic accidents are easy to occur in the tunnel due to its special environment, and the consequences are very serious. The existing vehicle accident detection system and CCTV system have the issues of low detection rate. Methods: A method of using Mel Frequency Cepstrum Coefficient (MFCC) to extract sound features and using a deep neural network (DNN) to learn sound features is proposed to distinguish accident sound from the non-accident sound. Results and Discussion: The experimental results show that the method can effectively classify accident sound and non-accident sound, and the recall rate can reach more than 78% by setting appropriate neural network parameters. Conclusion: The method proposed in this research can be used to detect tunnel accidents and consequently, accidents can be detected in time and avoid greater disasters.

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An Accident Detection System (ADS) for the Mumbai-Pune Expressway Using Vehicular Communication

Techno-Societal 2016 ◽

10.1007/978-3-319-53556-2_24 ◽

2017 ◽

pp. 239-247

Author(s):

Saurabh Patil ◽

Lata Ragha

Keyword(s):

Detection System ◽

Vehicular Communication ◽

Accident Detection

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Network Intrusion Detection and Prevention Systems for Attacks in IoT Systems

Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems - Advances in Digital Crime, Forensics, and Cyber Terrorism ◽

10.4018/978-1-5225-8241-0.ch006 ◽

2019 ◽

pp. 128-141 ◽

Cited By ~ 1

Author(s):

Vetrivelan Pandu ◽

Jagannath Mohan ◽

T. S. Pradeep Kumar

Keyword(s):

Machine Learning ◽

Web Application ◽

Detection System ◽

Security Threats ◽

Network Intrusion ◽

Security Personnel ◽

Prevention System ◽

Based Instruction ◽

Vast Network ◽

Intrusion Detection And Prevention

Internet of things (IoT) has transformed greatly the improved way of business through machine-to-machine (M2M) communications. This vast network and its associated technologies have opened the doors to an increasing number of security threats which are dangerous to IoT and 5G wireless networks. The first part of this chapter presents instruction detection system (IDS) which detect the various attacks in 6LoWPAN layer. An IDS is to detect and analyze both inbound and outbound network traffic for abnormal activities. An IPS complements an IDS configuration by proactively inspecting a system's incoming traffic to weed out malicious requests. A typical IPS configuration uses web application firewalls and traffic filtering solutions to secure applications. An IPS prevents attacks by dropping malicious packets, blocking offending IPs and alerting security personnel to potential threats. Machine learning (ML)-based instruction detection and prevention system (IDPS) is proposed and implemented in Contiki simulation environment.

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