Android’s dominant market share has made it a prime target for malware attacks. Although machine learning-based detection systems have demonstrated effectiveness, they remain vulnerable to adversarial attacks, which modify samples to preserve malicious functionality while evading detection. Adversarial training is a prevalent defense strategy. However, generating effective adversarial examples for Android malware is challenging due to the complex mapping between feature and problem space. To address this, recent efforts have explored feature-space attacks constrained by statistical dependencies. Yet, such approaches inherently rely on large-scale datasets to achieve strong performance, and may fail to capture the underlying semantic relationships among features, like call associations. In this paper, we propose a novel method that incorporates semantic dependencies, i.e., API dependencies extracted from function call graphs of APKs. By leveraging these dependencies as domain constraints, our method preserves intrinsic call associations among features during perturbation. This leads to adversarial examples that more closely reflect realistic attack behaviors. Furthermore, a reinforcement learning-based mechanism is employed to enhance the evasive capability of the generated adversarial samples against detection models. The resulting adversarial samples are leveraged for adversarial training to enhance detector robustness. Experimental results demonstrate that the adversarial examples generated by our approach effectively enhance model robustness via adversarial training, yielding superior resilience in realistic adversarial environments. In adversarial attack scenarios, the proposed method attains the highest detection accuracy against problem-space attacks, surpassing the baseline model without adversarial training by 45.7% and 14.3%, respectively. Moreover, our method significantly reduces the average generation time by 83.5% compared to problem-space adversarial example generation approaches.

As a useful cryptographic primitive, searchable symmetric encryption (SSE) has been intensively studied to achieve the secure and efficient retrieval of encrypted data. In order to process update operations, dynamic SSE schemes have been proposed. But recently, file-injection attack has threatened the security of traditional dynamic SSE protocols. Therefore, designing dynamic SSE schemes with forward privacy becomes a new demand to resist the above attack. Meanwhile, multi-client setting is another requirement in SSE techniques where multiple clients can be delegated and have access to the database. However, most of previous forward private schemes were constructed for single-client setting and cannot directly extended to multi-client environment efficiently. To solve the problem, we propose a forward private SSE scheme with support for multi-client in cloud computing. The proposed scheme is based on XOR-homomorphic function and involves two new data structures as private link and public search tree. Security proof demonstrates the proposed scheme can meet the desired secure features. We then conduct experimental evaluation of the proposed scheme and make comparison with related schemes. The result shows that the proposed scheme tends to have high efficiency.

Vehicular ad hoc network (VANETs) plays a major part in intelligent transportation to enhance traffic efficiency and safety. Security and privacy are the essential matters needed to be tackled due to the open communication channel. Most of the existing schemes only provide message authentication without identity authentication, especially the inability to support forward secrecy which is a major security goal of authentication schemes. In this article, we propose a privacy-preserving mutual authentication scheme with batch verification for VANETs which support both message authentication and identity authentication. More importantly, the proposed scheme achieves forward secrecy, which means the exposure of the shared key will not compromise the previous interaction. The security proof shows that our scheme can withstand various known security attacks, such as the impersonation attack and forgery attack. The experiment analysis results based on communication and computation cost demonstrate that our scheme is more efficient compared with the related schemes.

With the rapid development of network and storage technology, cloud storage has become a new service mode, while data sharing and user revocation are important functions in the cloud storage. Therefore, according to the characteristics of cloud storage, a revocable key-aggregate encryption scheme is put forward based on subset-cover framework. The proposed scheme not only has the key-aggregate characteristics, which greatly simplifies the user’s key management, but also can revoke user access permissions, realizing the flexible and effective access control. When user revocation occurs, it allows cloud server to update the ciphertext so that revoked users can not have access to the new ciphertext, while nonrevoked users do not need to update their private keys. In addition, a verification mechanism is provided in the proposed scheme, which can verify the updated ciphertext and ensure that the user revocation is performed correctly. Compared with the existing schemes, this scheme can not only reduce the cost of key management and storage, but also realize user revocation and achieve user’s access control efficiently. Finally, the proposed scheme can be proved to be selective chosen-plaintext security in the standard model.

Query processing is a major cost factor in operating large web search engines. In this paper, we study query result caching, one of the main techniques used to optimize query processing performance. Our first contribution is a study of result caching as a weighted caching problem. Most previous work has focused on optimizing cache hit ratios, but given that processing costs of queries can vary very significantly we argue that total cost savings also need to be considered. We describe and evaluate several algorithms for weighted result caching, and study the impact of Zipf-based query distributions on result caching. Our second and main contribution is a new set of feature-based cache eviction policies that achieve significant improvements over all previous methods, substantially narrowing the existing performance gap to the theoretically optimal (clairvoyant) method. Finally, using the same approach, we also obtain performance gains for the related problem of inverted list caching.

The Google search engine uses a method called PageRank, together with term-based and other ranking techniques, to order search results returned to the user. PageRank uses link analysis to assign a global importance score to each web page. The PageRank scores of all the pages are usually determined off-line in a large-scale computation on the entire hyperlink graph of the web, and several recent studies have focused on improving the efficiency of this computation, which may require multiple hours on a workstation.

Over the last few years, most major search engines have integrated link-based ranking techniques in order to provide more accurate search results. One widely known approach is the Pagerank technique, which forms the basis of the Google ranking scheme, and which assigns a global importance measure to each page based on the importance of other pages pointing to it. The main advantage of the Pagerank measure is that it is independent of the query posed by a user; this means that it can be precomputed and then used to optimize the layout of the inverted index structure accordingly. However, computing the Pagerank measure requires implementing an iterative process on a massive graph corresponding to billions of web pages and hyperlinks.In this paper, we study I/O-efficient techniques to perform this iterative computation. We derive two algorithms for Pagerank based on techniques proposed for out-of-core graph algorithms, and compare them to two existing algorithms proposed by Haveliwala. We also consider the implementation of a recently proposed topic-sensitive version of Pagerank. Our experimental results show that for very large data sets, significant improvements over previous results can be achieved on machines with moderate amounts of memory. On the other hand, at most minor improvements are possible on data sets that are only moderately larger than memory, which is the case in many practical scenarios.