19 results about "Interval Sequence" patented technology

The application relates to the field of computer data processing and discloses a traffic automatic playback method for software testing, which comprises the following steps: obtaining software traffic data and obtaining data packets and time intervals in the software traffic data; according to interval values and time sequence distribution relationships of the time intervals, a first category is obtained through multi-threshold segmentation, a third category is obtained through threshold segmentation of the first category, and all intervals except the third category in a time interval sequence are classified into a fourth category; a first compression rate of each interval in the fourth category is obtained, and a second compression rate of each interval in the third category is obtained; a correction coefficient is obtained according to the number of elements of the first category to which each interval in the fourth category belongs and packet values of adjacent data packets, and a third compression rate is obtained; and the traffic data is compressed and automatically played back according to the obtained compression rates. The application aims to solve the problems that the existing equal compression of time intervals between data packets leads to packet loss and large differences between traffic data before and after compression.

This invention relates to the fields of network security and network simulation technology, and discloses a method for reshaping malicious traffic based on feature serialization. By collecting traffic data from the target network environment, serialized features of load and time intervals are extracted, and session flow features are further statistically obtained. Then, based on Pareto, normal, log-normal, and Poisson probability distribution models, serialized features conforming to the target network traffic characteristics are generated, and the probability distribution model with the highest similarity to the target network traffic is selected by calculating Euclidean distance. Finally, based on this model, the load and time interval sequences of malicious traffic are reshaped to generate network attack traffic that is difficult to detect. This invention effectively improves the concealment of malicious traffic, making it more difficult for existing detection systems to identify; the generated malicious traffic can effectively test and evaluate the capabilities of network intrusion detection systems.

The invention discloses an anti-eavesdropping-based self-defined interval combination password method, which specifically comprises the following steps of: recording a time interval of adjacent keys in a password input process of a user by adopting a sliding window mechanism, and storing the time interval as a rhythm interval sequence; in the password input process, random jitter and rhythm speed scaling are applied to the rhythm interval sequence to generate a disturbed rhythm sequence; constructing a basic camouflage event comprising a camouflage sound source and a volume parameter, and randomly inserting an interference camouflage event into the basic camouflage event; combining the basic camouflage events and the interference camouflage events into an event queue, and performing sorting and anti-congestion adjustment according to a time sequence to form an interruptible camouflage event queue; and based on the camouflage event queue, playing corresponding camouflage sound according to event triggering time so as to carry out acoustic masking on a real key pressing rhythm.

The invention relates to the technical field of data security, in particular to a security operation method and system based on multi-agent collaboration, and the method comprises the steps: intercepting and capturing a TLS length and a time interval sequence of an encrypted session by using an edge-aware agent, constructing a spatio-temporal topology embedding vector based on a linear visibility criterion and a graph attention mechanism, and constructing a spatial-temporal topology embedding vector; and the flow agent maps the vector into a Gaussian distribution parameter to generate a token adjustment coefficient, dynamically adjusts the token bucket frequency and executes asynchronous delay shaping by using a time wheel, and the reflection agent combines a flow confusion escape rate and a processing delay index to carry out closed-loop updating on model parameters through a strategy gradient algorithm. According to the invention, adaptive defense of the encrypted covert channel is realized through dynamic traffic shaping.

The invention belongs to the technical field of digital data transmission, and particularly relates to an adaptive modulation data transmission method and system based on bit error rate statistics, and the method comprises the steps: recording the bit error interval length between adjacent bit error bits, and obtaining a bit error interval length set; sorting the error code interval length set to obtain an ordered error code interval sequence, and calculating an error code gathering index by using weighted summation; constructing a background noise retention weight by using an error code aggregation index, and calculating an effective background error rate in combination with a physical statistical error rate; and determining a target modulation mode according to the effective background bit error rate, and performing data transmission. According to the method, whether the error code is caused by the random background noise or the burst transient interference can be distinguished from the statistical domain, the effective background error rate is restored by stripping the burst component when the transient interference occurs, the throughput loss caused by blind order reduction modulation is avoided, and the link transmission efficiency and reliability under a complex channel are improved.

The present application relates to a kind of automatic unpacking device of present cash, including roller conveyor, roller conveyor includes multiple interval sequencing roller body, each roller body is rotationally sleeved rolling sleeve tube, the gap between two adjacent rolling sleeve tube is the storage space for placing coin column, further including support, pivot and swing arm;When the coin column conveying support is directly below, driving member drives pivot positive rotation, pivot is driven by swing block to move the swing arm close to coin column upward, while another swing arm moves downward, rotating roller starts to rotate, after obliquely arranged scraper and the seal of coin column contact, the paper of coin column in rotating state is gradually stripped, further, the paper of suction passage is recycled, by such, mechanical automation is realized to coin column and is disassembled, greatly improve work efficiency.

The application discloses a kind of MEC task unloading methods and devices for interval uncertainty, it is related to edge computing technical field.The method includes: obtaining the task parameter of task to be handled, terminal resource parameter and MEC resource parameter, and the uncertain parameter in it is expressed as interval parameter;According to unloading decision variable and interval parameter, construct interval uncertain optimization model including energy consumption, time delay and user side cost;Interval energy consumption target is converted into interval center value target and interval radius value target based on interval sequence relation, and interval constraint is converted into deterministic constraint based on interval possibility, to obtain deterministic multi-objective optimization model;Dual-layer optimization unloading algorithm is used to solve, to obtain target unloading strategy.The application can consider energy consumption, time delay, cost and resource constraint in the case where parameter exists interval uncertainty, improve the robustness of task unloading.

The invention discloses a data stream change detection method based on an isolation mechanism, and the method comprises the following steps: S1, data partitioning: dividing input sequence data into non-overlapping interval sequences according to the length w, and enabling one interval to be a window; s2, constructing isolated kernel mapping: selecting a series of representative windows, and uniformly sampling psi data points from the window data; s3, interval feature representation: aiming at each window, mapping all data contained in the window to a Hilbert space of an isolated kernel, and calculating a mean vector of the data in the space; s4, calculating the similarity of adjacent intervals; s5, automatically searching parameters; and S6, change interval detection. According to the method, a point-to-interval target detection method and an isolated kernel similarity measurement mode are adopted, the real-time performance, robustness and fine change capturing capacity in data flow change detection are balanced, and a practical solution is provided for distribution change detection in real large-scale streaming data.