The increasing prevalence of encrypted communication on the modern internet has presented new challenges for traffic classification and network management. Traditional traffic classification methods cannot handle encrypted traffic effectively. Meanwhile, many existing methods either rely on hand-crafted features or fail to extract the underlying interaction patterns between data packets adequately. In this paper, we propose a novel encrypted traffic classification method called the Attention-based Vision Transformer and Spatiotemporal for Traffic Classification (ATVITSC). In the preprocessing stage, packet-level images within a session, generated from the payload of data packets, are combined into a session image to mitigate information confusion. In the classification stage, session images are first processed by the packet vision transformer (PVT) module, which employs the transformer encoder and multi-head self-attention mechanism, to capture the global features. In parallel, session images are also processed by the spatiotemporal feature extraction (STFE) module, where spatial features of packets are extracted by the convolution operation with the attention mechanism and temporal features between packets are then combined by the bidirectional Long Short-Term Memory (LSTM). The global and spatiotemporal features are fused in the feature fusion classification (FFC) module by a dynamic weighting mechanism and encrypted traffic is finally classified based on the fused features. Comprehensive experiments on various types of encrypted traffic, including virtual private network (VPN), onion router (Tor), malicious traffic, and mobile traffic, show that the ATVITSC successfully improves the macro-f1 scores to 97.88%, 98.79%, 99.67%, 94.90%, respectively. The results also reveal that the ATVITSC exhibits better classification performance and generalization ability than the state-of-the-art methods.

Nowadays, more and more researchers are interested in reversible data hiding in encrypted images (RDHEI), which can be applied in privacy protection and cloud storage. In this paper, a new RDHEI method on the basis of hierarchical quad-tree coding and multi-MSB (most significant bit) prediction is proposed. The content owner performs pixel prediction to obtain a prediction error image and explores the maximum embedding capacity of the prediction error image by hierarchical quad-tree coding before image encryption. According to the marked bits of vacated room capacity, the data hider can embed additional data into the room-vacated image without knowing the content of original image. Through the data hiding key and the encryption key, the legal receiver is able to conduct data extraction and image recovery separately. Experimental results show that the average embedding rates of the proposed method can separately reach 3.504 bpp (bits per pixel), 3.394 bpp, and 2.746 bpp on three well-known databases, BOSSBase, BOWS-2, and UCID, which are higher than some state-of-the-art methods.

Off-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.

QARMA is a new tweakable block cipher used for memory encryption, the generation of short tags and the construction of the keyed hash functions in future. It adopts a three-round Even-Mansour scheme and supports 64 and 128 bits of block size, denoted by QARMA-64 and QARMA-128, respectively. Their tweak lengths equal the block sizes and their keys are twice as long as the blocks. In this paper, we improve the security analysis of reduced-version QARMA against impossible differential and meet-in-the-middle attacks. Specifically, first exploit some properties of its linear operations and the redundancy of key schedule. Based on them, we propose impossible differential attacks on 11-round QARMA-64/128, and meet-in-the-middle attacks on 10-round symmetric QARMA-128 and the last 12 rounds of asymmetric QARMA-128. Compared with the previously best known results on QARMA-64, our attack can recover 16 more bits of master key with the almost complexities. Compared with the previously best known results on symmetric QARMA-128, the memory complexity of our attack in Section IV is reduced by a factor of 2 ⁴⁸ . Moreover, the meet-in-the-middle attack on 12-round QARMA-128 is the best known attack on QARMA-128 in terms of the number of rounds.

Kiasu-BC and Joltik-BC are internal tweakable block ciphers of authenticated encryption algorithms Kiasu and Joltik submitted to the CAESAR competition. Kiasu-BC is a 128-bit block cipher, of which tweak and key sizes are 64 and 128 bits, respectively. Joltik-BC-128 is a 64-bit lightweight block cipher supporting 128 bits tweakey. Its designers recommended the key and tweak sizes are both 64 bits. In this paper, we propose improved meet-in-the-middle attacks on 8-round Kiasu-BC, 9-round and 10-round Joltik-BC-128 by exploiting properties of their structures and using precomputation tables and the differential enumeration. For Kiasu-BC, we build a 5-round distinguisher to attack 8-round Kiasu-BC with $2^{109}$ plaintext–tweaks, $2^{112.8}$ encrytions and $2^{92.91}$ blocks. Compared with previously best known cryptanalytic results on 8-round Kiasu-BC under chosen plaintext attacks, the data and time complexities are reduced by $2^{7}$ and $2^{3.2}$ times, respectively. For the recommended version of Joltik-BC-128, we construct a 6-round distinguisher to attack 9-round Joltik-BC-128 with $2^{53}$ plaintext–tweaks, $2^{56.6}$ encryptions and $2^{52.91}$ blocks, respectively. Compared with previously best known results, the data and time complexities are reduced by $2^7$ and $2^{5.1}$ times, respectively. In addition, we present a 6.5-round distinguisher to attack 10-round Joltik-BC-128 with $2^{53}$ plaintext–tweaks, $2^{101.4}$ encryptions and $2^{76.91}$ blocks.