In recent years,the rapid development of computer software has led to numerous security problems,particularly software *** flaws can cause significant harm to users’privacy and *** security defect detection technolog...
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In recent years,the rapid development of computer software has led to numerous security problems,particularly software *** flaws can cause significant harm to users’privacy and *** security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection *** intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false ***,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)***,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency *** feature vector is then used as the learning target for the neural *** types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection *** results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.
With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged...
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With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this *** methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and *** with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.
As indispensable components of superconducting circuit-based quantum computers,Josephson junctions determine how well superconducting qubits *** Monte Carlo(RMC)can be used to recreate Josephson junction’s atomic str...
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As indispensable components of superconducting circuit-based quantum computers,Josephson junctions determine how well superconducting qubits *** Monte Carlo(RMC)can be used to recreate Josephson junction’s atomic structure based on experimental data,and the impact of the structure on junctions’properties can be investigated by combining different analysis *** order to build a physical model of the atomic structure and then analyze the factors that affect its performance,this paper briefly reviews the development and evolution of the RMC *** also summarizes the modeling process and structural feature analysis of the Josephson junction in combination with different feature extraction techniques for electrical characterization ***,the obstacles and potential directions of Josephson junction modeling,which serves as the theoretical foundation for the production of superconducting quantum devices at the atomic level,are discussed.
Network flow watermarking(NFW) is usually used for flow *** actively modulating some features of the carrier traffic,NFW can establish the correspondence between different network *** the face of strict demands of net...
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Network flow watermarking(NFW) is usually used for flow *** actively modulating some features of the carrier traffic,NFW can establish the correspondence between different network *** the face of strict demands of network traffic tracing,current watermarking methods cannot work efficiently due to the dependence on specific protocols,demand for large quantities of packets,weakness on resisting network channel interferences and so *** this end,we propose a robust network flow watermarking method based on IP packet sequence,called as *** is designed to utilize the packet sequence as watermark carrier with IP identification field which is insensitive to time jitter and suitable for all IP based *** enhance the robustness against packet loss and packet reordering,the detection sequence set is constructed in terms of the variation range of packet sequence,correcting the possible errors caused by the network *** improve the detection accuracy,the long watermark information is divided into several short sequences to embed in turn and assembled during *** a large number of experiments on the Internet,the overall detection rate and accuracy of IP-Pealing reach 99.91% and 99.42%*** comparison with the classical network flow watermarking methods,such as PROFW,IBW,ICBW,WBIPD and SBTT,the accuracy of IP-Pealing is increased by 13.70% to 54.00%.
The function of the Internet proxy is to check and convert the data exchanged between client and server. In fact, the two-party secure communication protocol with good security is turned into an unsafe multiparty prot...
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The function of the Internet proxy is to check and convert the data exchanged between client and server. In fact, the two-party secure communication protocol with good security is turned into an unsafe multiparty protocol. At present, there are relatively few proxy protocols that can be applied in practice. This paper analyzes the classic agent protocol mcTLS and pointed out the security issues. We focus on the security of TLS 1.3 and proposed a lattice-based multi-party proxy protocol:La TLS. LaTLS can be proved secure in the eCK model, it can resist key-sharing attacks, counterfeiting attacks, replay attacks, and achieve forward security. Compared with traditional DH and ECDH schemes, LaTLS is more effcient. Its security is based on the shortest vector problem, therefor it has anti-quantum attack properties.
IP geolocation is essential for the territorial analysis of sensitive network entities,location-based services(LBS)and network fraud *** has important theoretical significance and application ***-based IP geolocation ...
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IP geolocation is essential for the territorial analysis of sensitive network entities,location-based services(LBS)and network fraud *** has important theoretical significance and application ***-based IP geolocation is a hot research ***,the existing IP geolocation algorithms cannot effectively utilize the distance characteristics of the delay,and the nodes’connection relation,resulting in high geolocation *** is challenging to obtain the mapping between delay,nodes’connection relation,and geographical *** on the idea of network representation learning,we propose a representation learning model for IP nodes(IP2vec for short)and apply it to street-level IP ***2vec model vectorizes nodes according to the connection relation and delay between nodes so that the IP vectors can reflect the distance and topological proximity between IP *** steps of the street-level IP geolocation algorithm based on IP2vec model are as follows:Firstly,we measure landmarks and target IP to obtain delay and path information to construct the network ***,we use the IP2vec model to obtain the IP vectors from the network ***,we train a neural network to fit the mapping relation between vectors and locations of ***,the vector of target IP is fed into the neural network to obtain the geographical location of target *** algorithm can accurately infer geographical locations of target IPs based on delay and topological proximity embedded in the IP *** cross-validation experimental results on 10023 target IPs in New York,Beijing,Hong Kong,and Zhengzhou demonstrate that the proposed algorithm can achieve street-level *** with the existing algorithms such as Hop-Hot,IP-geolocater and SLG,the mean geolocation error of the proposed algorithm is reduced by 33%,39%,and 51%,respectively.
Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local conten...
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Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content *** relationship-based methods represent a classical approach for geolocating social ***,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user *** address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation ***,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among *** are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate *** this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social *** better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure *** algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation *** results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social ***,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.
Nowadays, research of Text Classification (TC) based on graph neural networks (GNNs) is on the rise. Both inductive methods and transductive methods have made significant progress. For transductive methods, the semant...
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Privacy protection is the key to maintaining the Internet of Things(IoT)communication *** is an important way to achieve covert communication that protects user data *** technology is the key to checking steganography...
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Privacy protection is the key to maintaining the Internet of Things(IoT)communication *** is an important way to achieve covert communication that protects user data *** technology is the key to checking steganography security,and its ultimate goal is to extract embedded *** methods cannot extract under known cover *** this end,this paper proposes a method of extracting embedded messages under known cover ***,the syndrome-trellis encoding process is ***,a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix,while the embedding process is simulated using the cover sequence and parity-check *** the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion,comparing the path consistency can quickly filter the coding parameters to determine the correct matrices,and embedded messages can be extracted *** proposed method does not need to embed all possible messages for the second time,improving coding parameter recognition *** experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.
Graph few-shot learning aims to predict well by training with very few labeled data. Meta learning has been the most popular solution for few-shot learning problem. However, transductive linear probing shows that fine...
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