Mshabab Alrizah

Menu
Facebook Twitter Youtube Github Medium Whatsapp

malrizah@gmail.com

LinkDroid: Reducing Unregulated Aggregation of App Usage Behaviors

Privacy /

Usage behaviors of different smartphone apps capture different views of an individual’s life, and are largely independent of each other. However, in the current mobile app ecosystem, a curious party can covertly link and aggregate usage behaviors of the same user across different apps. We refer to this as unregulated aggregation of app usage behaviors. […]

LinkDroid: Reducing Unregulated Aggregation of App Usage Behaviors Read More »

Privacy Threats through Ultrasonic Side Channels on Mobile Devices

Privacy /

Device tracking is a serious threat to the privacy of users, as it enables spying on their habits and activities. A recent practice embeds ultrasonic beacons in audio and tracks them using the microphone of mobile devices. This side channel allows an adversary to identify a user’s current location, spy on her TV viewing habits

Privacy Threats through Ultrasonic Side Channels on Mobile Devices Read More »

Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems Share on

Privacy /

Ad-blocking systems such as Adblock Plus rely on crowdsourcing to build and maintain filter lists, which are the basis for determining which ads to block on web pages. In this work, we seek to advance our understanding of the ad-blocking community as well as the errors and pitfalls of the crowdsourcing process. To do so,

Errors, Misunderstandings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems Share on Read More »

Embedded systems security: Threats, vulnerabilities, and attack taxonomy

CyberSecurity /

Embedded systems are the driving force for technological development in many domains such as automotive, healthcare, and industrial control in the emerging post-PC era. As more and more computational and networked devices are integrated into all aspects of our lives in a pervasive and “invisible” way, security becomes critical for the dependability of all smart

Embedded systems security: Threats, vulnerabilities, and attack taxonomy Read More »

MICSS: A Realistic Multichannel Secrecy Protocol

CyberSecurity /

Flaws in cryptosystem implementations, such as the Heartbleed bug, render common confidentiality mechanisms ineffective. Defending in depth when this happens would require a different means of providing confidentiality, which could then be layered with existing cryptosystems. This paper presents MICSS, a network protocol which uses multichannel secret sharing rather than encryption to protect data confidentiality.

MICSS: A Realistic Multichannel Secrecy Protocol Read More »

Detecting anomalous latent classes in a batch of network traffic flows

CyberSecurity /

We focus on detecting samples from anomalous latent classes, “buried” within a collected batch of known (“normal”) class samples. In our setting, the number of features for each sample is high. We posit and observe to be true that careful “feature selection” within unsupervised anomaly detection may be needed to achieve the most accurate results.

Detecting anomalous latent classes in a batch of network traffic flows Read More »

Deep learning in neural networks: An overview

Deep Learning /

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their *credit assignment paths*, which are chains of possibly learnable, causal

Deep learning in neural networks: An overview Read More »

Recurrent convolutional neural network for object recognition

Deep Learning /

In recent years, the convolutional neural network (CNN) has achieved great success in many computer vision tasks. Partially inspired by neuroscience, CNN shares many properties with the visual system of the brain. A prominent difference is that CNN is typically a feed-forward architecture while in the visual system recurrent connections are abundant. Inspired by this

Recurrent convolutional neural network for object recognition Read More »

Batch-normalized Maxout Network in Network

2 Comments / Deep Learning /

This paper reports a novel deep architecture referred to as Maxout network In Network (MIN), which can enhance model discriminability and facilitate the process of information abstraction within the receptive field. The proposed network adopts the framework of the recently developed Network In Network structure, which slides a universal approximator, multilayer perceptron (MLP) with rectifier

Batch-normalized Maxout Network in Network Read More »

Deep Learning using Linear Support Vector Machines

Deep Learning /

In this paper, they [have demonstrated] a small but consistent advantage of replacing the softmax layer with a linear support vector machine [in fully-connected and convolutional neural networks]. Learning minimizes a margin-based loss instead of the cross-entropy loss. While there have been various combinations of neural nets and SVMs in prior art, [their] results using

Deep Learning using Linear Support Vector Machines Read More »