Cryptanalysis of a Cryptosystem with Non- Commutative Platform Groups

A cryptosystem with non-commutative platform groups based on conjugator search problem was recently introduced at Neural Computing and Applications 2016. Its versatility was illustrated by building a public-key encryption scheme. We propose an algebraic key-recovery attack in the polynomial computational complexity. Furthermore, we peel off the encryption and decryption process and propose attack methods for solving the conjugator search problem over the given non-abelian group. Finally, we provide corresponding practical attack examples to illustrate the attack methods in our cryptanalysis, and provide some improved suggestions.

HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic essential.Existing methods for detecting encrypted traffic face two significant challenges.First,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic.Second,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential characteristics.To address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed HGNN-ETC.This approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context information.HGNN-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate classification.We select the ISCXVPN and the USTC-TK2016 dataset for our experiments.The results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.

Memway:In-Memory Waylaying Acceleration for Practical Rowhammer Attacks Against Binaries

The Rowhammer bug is a novel micro-architectural security threat, enabling powerful privilege-escalation attacks on various mainstream platforms. It works by actively flipping bits in Dynamic Random Access Memory(DRAM) cells with unprivileged instructions. In order to set up Rowhammer against binaries in the Linux page cache, the Waylaying algorithm has previously been proposed. The Waylaying method stealthily relocates binaries onto exploitable physical addresses without exhausting system memory. However, the proof-of-concept Waylaying algorithm can be easily detected during page cache eviction because of its high disk I/O overhead and long running time. This paper proposes the more advanced Memway algorithm, which improves on Waylaying in terms of both I/O overhead and speed. Running time and disk I/O overhead are reduced by 90% by utilizing Linux tmpfs and inmemory swapping to manage eviction files. Furthermore, by combining Memway with the unprivileged posix fadvise API, the binary relocation step is made 100 times faster. Equipped with our Memway+fadvise relocation scheme,we demonstrate practical Rowhammer attacks that take only 15–200 minutes to covertly relocate a victim binary,and less than 3 seconds to flip the target instruction bit.