Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

With the rapid development of the Internet,a large number of private protocols emerge on the network.However,some of them are constructed by attackers to avoid being analyzed,posing a threat to computer network security.The blockchain uses the P2P protocol to implement various functions across the network.Furthermore,the P2P protocol format of blockchain may differ from the standard format specification,which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them.Therefore,the ability to distinguish different types of unknown network protocols is vital for network security.In this paper,we propose an unsupervised clustering algorithm based on maximum frequent sequences for binary protocols,which can distinguish various unknown protocols to provide support for analyzing unknown protocol formats.We mine the maximum frequent sequences of protocolmessage sets in bytes.Andwe calculate the fuzzymembership of the protocolmessage to each maximum frequent sequence,which is based on fuzzy set theory.Then we construct the fuzzy membership vector for each protocol message.Finally,we adopt K-means++to split different types of protocol messages into several clusters and evaluate the performance by calculating homogeneity,integrity,and Fowlkes and Mallows Index(FMI).Besides,the clustering algorithms based onNeedleman–Wunsch and the fixed-length prefix are compared with the algorithm presented in this paper.Compared with these traditional clustering methods,we demonstrate a certain improvement in the clustering performance of our work.

基于信道短化和圆球译码的水声通信迭代序列检测

The demand for high-data-rate underwater acoustic communications(UACs)in marine development is increasing;however,severe multipaths make demodulation a challenge.The decision feedback equalizer(DFE)is one of the most popular equalizers in UAC;however,it is not the optimal algorithm.Although maximum likelihood sequence estimation(MLSE)is the optimal algorithm,its complexity increases exponentially with the number of channel taps,making it challenging to apply to UAC.Therefore,this paper proposes a complexity-reduced MLSE to improve the bit error rate(BER)performance in multipath channels.In the proposed algorithm,the original channel is first shortened using a channel-shortening method,and several dominant channel taps are selected for MLSE.Subsequently,sphere decoding(SD)is performed in the following MLSE.Iterations are applied to eliminate inter-symbol interference caused by weak channel taps.The simulation and sea experiment demonstrate the superiority of the proposed algorithm.The simulation results show that channel shortening combined with SD can drastically reduce computational complexity,and iterative SD performs better than DFE based on recursive least squares(RLS-DFE),DFE based on improved proportionate normalized least mean squares(IPNLMS-DFE),and channel estimation-based DFE(CE-DFE).Moreover,the sea experimental results at Zhairuoshan Island in Zhoushan show that the proposed receiver scheme has improved BER performance over RLSDFE,IPNLMS-DFE,and CE-DFE.Compared with the RLS-DFE,the BER,after five iterations,is reduced from 0.0076 to 0.0037 in the 8–12 k Hz band and from 0.1516 to 0.1145 in the 13–17 k Hz band at a distance of 2000 m.Thus,the proposed algorithm makes it possible to apply MLSE in UAC in practical scenarios.

MLSE-assisted decision feedback equalizer for error-propagation suppression in high-speed IM-DD transmission systems

We propose a trellis-compressed maximum likelihood sequence estimation(TC-MLSE)-assisted sliding-block decision feedback equalizer(DFE)to suppress the error propagation resulting from the DFE in high-speed systems.We use an out-ofrange detector to detect the end of burst errors from the DFE and activate the optional TC-MLSE to correct burst errors.We conduct experiments to transmit a 201-Gbit/s PAM-8 signal.The results show that the proposed method achieves a bit error rate of 3.65×10^(-3),which is close to that of MLSE.The optional MLSE is only activated when needed and processes 11.4%of the total symbols.Moreover,the proposed method compresses the maximum length of burst errors from 19 to 5.

CNN demodulation model with cascade parallel crossing for CPM signals

The continuous phase modulation(CPM)technique is widely used in range telemetry due to its high spectral efficiency and power efficiency.However,the demodulation performance of the traditional maximum likelihood sequence detection(MLSD)algorithm significantly deteriorates in non-ideal synchronization or fading channels.To address this issue,this work proposes a convolutional neural network(CNN)called the cascade parallel crossing network(CPCNet)to enhance the robustness of CPM signals demodulation.The CPCNet model employs a multiple parallel structure and feature fusion to extract richer features from CPM signals.This approach constructs feature maps at different levels,resulting in a more comprehensive training of the model and improved demodulation performance.Simulation results show that under Gaussian channel,the proposed CPCNet achieves the same bit error rate(BER)performance as MLSD method when there is no timing error,but with 1/4 symbol period timing error,the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term memory deep neural network(CLDNN).In addition,under Rayleigh channel,the BER of the proposed method is reduced by 5%-87%compared to that of MLSD in the wide signal-to-noise ratio(SNR)region.

A portable single-cell analysis system integrating hydrodynamic trapping with broadband impedance spectroscopy

In this paper, we present a portable single-cell analysis system with the hydrodynamic cell trapping and the broadband electrical impedance spectroscopy （EIS）. Using the least flow resistance path principle, the hydrodynamic cell trapping in serpentine arrays can be carried out in a deterministic and automatic manner without the assistance of any external fields. The experimental results show that a cell trap rate of higher than 95% can be easily achieved in our ceil trapping microdevices. Using the maximum length sequences （MLS） technique, our home-made EIS is capable of measuring the impedance spectrum ranging from 1.953 kHz to 1 MHz in approximately 0.5 ms. Finally, on the basis of the developed single-cell analysis system, we precisely monitor the trapping process of human breast tumor cells （MCF-7 cells） according to the changes of electrical impedance. The MCF-7 cells with different trapping conditions or sizes can also be clearly distinguished through the impedance signals. Our portable single-cell analysis system may provide a promising tool to monitor single cells for long periods of time or to discriminate cell types.