Biblio
This paper studies the secure computation offloading for multi-user multi-server mobile edge computing (MEC)-enabled internet of things (IoT). A novel jamming signal scheme is designed to interfere with the decoding process at the Eve, but not impair the uplink task offloading from users to APs. Considering offloading latency and secrecy constraints, this paper studies the joint optimization of communication and computation resource allocation, as well as partial offloading ratio to maximize the total secrecy offloading data (TSOD) during the whole offloading process. The considered problem is nonconvex, and we resort to block coordinate descent (BCD) method to decompose it into three subproblems. An efficient iterative algorithm is proposed to achieve a locally optimal solution to power allocation subproblem. Then the optimal computation resource allocation and offloading ratio are derived in closed forms. Simulation results demonstrate that the proposed algorithm converges fast and achieves higher TSOD than some heuristics.
Today's companies are increasingly relying on Internet of Everything (IoE) to modernize their operations. The very complexes characteristics of such system expose their applications and their exchanged data to multiples risks and security breaches that make them targets for cyber attacks. The aim of our work in this paper is to provide an cybersecurity strategy whose objective is to prevent and anticipate threats related to the IoE. An economic approach is used in order to help to take decisions according to the reduction of the risks generated by the non definition of the appropriate levels of security. The considered problem have been resolved by exploiting a combinatorial optimization approach with a practical case of knapsack. We opted for a bi-objective modeling under uncertainty with a constraint of cardinality and a given budget to be respected. To guarantee a robustness of our strategy, we have also considered the criterion of uncertainty by taking into account all the possible threats that can be generated by a cyber attacks over IoE. Our strategy have been implemented and simulated under MATLAB environement and its performance results have been compared to those obtained by NSGA-II metaheuristic. Our proposed cyber security strategy recorded a clear improvment of efficiency according to the optimization of the security level and cost parametrs.
This work examines metrics that can be used to measure the ability of agile software development methods to meet security and privacy requirements of communications applications. Many implementations of communication protocols, including those in vehicular networks, occur within regulated environments where agile development methods are traditionally discouraged. We propose a framework and metrics to measure adherence to security, quality and software effectiveness regulations if developers desire the cost and schedule benefits of agile methods. After providing an overview of specific challenges that a regulated environment imposes on communications software development, we proceed to examine the 12 agile principles and how they relate to a regulatory environment. From this review we identify two metrics to measure performance of three key regulatory attributes of software for communications applications, and then recommend an approach of either tools, agile methods or DevOps that is best positioned to satisfy its regulated environment attributes. By considering the recommendations in this paper, managers of software-dominant communications programs in a regulated environment can gain insight into leveraging the benefits of agile methods.
One of the most efficient tool for human face recognition is neural networks. However, the result of recognition can be spoiled by facial expressions and other deviation from the canonical face representation. In this paper, we propose a resampling method of human faces represented by 3D point clouds. The method is based on a non-rigid Iterative Closest Point (ICP) algorithm. To improve the facial recognition performance, we use a combination of the proposed method and convolutional neural network (CNN). Computer simulation results are provided to illustrate the performance of the proposed method.
Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. This work enhances state-of-the-art neural style transfer techniques by a generalized user interface with interactive tools to facilitate a creative and localized editing process. Thereby, we first propose a problem characterization representing trade-offs between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, first user tests indicate different levels of satisfaction for the implemented techniques and interaction design.
With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.
We consider the problem of attack detection for IoT networks based only on passively collected network parameters. For the first time in the literature, we develop a blind attack detection method based on data conformity evaluation. Network parameters collected passively, are converted to their conformity values through iterative projections on refined L1-norm tensor subspaces. We demonstrate our algorithmic development in a case study for a simulated star topology network. Type of attack, affected devices, as well as, attack time frame can be easily identified.
Distributed consensus is a prototypical distributed optimization and decision making problem in social, economic and engineering networked systems. In collaborative applications investigating the effects of adversaries is a critical problem. In this paper we investigate distributed consensus problems in the presence of adversaries. We combine key ideas from distributed consensus in computer science on one hand and in control systems on the other. The main idea is to detect Byzantine adversaries in a network of collaborating agents who have as goal reaching consensus, and exclude them from the consensus process and dynamics. We describe a novel trust-aware consensus algorithm that integrates the trust evaluation mechanism into the distributed consensus algorithm and propose various local decision rules based on local evidence. To further enhance the robustness of trust evaluation itself, we also introduce a trust propagation scheme in order to take into account evidences of other nodes in the network. The resulting algorithm is flexible and extensible, and can incorporate more complex designs of decision rules and trust models. To demonstrate the power of our trust-aware algorithm, we provide new theoretical security performance results in terms of miss detection and false alarm rates for regular and general trust graphs. We demonstrate through simulations that the new trust-aware consensus algorithm can effectively detect Byzantine adversaries and can exclude them from consensus iterations even in sparse networks with connectivity less than 2f+1, where f is the number of adversaries.