Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases

Machine learning is successful in many applications including securing a network from unseen attack. The application of learning algorithm for detecting anomaly in a Network has been fundamental since few years. With increasing use of machine learning techniques it has become important to study to what extent it is good to be dependent on them. Altogether a different discipline called ‘Adversarial Learning’ have come up as a separate dimension of study. The work in this paper is to test the robustness of online machine learning based IDS to carefully crafted packets by attacker called poison packets. The objective is to observe how a remote attacker can deviate the normal behavior of machine learning based classifier in the IDS by injecting the network with carefully crafted packets externally, that may seem normal by the classification algorithm and the instance made part of its future training set. This behavior eventually can lead to a poison learning by the classification algorithm in the long run, resulting in misclassification of true attack instances. This work explores one such approach with SOM and SVM as the online learning based classification algorithms.

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Are Machine Learning Based Intrusion Detection System Always Secure?: An insight into tampered learning

This paper describes the design and analysis of a modified E-shaped and a crown-shaped microstrip patch antenna with excitation frequency of 1.2 GHz. Both the antennas are derived from a Rogers RT/Duroid 6202 laminate substrate (dielectric constant = 2.94 and loss tangent = 0.001). The simulation is achieved using the software Ansoft Nexxim. From the simulation results it is observed that the crown-shaped antenna exhibits 20.15 dB better return loss and 50% more gain than the modified E-shaped antenna. Furthermore, better impedance matching is observed for the crown-shaped antenna exhibiting that the crown-shaped antenna finds better applications than the modified E-shaped antenna to be effectively used for wireless application.

A Crown-Shaped Microstrip Patch Antenna for Wireless Communication Systems

The ACM Multimedia 2022 Stuttering Challenge is to determine the stuttering-related class of a speech segment. There are seven stuttering-related classes and an eighth garbage class. For this purpose, we have investigated the Wav2Vec 2.0 deep neural network to extract audio features from Transformer embeddings. Experiments were conducted on a part of the Kassel State of Fluency Corpus (KSoF). First, we introduced 21 functionals allowing to define two large composite audio feature: the first one set called W2V2 Basic audio-feature set (193,536 features) from the Base version of Transformer embeddings and the second one called W2V2 Large audio-feature set (516, 096 features) from the Large version of Transformer embeddings. Some functionals aim at estimating the spatial variability (e.g., mean, standard deviation, quartiles) and others aim at the temporal variability (e.g., linear regression slope). Then, each composite audio feature set have been splitted into a set of audio feature sets by grouping audio features by functional and by layer. Then, the most discriminant audio feature sets have been selected from these audio feature sets. Finally, two audio features sets specializing in stuttering speech have been developed and assessed: W2V2 Advanced audio-feature set (9,984 features) and W2V2 Large Advanced audio-feature set (15,360 features). Experiments have shown an improvement of 9.3% to the first one and 11.9% for the second one on the Test set compared to the official baseline of the Challenge (40.4%).

Audio Features from the Wav2Vec 2.0 Embeddings for the ACM Multimedia 2022 Stuttering Challenge

Circuit simulators have the capability to create virtual environment to test circuit design. Simulators save time and hardware cost. However, when components in circuit design increase, most simulators take longer time to test large circuit design, in many cases days or even weeks. Therefore, to handle large dataset and accurate performance, simulators need to be improved. In this paper, we propose machine learning-based parallel implementations of circuit analyser on graphics card with Compute Unified Device Architecture (CUDA). After parsing netlist file, the first approach is to analyse compute intensive mathematical functions and then convert it into parallel executable version. Further, we propose a Design-Level Parallelism with hybrid parallel implementation of components and processing methods. Dynamic decision-making is required to select functions and parameters to map on Graphics Processing Unit (GPU). To reduce load overhead, machine learning clustering approach has been adopted. Combination of procedure clustering and mapping takes few cycles but overall performance enhances efficiency as compared to serial processing.

https://link.springer.com/content/pdf/10.1007/s42452-020-2667-6.pdf

Nabendu Chaki

Papers

Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics