Timely anomaly detection of key performance indicators (KPIs), e.g. , service response time, error rate, is of utmost importance to Web services. Over the years, many unsupervised deep learning-based anomaly detection approaches have been proposed. To achieve good performance, they require a long period of KPI data for model training, which is not easy to guarantee with frequent service changes. Additionally, the training overhead is too significant for the vast number of KPIs in large-scale Web services. To address the problems, we propose an unsupervised KPI anomaly detection approach, named AnoTransfer , by combining a novel Variational Auto-Encoder (VAE)-based KPI clustering algorithm with an adaptive transfer learning strategy. Extensive evaluation experiments using real-world data collected from several large-scale Web service providers demonstrate that AnoTransfer reduces the average initialization time by 65.71% and improves the training efficiency by 50.62 times, without significantly degrading anomaly detection accuracy.

To bridge the gap of conventional single-hop task offloading schemes in infrastructure-free scenarios, multi-hop task offloading schemes for IoT devices in Mobile Edge Computing (MEC) are desired to jointly optimize task offloading decisions and routing paths. In this paper, we investigate a hierarchical multi-hop edge computing framework and propose a joint Task Offloading and Relay Selection (TORS) scheme. It considers real-time computation at each relay node and employs directional searches to facilitate the task execution and results reporting at the fastest speed. However, finding the optimal TORS solution is a formidable challenge due to the time-varying network environments, the strong interdependence of decision sets across different time slots, and the high computational complexity. To address these challenges, we first leverage Lyapunov optimization to transform the stochastic TORS problem into a deterministic per-slot block problem, avoiding the need for extensive system prior knowledge. Subsequently, we propose a Soft Actor-Critic (SAC)-based algorithm, SAC-TORS, to find a satisfactory TORS solution with minimal computational complexity in a distributed manner. Accordingly, each IoT device can independently make self-determined and directional decisions with observable network information. Through extensive experiments, we demonstrate that the SAC-TORS outperforms state-of-the-art solutions, achieving performance improvements of up to 66%.

With the rapid growth of social network traffic, the design of an efficient caching strategy is crucial in the social content-centric network (SocialCCN). In order to design a more comprehensive popularity prediction caching strategy, in this letter, we proposed a novel architecture that integrates mobile edge computing (MEC) in SocialCCN (MeSoCCN) and proposed multi-head attention based popularity prediction caching strategy in MeSoCCN. Firstly, we proposed a multi-head attention based popularity prediction model (MAPP) that considers multidimensional features including history and future popularity, social relationships, and geographic location to predict content popularity. Then, we design a caching strategy based on the prediction results of MAPP. The simulation results show that the proposed MAPP model achieves lower predictive error and the proposed predictive caching strategy improves cache hit rate and reduces hop redundancy in the network.

Despite being widely used in network intrusion detection systems (NIDSs), machine learning (ML) has proven to be vulnerable to adversarial attacks. White-box and black-box adversarial ML attacks of NIDS have been explored in several studies. However, white-box attacks unrealistically assume that the attackers have full knowledge of the target NIDSs. Meanwhile, existing black-box attacks can not achieve high attack success rate due to the weak adversarial transferability between models (e.g., neural networks and tree models). Additionally, neither of them explains why adversarial examples exist and why they can transfer across models. To address these challenges, this paper introduces ETA, an Explainable Transfer-based Black-Box Adversarial Attack framework. ETA aims to achieve two primary objectives: 1) create transferable adversarial examples applicable to various ML detectors and 2) provide insights into the existence of adversarial examples and their transferability within NIDSs. Specifically, we first provide a general transfer-based adversarial attack method applicable across the entire ML space. Following that, we exploit a unique insight based on cooperative game theory and perturbation interpretations to explain adversarial examples and adversarial transferability. On this basis, we propose an Important-Sensitive Feature Selection (ISFS) method to guide the search for adversarial examples, achieving stronger transferability and ensuring traffic-space constraints. Finally, the experimental results on three NIDSs datasets show that our method performs significantly effectively against several classical and state-of-the-art ML classifiers, outperforming the latest baselines. We conduct three interpretation experiments and two cases to verify our interpretation method's correctness. Meanwhile, we uncover two major misconceptions about applying machine learning to NIDSs systems.

Recently, the task offloading and resource allocation (TORA) issues in mobile edge computing system have attracted much attention. However, the impact of different movement speeds on TORA problem and customized services for different service requirements have been largely ignored. These may severely influence the user’s quality of service (QoS). Accordingly, a speed-aware and customized TORA scheme is proposed to minimize total service latency (consisted of execution delay and migration cost) for mobile users. First, the Advantage Actor Critic (A2C) algorithm is applied to dynamically decide on which computation node to execute the tasks. Second, we assign the user’s priorities based on movement speeds and tolerable delay to allocate the wireless and computation resources respectively. Finally, the migration cost is introduced into objective model for further weakening the impact of service migration on user’s QoS. Simulation results demonstrate the superiority and effectiveness of the proposed algorithm compared with the baselines.