arXiv subjects

Junhua Tang

Publications and source records attributed to Junhua Tang.

5 recordsLinked to original sources

arXiv2026

Fixed Detectors, Moving Code: How Well Do Static Security Analyzers Hold Up over a Decade of C/C++ Evolution?

Static application security testing (SAST) tools are often deployed on the assumption that detectors configured today will remain effective on future code, including previously unknown vulnerabilities. We test this assumption using 20,000 vulnerable and fixed C/C++ cases from 1,116 open-source projects (2016-2025), evaluated with three CodeQL releases, three Infer releases, and one Semgrep configuration. To distinguish temporal effects from shifts in code composition, we compare yearly populations, project- and label-matched year pairs, and within-project trajectories. We investigate the largest within-project declines through repair diffs, detection predicates, alert locations, and execution logs, while accounting for failed, degraded, and costly analyses. Under changed-file case-label scoring, overall F1 ranges from 0.066 to 0.478, but count-based AUROC stays near chance (0.495-0.526). Newer CodeQL releases attain higher F1 alongside nearly parallel rises in recall and false-positive rate, not stronger separation of vulnerable from fixed cases. In project-matched year pairs, CodeQL R2024 has negative mean later-minus-earlier F1 at every source-year gap, whereas Infer v1.1 and v1.3 have positive means throughout. Among selected drops, only 6 of 39 CodeQL and 2 of 620 Infer findings overlap a changed repair line. For Semgrep, 78.96% of cases show parser or timeout diagnostics even though every run reports success. These results suggest that apparent changes in temporal SAST effectiveness can arise from broader alerting, changing analysis coverage, and incidental findings, not solely from losses in vulnerability detection. Evaluations of temporal sustainability should fix detector conditions, control for projects, inspect repair-level evidence, and report failed or degraded analyses.

cs.CR↗

arXiv2025

Fre-CW: Targeted Attack on Time Series Forecasting using Frequency Domain Loss

Transformer-based models have made significant progress in time series forecasting. However, a key limitation of deep learning models is their susceptibility to adversarial attacks, which has not been studied enough in the context of time series prediction. In contrast to areas such as computer vision, where adversarial robustness has been extensively studied, frequency domain features of time series data play an important role in the prediction task but have not been sufficiently explored in terms of adversarial attacks. This paper proposes a time series prediction attack algorithm based on frequency domain loss. Specifically, we adapt an attack method originally designed for classification tasks to the prediction field and optimize the adversarial samples using both time-domain and frequency-domain losses. To the best of our knowledge, there is no relevant research on using frequency information for time-series adversarial attacks. Our experimental results show that these current time series prediction models are vulnerable to adversarial attacks, and our approach achieves excellent performance on major time series forecasting datasets.

cs.LG↗

arXiv2013

Optimal Power Allocation over Multiple Identical Gilbert-Elliott Channels

We study the fundamental problem of power allocation over multiple Gilbert-Elliott communication channels. In a communication system with time varying channel qualities, it is important to allocate the limited transmission power to channels that will be in good state. However, it is very challenging to do so because channel states are usually unknown when the power allocation decision is made. In this paper, we derive an optimal power allocation policy that can maximize the expected discounted number of bits transmitted over an infinite time span by allocating the transmission power only to those channels that are believed to be good in the coming time slot. We use the concept belief to represent the probability that a channel will be good and derive an optimal power allocation policy that establishes a mapping from the channel belief to an allocation decision. Specifically, we first model this problem as a partially observable Markov decision processes (POMDP), and analytically investigate the structure of the optimal policy. Then a simple threshold-based policy is derived for a three-channel communication system. By formulating and solving a linear programming formulation of this power allocation problem, we further verified the derived structure of the optimal policy.

cs.IT↗

arXiv2012

Optimal Power Allocation Policy over Two Identical Gilbert-Elliott Channels

We study the fundamental problem of optimal power allocation over two identical Gilbert-Elliott (Binary Markov) communication channels. Our goal is to maximize the expected discounted number of bits transmitted over an infinite time span by judiciously choosing one of the four actions for each time slot: 1) allocating power equally to both channels, 2) allocating all the power to channel 1, 3) allocating all the power to channel 2, and 4) allocating no power to any of the channels. As the channel state is unknown when power allocation decision is made, we model this problem as a partially observable Markov decision process(POMDP), and derive the optimal policy which gives the optimal action to take under different possible channel states. Two different structures of the optimal policy are derived analytically and verified by linear programming simulation. We also illustrate how to construct the optimal policy by the combination of threshold calculation and linear programming simulation once system parameters are known.

cs.IT↗

arXiv2012

Power Allocation over Two Identical Gilbert-Elliott Channels

We study the problem of power allocation over two identical Gilbert-Elliot communication channels. Our goal is to maximize the expected discounted number of bits transmitted over an infinite time horizon. This is achieved by choosing among three possible strategies: (1) betting on channel 1 by allocating all the power to this channel, which results in high data rate if channel 1 happens to be in good state, and zero bits transmitted if channel 1 is in bad state (even if channel 2 is in good state) (2) betting on channel 2 by allocating all the power to the second channel, and (3) a balanced strategy whereby each channel is allocated half the total power, with the effect that each channel can transmit a low data rate if it is in good state. We assume that each channel's state is only revealed upon transmission of data on that channel. We model this problem as a partially observable Markov decision processes (MDP), and derive key threshold properties of the optimal policy. Further, we show that by formulating and solving a relevant linear program the thresholds can be determined numerically when system parameters are known.

cs.IT↗