As deep learning empowers various fields, many domain-specific non-neural network operators have been proposed to improve the accuracy of deep learning models. Researchers often use the imperative programming diagram (PyTorch) to express these new operators, leaving the fusion optimization of these operators to deep learning compilers. Unfortunately, the inherent side effects introduced by imperative tensor programs, especially tensor-level mutations, often make optimization extremely difficult. Previous works either fail to eliminate the side effects of tensor-level mutations or require programmers to manually analyze and transform them. In this paper, we present a holistic functionalization approach (TensorSSA) to optimizing imperative tensor programs beyond control flow boundaries. We first introduce TensorSSA intermediate representation for removing tensor-level mutation and expanding the scope and ability of operator fusion. Based on TensorSSA IR, we propose a TensorSSA conversion algorithm that performs functionalization crossing the boundary of control flow. TensorSSA achieves a 1.79X (1.34X on average) speedup in representative deep learning tasks than state-of-the-art works.

Mobile crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of smart devices to improve the sensing capacity and reduce sensing costs in large regions. Due to the ubiquitous nature of MCS services, smart devices require awareness of against misbehaving users that are becoming smarter to clog the resources in such a nondedicated sensing environment. In an MCS setting, the primary goal of a fake sensing task submission is to keep participant devices occupied, such as the battery, sensing, storage, and computing. Since the development of robust sensing campaigns highly depends on the existence of a realistic model of misbehaving users, this article leverages artificial intelligence and introduces a region-based self-organizing feature map (SOFM)-based model on user movement patterns so as to place the fake sensing tasks with the objective of maximum impacted participants and recruits. Uniformly and randomly initialized neurons are designed with fixed and adaptive quantities that are determined based upon the affected area on the covered terrain. Through numerical studies, we show that the impact of the SOFM structures can affect up to 46% of the participants and up to 37% of the recruits under various SOFM topologies. Furthermore, SOFM-based task submission models can increase the energy consumption in recruited devices by up to 39% due to the illegitimate task submission.

Mobile crowdsensing (MCS) is a ubiquitous sensing paradigm that emerged in the form of”sensed data as a service” model in the Internet of Things Era. Distributed nature of MCS results in vulnerabilities at the MCS platforms as well as participating devices that provide sensory data services. Submission of fake tasks with the aim of clogging sensing server resources and draining participating device batteries is a crucial threat that has not been investigated well. In this paper, we provide a detailed analysis by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. The experimental results show that the presented DBN-driven fake task mitigation detection of fake sensing tasks can ensure up to 0.92 accuracy, 0.943 precision and up to 0.928 F1 score outperforming prior work on MCS data with deep learning networks.

Non-dedicated nature of mobile crowdsensing (MCS) systems introduces vulnerabilities for MCS platforms in terms of sensing, computing, storage, and battery resources. The advent of adversarial artificial intelligence (AI) leads to high impact malicious behavior when adversaries aim to clog the resources of such a non-dedicated and ubiquitous system. This paper proposes an ensemble learning-based methodology for MCS platforms in order to mitigate the impacts of adversarial AI-driven fake task submission attacks, which are intelligently designed so to clog resources such as batteries, sensing, or memory resources. We validate our proposal through realistic simulations to generate crowdsensing data under two different cities, and intelligent fake task submissions under adversarial self-organizing maps. The experimental results show that when the submitted tasks undergo a Gradient Boosting-based classifier prior to being assigned to participants, the proposed solution can introduce battery savings at the participant devices up to 23%, and the impacted recruit population can be reduced from 24% to 6% whereas the defense mechanism can achieve an overall accuracy level above 98% concerning the legitimacy of the submitted tasks.

Clogging attacks in mobile crowdsensing (MCS) denote injection of fake sensing tasks into MCS campaigns in order to interfere with service ability and user participation in sensing campaigns. Due to the lack of a realistic location-based and energy-oriented clogging attack model in MCS, this type of attacks have not been well investigated. To this end, for the first time, we introduce a self organizing feature map (SOFM)-based clogging attack model that aims at maximizing the number of affected participants according to the location of attack zones. These zones are identified by clustering 2-D coordinates of all potential participants and finding out aggregation areas of their mobile devices. We evaluate and verify the introduced attack model via simulations by comparing it to an attack model that relies on random mobility of illegitimate tasks over the attack zones. Our simulation results demonstrate that SOFM-based modeling of clogging attacks in MCS results in a significant impact with almost 50% affected participant population, 24% affected recruitment decisions, and up to 28% energy overhead introduced by illegitimate tasks injected to the MCS campaigns.