71 research outputs found
Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration
As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay. [ABSTRACT FROM PUBLISHER
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
The widespread adoption of the Android operating system has made malicious
Android applications an appealing target for attackers. Machine learning-based
(ML-based) Android malware detection (AMD) methods are crucial in addressing
this problem; however, their vulnerability to adversarial examples raises
concerns. Current attacks against ML-based AMD methods demonstrate remarkable
performance but rely on strong assumptions that may not be realistic in
real-world scenarios, e.g., the knowledge requirements about feature space,
model parameters, and training dataset. To address this limitation, we
introduce AdvDroidZero, an efficient query-based attack framework against
ML-based AMD methods that operates under the zero knowledge setting. Our
extensive evaluation shows that AdvDroidZero is effective against various
mainstream ML-based AMD methods, in particular, state-of-the-art such methods
and real-world antivirus solutions.Comment: To Appear in the ACM Conference on Computer and Communications
Security, November, 202
Static Semantics Reconstruction for Enhancing JavaScript-WebAssembly Multilingual Malware Detection
The emergence of WebAssembly allows attackers to hide the malicious
functionalities of JavaScript malware in cross-language interoperations, termed
JavaScript-WebAssembly multilingual malware (JWMM). However, existing
anti-virus solutions based on static program analysis are still limited to
monolingual code. As a result, their detection effectiveness decreases
significantly against JWMM. The detection of JWMM is challenging due to the
complex interoperations and semantic diversity between JavaScript and
WebAssembly. To bridge this gap, we present JWBinder, the first technique aimed
at enhancing the static detection of JWMM. JWBinder performs a
language-specific data-flow analysis to capture the cross-language
interoperations and then characterizes the functionalities of JWMM through a
unified high-level structure called Inter-language Program Dependency Graph.
The extensive evaluation on one of the most representative real-world
anti-virus platforms, VirusTotal, shows that \system effectively enhances
anti-virus systems from various vendors and increases the overall successful
detection rate against JWMM from 49.1\% to 86.2\%. Additionally, we assess the
side effects and runtime overhead of JWBinder, corroborating its practical
viability in real-world applications.Comment: Accepted to ESORICS 202
Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
This paper strives to learn fine-grained fashion similarity. In this
similarity paradigm, one should pay more attention to the similarity in terms
of a specific design/attribute among fashion items, which has potential values
in many fashion related applications such as fashion copyright protection. To
this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly
learn multiple attribute-specific embeddings in an end-to-end manner, thus
measure the fine-grained similarity in the corresponding space. With two
attention modules, i.e., Attribute-aware Spatial Attention and Attribute-aware
Channel Attention, ASEN is able to locate the related regions and capture the
essential patterns under the guidance of the specified attribute, thus make the
learned attribute-specific embeddings better reflect the fine-grained
similarity. Extensive experiments on four fashion-related datasets show the
effectiveness of ASEN for fine-grained fashion similarity learning and its
potential for fashion reranking.Comment: 16 pages, 13 figutes. Accepted by AAAI 2020. Code and data are
available at https://github.com/Maryeon/ase
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