Amit Parmar - Academia.edu (original) (raw)
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Papers by Amit Parmar
Indian Journal of Computer Science and Engineering
Malware detection has gained huge attention in recent times. This is mainly because of the increa... more Malware detection has gained huge attention in recent times. This is mainly because of the increase in new malware variants which pose a significant threat to information security. The conventional malware detection systems are not capable of detecting new generation malwares due to the constant changes in the network behavior. An efficient malware detection approach must be able to handle the dynamic changes in the malware behavior with a very minimum processing time to identify malicious attacks at the initial stage. This paper presents a novel Performance Importance Weighted Random Forest (PERI-WRF) for detecting different types of malwares in network systems. The proposed PERI-WRF incorporates a novel data reduction technique which is capable of reducing the size of training data to maximize the classification accuracy. A clustering algorithm consisting of GWO and K-means++ algorithm is implemented to group the malicious data samples collected from input. To validate the effectiveness of the detection framework, the system was tested using various evaluation metrics. Results show that the proposed malware detection model with novel data reduction techniques achieves superior classification accuracy, and the proposed approach is appropriate for detecting real-time malwares with superior accuracy and low MAE score.
International Journal of Advance Engineering and Research Development, 2014
Vehicular Ad-Hoc Network (VANET) is surging in popularity, in which vehicles constitute the mobil... more Vehicular Ad-Hoc Network (VANET) is surging in popularity, in which vehicles constitute the mobile nodes in the network. Due to the prohibitive cost of deploying and implementing such a system in real world, most research in VANET relies on simulations for evaluation. A key component for VANET simulations is a realistic vehicular mobility model that ensures that conclusions drawn from simulation e xperiments will carry through to real deployments. In this work, we introduce a tool MOVE that allows users to rapidly generate realistic mobility models for VANET simulations. MOVE is built on top of an open source micro-traffic simulator SUMO. The output of MOVE is a realistic mobility model and can be immediately used by popular network simulators such as ns-2 and qualnet.TraCI is the short term for "Traffic Control Interface". Giving the access to a running road traffic simulation, it allows to retrieve values of simulated objects and to manipulate their behavior "on-line" Vehicular Ad-Hoc Networks (VANETs) enable communication among vehicles as well as between vehicles and roadside infrastructures. Currently available software tools for VANET research still lack the ability to assess the usability of vehicular applications. In this article, we present Traffic Control Interface (TraCI) a technique for interlinking road traffic and network simulators. It permits us to control the behavior of vehicles during simulation runtime, and consequently to better understand the influence of VANET applications on traffic patterns. We describe the basic concept, design decisions and the message format of this open-source architecture. Additionally, we provide implementations for non-commercial traffic and network simulators namely SUMO and ns2, respectively. This coupling enables for the first time systematic evaluations of VANET applications in realistic settings.
Indian Journal of Computer Science and Engineering
Malware detection has gained huge attention in recent times. This is mainly because of the increa... more Malware detection has gained huge attention in recent times. This is mainly because of the increase in new malware variants which pose a significant threat to information security. The conventional malware detection systems are not capable of detecting new generation malwares due to the constant changes in the network behavior. An efficient malware detection approach must be able to handle the dynamic changes in the malware behavior with a very minimum processing time to identify malicious attacks at the initial stage. This paper presents a novel Performance Importance Weighted Random Forest (PERI-WRF) for detecting different types of malwares in network systems. The proposed PERI-WRF incorporates a novel data reduction technique which is capable of reducing the size of training data to maximize the classification accuracy. A clustering algorithm consisting of GWO and K-means++ algorithm is implemented to group the malicious data samples collected from input. To validate the effectiveness of the detection framework, the system was tested using various evaluation metrics. Results show that the proposed malware detection model with novel data reduction techniques achieves superior classification accuracy, and the proposed approach is appropriate for detecting real-time malwares with superior accuracy and low MAE score.
International Journal of Advance Engineering and Research Development, 2014
Vehicular Ad-Hoc Network (VANET) is surging in popularity, in which vehicles constitute the mobil... more Vehicular Ad-Hoc Network (VANET) is surging in popularity, in which vehicles constitute the mobile nodes in the network. Due to the prohibitive cost of deploying and implementing such a system in real world, most research in VANET relies on simulations for evaluation. A key component for VANET simulations is a realistic vehicular mobility model that ensures that conclusions drawn from simulation e xperiments will carry through to real deployments. In this work, we introduce a tool MOVE that allows users to rapidly generate realistic mobility models for VANET simulations. MOVE is built on top of an open source micro-traffic simulator SUMO. The output of MOVE is a realistic mobility model and can be immediately used by popular network simulators such as ns-2 and qualnet.TraCI is the short term for "Traffic Control Interface". Giving the access to a running road traffic simulation, it allows to retrieve values of simulated objects and to manipulate their behavior "on-line" Vehicular Ad-Hoc Networks (VANETs) enable communication among vehicles as well as between vehicles and roadside infrastructures. Currently available software tools for VANET research still lack the ability to assess the usability of vehicular applications. In this article, we present Traffic Control Interface (TraCI) a technique for interlinking road traffic and network simulators. It permits us to control the behavior of vehicles during simulation runtime, and consequently to better understand the influence of VANET applications on traffic patterns. We describe the basic concept, design decisions and the message format of this open-source architecture. Additionally, we provide implementations for non-commercial traffic and network simulators namely SUMO and ns2, respectively. This coupling enables for the first time systematic evaluations of VANET applications in realistic settings.