AVK: The German NPP Waste Flow Tracking System

Dynamic information flow tracking (also known as taint tracking) is an appealing approach to combat various security attacks. However, the performance of applications can severely degrade without hardware support for tracking taints. This paper observes that information flow tracking can be efficiently emulated using deferred exception tracking in microprocessors supporting speculative execution. Based on this observation, we propose SHIFT, a low-overhead, software-based dynamic information flow tracking system to detect a wide range of attacks. The key idea is to treat tainted state (describing untrusted data) as speculative state (describing deferred exceptions). SHIFT leverages existing architectural support for speculative execution to track tainted state in registers and needs to instrument only load and store instructions to track tainted state in memory using a bitmap, which results in significant performance advantages. Moreover, by decoupling mechanisms for taint tracking from security policies, SHIFT can detect a wide range of exploits, including high-level semantic attacks. We have implemented SHIFT using the Itanium processor, which has support for deferred exceptions, and by modifying GCC to instrument loads and stores. A security assessment shows that SHIFT can detect both low-level memory corruption exploits as well as high-level semantic attacks with no false positives. Performance measurements show that SHIFT incurs about 1% overhead for server applications. The performance slowdown for SPEC-INT2000 is 2.81X and 2.27X for tracking at byte-level and wordlevel respectively. Minor architectural improvements to the Itanium processor (adding three simple instructions) can reduce the performance slowdown down to 2.32X and 1.8X for byte-level and word-level tracking, respectively.

In the crowd congestion in smart cities, the people flow statistics is necessary in public areas to reasonably control people flow. The You Only Look Once-v3 (YOLOv3) algorithm is employed for pedestrian detection, and the Smooth_L1 loss function is introduced to update the backpropagation parameters to ensure the stability of the object detection model. After the pedestrian is detected, tracking the pedestrian for a certain time is necessary to count out the specific number of pedestrians entering and leaving. Specifically, the Mean Shift algorithm is combined with the Kalman filter to track the target. When the target is lost, the Mean Shift algorithm is used for iterative tracking, and then the Kalman prediction is updated. In the experiment, 7,000 original images are collected from the library, mentioning 88 people of which 82 are recognized, and the detection accuracy reaches 93.18%. The 12,200 original images collected in the teaching building include149 people, of which 139 are recognized, with the detection accuracy reaching 93.29%. Therefore, the people flow statistics system based on machine vision and deep learning can detect and track pedestrians effectively, which is of great significance for the people flow statistics in public areas in smart cities and for the smooth development of various activities.