Voice-driven devices (VDDs) like Google Home and Amazon Alexa, which are well-known connected devices in consumer IoT, have applications in various domains i.e., home appliances automation, next-generation vehicles, voice banking, and so on. However, these VDDs that are based on automatic speaker verification systems (ASVs) are vulnerable to voice based logical access (LA) attacks like Text-to-Speech (TTS) synthesis and converted voice signals. Intruders can exploit these attacks to bypass the security of such systems and gain access of victim’s bank account or home control. Thus, there exists a need to develop an effective voice spoofing countermeasure that can reliably be used to protect these VDDs against such malicious attacks. This work presents a novel audio features descriptor named as extended local ternary pattern (ELTP) to capture the vocal tract dynamically induced attributes of bonafide speech and algorithmic artifacts in synthetic and converted speeches. We fused our novel ELTP features with the linear frequency cepstral coefficients (LFCC) to further strengthen the capability of our features for capturing the traits of bonafide and spoofed signals. We employ the proposed ELTP-LFCC features to train the deep bidirectional Long Short-Term Memory (DBiLSTM) network for classification of the bonafide and spoof signal (i.e., TTS synthesis, converted speech). Performance of our spoofing countermeasure is measured on the large-scale and diverse ASVspoof 2019 logical access dataset. Experimental results demonstrate that the proposed audio spoofing countermeasure can reliably be used to detect the LA spoofing attacks.

In recent decades, the traffic on road increased in a huge number. It is very important to manage the safety of the humans as well as to make an efficient flow of the traffic. To manage the traffic flow and to overcome from the situation of the traffic congestion the vehicle detection and counting needs a greater amount of accuracy. In this work, two different techniques are proposed that provides better performance in terms of F-Measure score and Error Ratio. The first technique is based on the foreground estimation while the second proposed technique is based on the training of dataset using a cascade classifier which is based on the Histogram of Oriented Gradients (HOG). Furthermore, four images are provided at once to the proposed system to count the vehicles and generate a signal that shows a greater number of vehicles in that image. The priority of each image will be set on the basics of greater number of vehicles present. The proposed techniques showed outstanding performance on a sunny and a cloudy day which is verified from the experimental results.

Software Defined Networking (SDN), as a cutting-edge network, splits control and management planes from the data plane for simplifying network manageability as well as programmability. In SDN, network policies change with the passage of time due to changes in the application environment, topology or user/admin requirements. As a result, modifications at the control plane take place. In existing research works, packet violations occur due to already installed flow rules at the data plane (switches) that are not modified in case of a change of the Access Control List (ACL) policies at the SDN controller. There has been no research carried out that identifies packet violations and network inefficiencies in case of policy change. Our novel approach stores generated flow rules at the SDN controller and detects policy change, along with conflicting flow rules, to resolve the identified problem of policy change. Afterwards, the conflicting flow rules are removed from switches and new flow rules are installed along the new path according to new ACL policies. It helps to minimize packet violations, which increases network efficiency. In this research work, we deal with the inefficiencies of policy change detection with respect to access time, cost and space. In this regard, we used abstractions to formalize and detect network policies with the help of multi-attributed graphs. We utilized intent-based policies for the representation and implementation of our proposed approach. In addition, we used extended performance metrics for the analysis of our proposed approach. The simulation results show that our proposed approach performs better as compared to the existing approach, by varying the number of policy change and packet transmission rate. The results clearly indicate that our proposed approach helps to increase network performance and efficiency.

Because of the rising population density, relationships are necessary to raise living standards through sending and receiving a wide range of services. Because of this, many means of object communication—regardless of their nature—are necessary to meet our daily needs. IoT is a network of physical things integrated with sensors, and software to communicate with each other. To establish a good connection, every object considered to be an associate of another object should meet certain requirements including scalability, interoperability, and trustworthiness. IoT security is a challenging task to protect the hardware and networks in the IoT system and a significant constraint to the deployment and realization of IoT. IoT security may include data confidentiality, authentication, access control, anonymity, and trust among services and products. Exchanging trust information is critical for assessing an entity’s trustworthiness. Therefore, trust information must be shared and stored securely to ensure reliability, honesty, and safety. We propose a secure trust management scheme built on blockchain technologies to secure the entire system in transparency, traceability, and material integrity. We implement a blockchain-based trust management architecture for smart buildings that collect node trust proof. It assigns a trust score to each node, securely stores them in an array, then the threshold value is computed using the ID3 Algorithm. IoT threshold value is broadcasted into the blockchain network and stored in the trusted list. According to the findings, our approach encompasses security measures such as tamper-proofing, attack resistance, reliability, and low functionality for IoT in smart buildings.

The development of a biomedical sensor involves an extremely sensitive optoelectronics system whose foundations lie on intracavity absoprtion spectroscopy. In order to make it convenient for technical reasons to use this application based utility, we implement dual mode competition. All of the components in this system must be investigated in detail. This work concerns with the design of the optical receiver which is mandatory to understand and analyze the received signal. This has been effectively done by designing an inexpensive system which replaces the existing Agilent based setup. Next, the performance of the system has been investigated by using an important parameter Relative Intensity Noise (RIN). The variation of injection current and temperature and their subsequent effect on RIN has been comprehensively examined. This has been enhanced by an investigation of RIN as subject to the mode positions (values of wavelengths) at which the framework is being operated. Through these considerations, the system’s behaviour is understood in a much better way as they lead to an improvement in the sensor’s realization with high sensitivity and stability.