arXiv subjects

Seyedakbar Mostafavi

Publications and source records attributed to Seyedakbar Mostafavi.

6 recordsLinked to original sources

arXiv2026

Trustworthy Agentic AI: A Comprehensive Cybersecurity and Systems Survey on Threat Landscapes, Defense Architectures, and Open Challenges

The transition from passive foundation models to autonomous, goal-directed agentic AI systems has introduced unprecedented capabilities by coupling recursive cognitive reasoning loops, persistent memory architectures, live tool execution planes, and multi-agent collaboration topologies. However, granting probabilistic neural cores execution authority across filesystems, networks, and cloud infrastructure dissolves classical security perimeters: natural language simultaneously serves as input data, internal control code, and communication protocols, exposing a Turing-complete blast radius where untrusted data represents executable instructions. This survey delivers a comprehensive systems-security reference framework for trustworthy agentic AI, synthesizing 206 foundational studies and regulatory standards. We formalize the general agent architecture as a stateful 5-tuple and establish a 6-dimensional trustworthiness taxonomy covering security, safety, privacy, explainability, fairness, and accountability. We systematically analyze threat surfaces across intra-execution loops and interaction planes, formulate a multi-layered zero-trust defense-in-depth architecture integrating Dual-LLM isolation, Capability-Based Access Control, kernel eBPF probes, and sandboxed runtimes, review standardized evaluation benchmarks, and map technical controls to international AI governance frameworks.

cs.AI↗

arXiv2026

Trustworthy mobile edge caching: a blockchain approach to mitigate malicious nodes and incentivize cache sharing

As mobile network traffic continues to grow, content caching on edge servers is critical for reducing latency. However, challenges such as malicious edge servers that may delete or manipulate cached content, along with the limited capacity of these servers, need to be addressed. To overcome the capacity limitations, helper mobile nodes can contribute their cache resources. However, due to their selfish behavior, an incentive mechanism is necessary to encourage resource sharing. Additionally, these helper nodes can also be malicious. This paper proposes a blockchain-based trust management mechanism that addresses these challenges by accurately identifying trustworthy edge servers and mobile nodes. The proposed mechanism calculates both direct and indirect trust using smart contracts, ensuring that malicious nodes are effectively filtered out. Trustworthiness is determined based on mobile node satisfaction with the quality of service, and trust data is securely stored on the blockchain. To combat node selfishness, a reward mechanism is introduced to incentivize cache sharing. Furthermore, a blockchain-based authentication mechanism protects against node impersonation. Our approach optimizes trust, cache capacity, and cost efficiency while considering mobile node mobility, energy consumption, and computational power constraints during the consensus process. Simulation results show that the proposed method can accurately distinguish between honest and malicious servers, even with a 10% noise in data.

cs.CR↗

arXiv2021

Game theory and Evolutionary-optimization methods applied to resource allocation problems in emerging computing environments: A survey

Today's intelligent computing environments, including Internet of Things, cloud computing and fog computing, allow many organizations around the world to optimize their resource allocation regarding time and energy consumption. Due to the sensitive conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet been able to provide a robust and reliable capability for proper resource allocation. Although, traditional methods of resource allocation in a low-capacity hardware resource system are efficient for small-scale resource providers, for a complex system in the conditions of dynamic computing resources and fierce competition in obtaining resources, they do not have the ability to develop and adaptively manage the conditions optimally. To solve this problem, computing intelligence techniques try to optimize resource allocation with minimal time delay and energy consumption. Therefore, the objective of this research is a comprehensive and systematic survey on resource allocation problems using computational intelligence methods under Game Theory and Evolutionary-optimization in emerging computing environments, including cloud, fog and Internet of Things according to the latest scientific-research achievements.

cs.DC↗

arXiv2020

A Stochastic Approximation Approach for Foresighted Task Scheduling in Cloud Computing

With the increasing and elastic demand for cloud resources, finding an optimal task scheduling mechanism become a challenge for cloud service providers. Due to the time-varying nature of resource demands in length and processing over time and dynamics and heterogeneity of cloud resources, existing myopic task scheduling solutions intended to maximize the performance of task scheduling are inefficient and sacrifice the long-time system performance in terms of resource utilization and response time. In this paper, we propose an optimal solution for performing foresighted task scheduling in a cloud environment. Since a-priori knowledge from the dynamics in queue length of virtual machines is not known in run time, an online reinforcement learning approach is proposed for foresighted task allocation. The evaluation results show that our method not only reduce the response time and makespan of submitted tasks, but also increase the resource efficiency. So in this thesis a scheduling method based on reinforcement learning is proposed. Adopting with environment conditions and responding to unsteady requests, reinforcement learning can cause a long-term increase in system's performance. The results show that this proposed method can not only reduce the response time and makespan but also increase resource efficiency as a minor goal.

cs.DC↗

arXiv2019

A new rank-order clustering algorithm for prolonging the lifetime of wireless sensor networks

Energy efficient resource management is critical for prolonging the lifetime of wireless sensor networks (WSN). Clustering of sensor nodes with the aim of distributing the traffic loads in the network is a proven approach for balanced energy consumption in WSN. The main body of literature in this topic can be classified as hierarchical and distance-based clustering techniques in which multi-hop, multi-level forwarding and distance-based criteria, respectively, are utilized for categorization of sensor nodes. In this study, we propose the Approximate Rank-Order Wireless Sensor Networks (ARO-WSN) clustering algorithm as a combined hierarchical and distance-based clustering approach. ARO-WSN algorithm which has been extensively used in the field of image processing, runs in the order of O(n) for a large data set, therefore it can be applied on WSN. The results shows that ARO-WSN outperforms the classical LEACH, LEACH-C and K-means clustering algorithms in the terms of energy consumption and network lifetime.

cs.DC↗

arXiv2014

Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems

In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus bandwidth resources act as micro-servers to compensate the server deficiencies in balancing the resources between different channel overlays. With deployment of helper level between server and peers, optimizing the user/helper topology becomes a challenging task since applying well-known reciprocity-based choking algorithms is impossible due to the one-directional nature of video streaming from helpers to users. Because of selfish behavior of peers and lack of central authority among them, selection of helpers requires coordination. In this paper, we design a distributed online helper selection mechanism which is adaptable to supply and demand pattern of various video channels. Our solution for strategic peers' exploitation from the shared resources of helpers is to guarantee the convergence to correlated equilibria (CE) among the helper selection strategies. Online convergence to the set of CE is achieved through the regret-tracking algorithm which tracks the equilibrium in the presence of stochastic dynamics of helpers' bandwidth. The resulting CE can help us select proper cooperation policies. Simulation results demonstrate that our algorithm achieves good convergence, load distribution on helpers and sustainable streaming rates for peers.

cs.NI↗