Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.

IOT (Internet of things) is designed to provide friendly intelligent operation through big data analysis of information generated or collected from various of devices. However, the diversity of IOT devices make the IOT networks environment more complex, IOT networks are becoming more vulnerable to various of web attacks. In this paper, we proposed a novel web attack detection system (WADS). Specifically, we designed three deep learning models to detect web attacks separately and an ensemble classifier to make the final detection according to combing the results from those three deep learning models. In order to evaluate WADS, we conducted experiments on a public dataset and a real-word dataset and deployed it in a distributed environment. Experimental results on several datasets show that the proposed system can detect web attacks accurately with low false alarm rate and false negative rate

In this paper, we consider task-level scheduling algorithms with respect to budget and deadline constraints for a batch of MapReduce jobs on a set of provisioned heterogeneous (virtual) machines in cloud platforms. The heterogeneity is manifested in the popular “pay-as-you-go” charging model where the service machines with different performance would have different service rates. We organize the batch of jobs as a k-stage workflow and study two related optimization problems, depending on whether the constraints are on monetary budget or on scheduling length of the workflow. First, given a total monetary budget B, by combining an in-stage local greedy algorithm (whose optimality is also proven) and dynamic programming (DP) techniques, we propose a global optimal scheduling algorithm to achieve minimum scheduling length of the workflow within O(kB ² ). Although the optimal algorithm is efficient when B is polynomially bounded by the number of tasks in the MapReduce jobs, the quadratic time complexity is still high. To improve the efficiency, we further develop two greedy algorithms, called Global Greedy Budget (GGB) and Gradual Refinement (GR), each adopting different greedy strategies. In GGB we extend the idea of the local greedy algorithm to the efficient global distribution of the budget with minimum scheduling length as a goal whilst in GR we iteratively apply the DP algorithm to the distribution of exponentially reduced budget so that the solutions are gradually refined. Second, we consider the optimization problem of minimizing cost when the (time) deadline of the computation D is fixed. We convert this problem into the standard Multiple-Choice Knapsack Problem via a parallel transformation. Our empirical studies verify the proposed optimal algorithms and show the efficiencies of the greedy algorithms in cost-effectiveness to distribute the budget for performance optimizations of the MapReduce workflows.

Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beamwidth. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-squint (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques.