اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدTowards Automatic Discovery of Cybercrime Supply Chains
126
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Cybercrime forums enable modern criminal entrepreneurs to collaborate with other criminals into increasingly efficient and sophisticated criminal endeavors. Understanding the connections between different products and services can often illuminate effective interventions. However, generating this understanding of supply chains currently requires time-consuming manual effort. In this paper, we propose a language-agnostic method to automatically extract supply chains from cybercrime forum posts and replies. Our supply chain detection algorithm can identify 36% and 58% relevant chains within major English and Russian forums, respectively, showing improvements over the baselines of 13% and 36%, respectively. Our analysis of the automatically generated supply chains demonstrates underlying connections between products and services within these forums. For example, the extracted supply chain illuminated the connection between hack-for-hire services and the selling of rare and valuable `OG accounts, which has only recently been reported. The understanding of connections between products and services exposes potentially effective intervention points.
قيم البحث
اقرأ أيضاً
Blockchain offers traceability and transparency to supply chain event data and hence can help overcome many challenges in supply chain management such as: data integrity, provenance and traceability. However, data privacy concerns such as the protect
ion of trade secrets have hindered adoption of blockchain technology. Although consortium blockchains only allow authorised supply chain entities to read/write to the ledger, privacy preservation of trade secrets cannot be ascertained. In this work, we propose a privacy-preservation framework, PrivChain, to protect sensitive data on blockchain using zero knowledge proofs. PrivChain provides provenance and traceability without revealing any sensitive information to end-consumers or supply chain entities. Its novelty stems from: a) its ability to allow data owners to protect trade related information and instead provide proofs on the data, and b) an integrated incentive mechanism for entities providing valid proofs over provenance data. In particular, PrivChain uses Zero Knowledge Range Proofs (ZKRPs), an efficient variant of ZKPs, to provide origin information without disclosing the exact location of a supply chain product. Furthermore, the framework allows to compute proofs and commitments off-line, decoupling the computational overhead from blockchain. The proof verification process and incentive payment initiation are automated using blockchain transactions, smart contracts, and events. A proof of concept implementation on Hyperledger Fabric reveals a minimal overhead of using PrivChain for blockchain enabled supply chains.
Package managers have become a vital part of the modern software development process. They allow developers to reuse third-party code, share their own code, minimize their codebase, and simplify the build process. However, recent reports showed that
package managers have been abused by attackers to distribute malware, posing significant security risks to developers and end-users. For example, eslint-scope, a package with millions of weekly downloads in Npm, was compromised to steal credentials from developers. To understand the security gaps and the misplaced trust that make recent supply chain attacks possible, we propose a comparative framework to qualitatively assess the functional and security features of package managers for interpreted languages. Based on qualitative assessment, we apply well-known program analysis techniques such as metadata, static, and dynamic analysis to study registry abuse. Our initial efforts found 339 new malicious packages that we reported to the registries for removal. The package manager maintainers confirmed 278 (82%) from the 339 reported packages where three of them had more than 100,000 downloads. For these packages we were issued official CVE numbers to help expedite the removal of these packages from infected victims. We outline the challenges of tailoring program analysis tools to interpreted languages and release our pipeline as a reference point for the community to build on and help in securing the software supply chain.
Since their introduction over two decades ago, side-channel attacks have presented a serious security threat. While many ciphers implementations employ masking techniques to protect against such attacks, they often leak secret information due to unin
tended interactions in the hardware. We present Rosita, a code rewrite engine that uses a leakage emulator which we amend to correctly emulate the micro-architecture of a target system. We use Rosita to automatically protect masked implementations of AES, ChaCha, and Xoodoo. For AES and Xoodoo, we show the absence of observable leakage at 1,000,000 traces with less than 21% penalty to the performance. For ChaCha, which has significantly more leakage, Rosita eliminates over 99% of the leakage, at a performance cost of 64%.
The Transformer self-attention network has recently shown promising performance as an alternative to recurrent neural networks in end-to-end (E2E) automatic speech recognition (ASR) systems. However, Transformer has a drawback in that the entire inpu
t sequence is required to compute self-attention. We have proposed a block processing method for the Transformer encoder by introducing a context-aware inheritance mechanism. An additional context embedding vector handed over from the previously processed block helps to encode not only local acoustic information but also global linguistic, channel, and speaker attributes. In this paper, we extend it towards an entire online E2E ASR system by introducing an online decoding process inspired by monotonic chunkwise attention (MoChA) into the Transformer decoder. Our novel MoChA training and inference algorithms exploit the unique properties of Transformer, whose attentions are not always monotonic or peaky, and have multiple heads and residual connections of the decoder layers. Evaluations of the Wall Street Journal (WSJ) and AISHELL-1 show that our proposed online Transformer decoder outperforms conventional chunkwise approaches.
In wireless systems, neighbor discovery (ND) is a fundamental building block: determining which devices are within direct radio communication is an enabler for networking protocols and a wide range of applications. To thwart abuse of ND and the resul
tant compromise of the dependent functionality of wireless systems, numerous works proposed solutions to secure ND. Nonetheless, until very recently, there has been no formal analysis of secure ND protocols. We close this gap in cite{asiaccs08}, but we concentrate primarily on the derivation of an impossibility result for a class of protocols. In this paper, we focus on reasoning about specific protocols. First, we contribute a number of extensions and refinements on the framework of [24]. As we are particularly concerned with the practicality of provably secure ND protocols, we investigate availability and redefine accordingly the ND specification, and also consider composability of ND with other protocols. Then, we propose and analyze two secure ND protocols: We revisit one of the protocols analyzed in [24], and introduce and prove correct a more elaborate challenge-response protocol.
الأسئلة المقترحة
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
