Sequential Mathematical Programming With \(\zeta\)-Analysis for HESVM | Chapter 9 | Mathematics and Computer Science: Contemporary Developments Vol. 7

Vapnik's quadratic programming (QP)-based support vector machine (SVM) is a state-of-the-art powerful classifier with high accuracy while being sparse. Moving one step further in the direction of sparsity, Vapnik proposed one more SVM that uses linear programming (LP) for the cost function. This machine, compared with the complex QP based one, is more sparse but offers similar accuracy, which is essential to work on any large dataset. However, further sparsity is optimum for computational savings as well as to work with very large and complicated datasets. Producing even more sparsity without reducing generalization capability of a detector is extremely challenging. In this dimension, we apply a distinct sequence of Mathematical Programming followed by slack variable analysis that leads to an exceptionally fast and accurate SVM based detector. Being immensely sparse and optimally complex, this Highly Effcient SVM (HESVM) can expertly work on very large and noise-effected complicated data. Experiments on Benchmark data shows that HESVM requires kernel execution as little as 6.8% of the classical QP based SVM while producing nearly the same classification accuracy on test data and demanding 42.7, 27.7 and 46.6% that of other three executed cutting-edge heavy-sparse machines while posing similar classification accuracy. It also claims the least Machine Accuracy Cost (MAC) value among all of these machines though producing very similar generalization performance, which is calculated statistically using the term Generalization Failure Rate (GFR). Being quite practical for contemporary technological development, it has become indispensable for optimum manipulation of the troublesome massive, and diffcult data.

 

Author (s) Details

 

Rezaul Karim
Uttara University, Bangladesh.

 

Amit Kumar Kundu
Uttara University, Bangladesh.

 

Ali Ahmed Ave
Uttara University, Bangladesh.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v7/2725

Cloning Mac Address Results Review: Advanced Research | Chapter 12 | Research Updates in Mathematics and Computer Science Vol. 1

Network interface cards come from the factory with a unique MAC address associated with the hardware. MAC is unique, but now we know the way of making MAC address clone of any PC or Router. Every network interface has a media access control (MAC) address. Most network cards and routers allow one to set a custom MAC address, overriding the MAC address present in the hardware. On the other hand if the ISP blocked the Mac address of the original device, changing Mac address is efficient way to communicate to the internet. Another function of cloning MAC address is used to jam the network with the IP address conflict associated with two devices.

Author(s) Details:

Md. Abdullah Yusuf Imam,
Department of ICT, National University, Gazipur-1704, Bangladesh.

Prodip Kumar Biswas,
Department of ICT, National University, Gazipur-1704, Bangladesh.

Please see the link here: https://stm.bookpi.org/RUMCS-V1/article/view/13679

MAC Address Cloning Technique Results | Chapter 8 | Contemporary Perspective on Science, Technology and Research Vol. 2

 Desktop computer address cloning causes the Docker canister to use the MAC address of the host, and the host to use the MAC address of either an address you designate or a randomly produce one. Desktop computer filtering is not an effective hole or door in vessel wireless socializing for professional or personal gain as attackers cannot search easily Desktop computer address on wireless transmissions mode. In another way, in a wired network, Desktop computer filtering everything better since it is simpler for an aggressor to determine an certified MAC address. All LAN sheet has a unique MAC address assigned to it. In this way, Changeful MAC address should in network virtualization. It can also be used while exploiting freedom of a system. By replicating MAC address makes individual device unseen from outsider, similarly cloning MAC address form a network vulnerable. It is main to choice who wants commotion what. In IP networks, the MAC address of an interface is query likely the IP address using the Address Judgment Protocol (ARP) for Computer network Protocol Version 4 (IPv4) or the Neighbor Finding Protocol (NDP) for IPv6. ARP and NDP are used to pertain IP addresses (OSI layer 3) to Ethernet Desktop computer addresses (OSI layer 2). Security maybe enhanced through Desktop computer address cloning cause it enables you to use a loyal virtual NIC for the Admin Network, Grid Network, and Customer Network. Having the Docker capsule use the MAC address of the loyal NIC on the host allows you to avoid utilizing promiscuous style network configurations.

Author(s) Details:

Md. Abdullah Yusuf Imam,
Department of ICT, National University, Gazipur-1704, Bangladesh.

Prodip Kumar Biswas,
Department of ICT, National University, Gazipur-1704, Bangladesh.

Please see the link here: https://stm.bookpi.org/CPSTR-V2/article/view/12865

Accurate Detection of Multi-layer Packet Dropping Attacks in MANET| Chapter 7 | Research Highlights in Mathematics and Computer Science Vol. 3

 Mobile Ad Hoc Networks grant permission experience significant convert overhead while trying to discover multi-layer packet drop attacks (MANET). The most of current efforts only take data small drops into account while discounting routing packet drops. Using travelling agents (MPDDMA), a method for exact detection of hateful multi-layer packet drop attacks is submitted in this paper. In this method, movable agents are installed in each bud to look for the hateful nodes' selective abandoning of data and routing packets. The beginning node identities the knots whose route request and route reply count significantly differs from possible choice by a margin. The source bud similarly perceives nodes whose packet taken count considerably varies from possible choice by a different border. Using these margin values and the MAC tier packet drop value as recommendation variables, the source before uses a fuzzy logic conclusion model, returning the output as the anticipation of malicious (PrM). The outcomes of the experiments display that the MPDDMA approach improves discovery accuracy and reduces packet deficits.

Author(s) Details:

Mythili Boopathi,
School of Information Technology and Engineering, VIT University, India.

Please see the link here: https://stm.bookpi.org/RHMCS-V3/article/view/8879