Website fingerprinting attacks leverage encrypted traffic features to identify specific services accessed by users within anonymity networks such as Tor. Although existing WF methods achieve high accuracy on static datasets using deep learning techniques, they struggle in dynamic environments where anonymous websites continually evolve. These methods typically require full retraining on composite datasets, resulting in substantial computational and storage burdens, and are particularly vulnerable to classification bias caused by data imbalance and concept drift. To address these challenges, we propose EIL-WF, a dynamic WF framework based on incremental learning that enables efficient adaptation to newly emerging websites without the need for full retraining. EIL-WF incrementally trains lightweight, independent classifiers for new website classes and integrates them through classifier normalization and energy alignment strategies grounded in energy-based model theory, thereby constructing a unified and robust classification model. Comprehensive experiments on two public Tor traffic datasets demonstrate that EIL-WF outperforms existing incremental learning methods by 6.2%–20.2% in identifying new websites and reduces catastrophic forgetting by 5.4%–20%. Notably, EIL-WF exhibits strong resilience against data imbalance and concept drift, maintaining stable classification performance across evolving distributions. Furthermore,EIL-WF decreases training time during model updates by 2–3 orders of magnitude, demonstrating substantial advantages over conventional full retraining paradigms.

This article explores the problem of protection against false data injection attacks (FDIAs) on the power system state estimation. Although many research works have been reported previously to solve the same problem, yet most of them are only for perfect FDIAs. To address the problem reasonably, all related factors influencing the success probability and corresponding attack impact of imperfect FDIAs should also be considered. Based on such considerations, a topology, parameter, accuracy, level (TOTAL) protection strategy considering all corresponding factors is proposed. The TOTAL protection strategy minimizes the attack impact of typical imperfect FDIAs (single measurement attacks) while defending against typical perfect FDIAs (single-state variable attacks). Depending on whether the protection scheme contains phasor measurement units (PMUs), we formulate the meter selection as a linear binary programming or integer programming problem, which can be solved by suitable solvers. The proposed strategy is compared with the existing methods in the literature and evaluated using the standard IEEE test cases.

With the fast increase in airspace density and high‐safety requirements on aviation, automatic dependent surveillance‐broadcast (ADS‐B) is regarded as the primary method in the next generation air traffic surveillance. The ADS‐B data is broadcast with the plain text without sufficient security measures, which results in various attack patterns emerging. However, in terms of constrictions with laws and regulations, ADS‐B attack data is difficult to collect and obtain, which is essential for data security research studies. To deal with the absence of ADS‐B attack data in real environments, the construction strategy on ADS‐B attack data is proposed. For construction fidelity, ADS‐B data features are analysed and modelled at first. Then the popular and classical attack patterns on ADS‐B data are analysed to establish threat models. Based on the original ADS‐B data sets, the construction strategy is designed to focus on attack target selection, key parameter determination, and mixture strategy, reproducing the attack intentions. The constructed ADS‐B attack data sets are hybrid data sets including the normal and attack data. By simulation analyses, the feasibility and availability of the construction strategy were validated with real ADS‐B data.

The key idea of non-orthogonal multiple access (NOMA) is to serve multiple users in the same resource block to improve the spectral efficiency. Whereas due to the resource sharing, a security flaw of NOMA emerges in the presence of internal untrusted users, especially untrusted near users who are closer to the base station and can easily access the confidential information for paired far users. To mitigate the flaw, in this paper, we investigate the reliable and secure transmission of NOMA systems with untrusted near users, and propose joint beamforming and power allocation (JBP) scheme for the scenario. Meanwhile, from security point of view, we extend to a worse-case scenario where both untrusted near users and external eavesdroppers exist, and propose joint artificial noise aided beamforming and power allocation (JANBP) scheme to achieve a reliable and secure transmission for the scenario. The exact and asymptotic closed-form expressions of secrecy outage probability (SOP) for the two scenarios are derived to evaluate the secrecy performance achieved by the proposed schemes, respectively. The analysis and simulation results show the superiority of the proposed JBP and JANBP schemes in terms of combating internal and external eavesdropping, and also indicate the two schemes can achieve the same SOP at high SNR.

Location verification is a promising approach among various ADS-B security mechanisms, which can monitor announced positions in ADS-B messages with estimated positions. Based on common assumption that the attacker is equipped with only a single device, this mechanism can estimate the position state through analysis of time measurements of messages using multilateration algorithm. In this paper, we propose the formal model of multidevice false data injection attacks in the ATC system against the location verification. Assuming that attackers equipped with multiple devices can manipulate the ADS-B messages in distributed receivers without any mutual interference, such attacker can efficiently construct attack vectors to change the results of multilateration. The feasibility of a multidevice false data injection attack is demonstrated experimentally. Compared with previous multidevice attacks, the multidevice false data injection attacks can offer lower cost and more covert attacks. The simulation results show that the proposed attack can reduce the attackers’ cost by half and achieve better time synchronization to bypass the existing anomaly detection. Finally, we discuss the real-world constraints that limit their effectiveness and the countermeasures of these attacks.