Manu Sood

Bio: Manu Sood is an academic researcher from Himachal Pradesh University. The author has contributed to research in topics: Sybil attack & Software development. The author has an hindex of 11, co-authored 59 publications receiving 401 citations. Previous affiliations of Manu Sood include Jaypee University of Information Technology.

Clustering in MANET and VANET: A survey

Abstract: Vehicular Ad hoc Network (VANET) as a special class of Mobile Ad hoc Network (MANET) differs from the latter in a number of parameters. Clustering and Cluster Based Routing (CBR) protocols are the two important areas where a lot research work is in progress at present in both of these categories of networks, especially in VANET. The aim of this paper is to present a snapshot of clustering techniques and CBR protocols with a special focus on VANET since clusters ensure rapid and reliable data transmission.

Model Driven Architecture: A Perspective

Abstract: The current software development approaches that support developers in providing enterprise centric computing solutions have been falling short of expectations in handling some of the most trivial issues like changes in requirements, changing technologies, multiple platforms and platform interoperability. Model Driven Development (MDD) approach for software development is aimed at leveraging models to cater to these challenges. Model Driven Architecture (MDA) based on MDD and supported by UML, MOF and other standards is fast becoming a dominant approach for software development these days. This paper is an attempt to provide a state-of-the-art review of MDA concepts and summarizes various advantages and disadvantages of MDA.

Sybil attack on lowest id clustering algorithm in the mobile ad hoc network

Abstract: It is quite a challenging task to achieve security in a mobile ad hoc network because of its open nature, dynamically changing topology, lack of infrastructure and central management. A particular harmful attack that takes the advantage of these characteristics is the Sybil attack, in which a malicious node illegitimately claims multiple identities. This attack can exceedingly disrupt various operations of the mobile ad hoc networks such as data aggregation, voting, fair resource allocation scheme, misbehavior detection and routing mechanisms etc. Two routing mechanisms known to be vulnerable to the Sybil attack in the mobile ad hoc networks are multi-path routing and geographic routing. In addition to these routing protocols, we show in this paper that the Sybil attack can also disrupt the head selection mechanism of the lowest ID cluster-based routing protocol. To the best of our knowledge, this is for the first time that a Sybil attack is shown to disrupt this cluster based routing protocol. To achieve this, we illustrate to have introduced a category of Sybil attack in which the malicious node varies its transmission power to create a number of virtual illegitimate nodes called Sybil nodes, for the purpose of communication with legitimate nodes of the Mobile Ad Hoc Network. The variation in the transmission power makes the Sybil attack more deadly and difficult to be detected.

Detecting DDoS Attacks Using Machine Learning Techniques and Contemporary Intrusion Detection Dataset

Abstract: Recent trends have revealed that DDoS attacks contribute to the majority of overall network attacks. Networks face challenges in distinguishing between legitimate and malicious flows. The testing and implementation of DDoS strategies are not easy to deploy due to many factors like complexities, rigidity, cost, and vendor specific architecture of current networking equipment and protocols. Work is being done to detect DDoS attacks by application of Machine Learning (ML) models but to find out the best ML model among the given choices, is still an open question. This work is motivated by two research questions: 1) which supervised learning algorithm will give the best outcomes to detect DDoS attacks. 2) What would be the accuracy of training these algorithms on a real-life dataset? We achieved more than 96% accuracy in the case of Random Forest Classifier and validated our results using two metrics. The outcome was also compared with the other works to confirm its adequacy. We also present a detailed analysis to support our findings.

On Global Electricity Usage of Communication Technology: Trends to 2030

Abstract: This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worst-case scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.

Networking and Communications in Autonomous Driving: A Survey

Abstract: The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle’s perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.

Predicting academic performance: a systematic literature review

Abstract: The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.