Code-signing PKI ecosystems are vulnerable to abusers. Kim et al. reported such abuse cases, e.g., malware authors misused the stolen private keys of the reputable code-signing certificates to sign their malicious programs. This certified malware exploits the chain of the trust established in the ecosystem and helps an adversary readily bypass security mechanisms such as anti-virus engines. Prior work analyzed the large corpus of certificates collected from the wild to characterize the security problems. However, this practice was typically performed in a global perspective and often left the issues that could happen at a local level behind. Our work revisits the investigations conducted by previous studies with a local perspective. In particular, we focus on code-signing certificates issued to South Korean companies. South Korea employs the code-signing PKI ecosystem with its own regional adaptations; thus, it is a perfect candidate to make a comparison. To begin with, we build a data collection pipeline and collect 455 certificates issued for South Korean companies and are potentially misused. We analyze those certificates based on three dimensions: (i) abusers, (ii) issuers, and (iii) the life-cycle of the certificate. We first identify that the strong regulation of a government can affect the market share of CAs. We also observe that several problems in certificate revocation: (i) the certificates had issued by local companies that closed the code-signing business still exist, (ii) only 6.8% of the abused certificates are revoked, and (iii) eight certificates are not revoked properly. All of those could lead to extending the validity of certified malware in the wild. Moreover, we show that the number of abuse cases is high in South Korea, even though it has a small population. Our study implies that Korean security practitioners require immediate attention to code-signing PKI abuse cases to safeguard the entire ecosystem.

Recent measurement studies have highlighted security threats against the code-signing public key infrastructure (PKI), such as certificates that had been compromised or issued directly to the malware authors. The primary mechanism for mitigating these threats is to revoke the abusive certificates. However, the distributed yet closed nature of the code signing PKI makes it difficult to evaluate the effectiveness of revocations in this ecosystem. In consequence, the magnitude of signed malware threat is not fully understood. In this paper, we collect seven datasets, including the largest corpus of code-signing certificates, and we combine them to analyze the revocation process from end to end. Effective revocations rely on three roles: (1) discovering the abusive certificates, (2) revoking the certificates effectively, and (3) disseminating the revocation information for clients. We assess the challenge for discovering compromised certificates and the subsequent revocation delays. We show that erroneously setting revocation dates causes signed malware to remain valid even after the certificate has been revoked. We also report failures in disseminating the revocations, leading clients to continue trusting the revoked certificates.

Recent measurements of the Windows code-signing certificate ecosystem have highlighted various forms of abuse that allow malware authors to produce malicious code carrying valid digital signatures. However, the underground trade that allows miscreants to acquire such certificates is not well understood. In this paper, we illuminate two aspects of this trade. First, we investigate 4 leading vendors of Authenticode certificates, we document how they conduct business, and we estimate their market share. Second, we collect a data set of recently signed malware and we use it to study the relationships among malware developers, malware families and the certificates. We also use information from the black market to fingerprint the certificates traded and to identify when the are likely used to sign malware in the wild. Using these methods, we document a shift in the methods that malware authors employ to obtain valid digital signatures. While prior studies have reported the use of code-signing certificates that had been compromised or obtained directly from legitimate Certification Authorities, we observe that, in 2017, these methods have become secondary to purchasing certificates from underground vendors. We also find that the need to bypass platform protections such as Microsoft Defender SmartScreen plays a growing role in driving the demand for Authenticode certificates. Together, these findings suggest that the trade in certificates issued for abuse represents an emerging segment of the underground economy.

Digitally signed malware can bypass system protection mechanisms that install or launch only programs with valid signatures. It can also evade anti-virus programs, which often forego scanning signed binaries. Known from advanced threats such as Stuxnet and Flame, this type of abuse has not been measured systematically in the broader malware landscape. In particular, the methods, effectiveness window, and security implications of code-signing PKI abuse are not well understood. We propose a threat model that highlights three types of weaknesses in the code-signing PKI. We overcome challenges specific to code-signing measurements by introducing techniques for prioritizing the collection of code signing certificates that are likely abusive. We also introduce an algorithm for distinguishing among different types of threats. These techniques allow us to study threats that breach the trust encoded in the Windows code signing PKI. The threats include stealing the private keys associated with benign certificates and using them to sign malware or by impersonating legitimate companies that do not develop software and, hence, do not own code-signing certificates. Finally, we discuss the actionable implications of our findings and propose concrete steps for improving the security of the code-signing ecosystem.

The growing commoditization of the underground economy has given rise to malware delivery networks, which charge fees for quickly delivering malware or unwanted software to a large number of hosts. To provide this service, a key method is the orchestration of silent delivery campaigns, which involve a group of downloaders that receive remote commands and that deliver their payloads without any user interaction. These campaigns have not been characterized systematically, unlike other aspects of malware delivery networks. Moreover, silent delivery campaigns can evade detection by relying on inconspicuous downloaders on the client side and on disposable domain names on the server side. We describe Beewolf, a system for detecting silent delivery campaigns from Internet-wide records of download events. The key observation behind our system is that the downloaders involved in these campaigns frequently retrieve payloads in lockstep. Beewolf identifies such locksteps in an unsupervised and deterministic manner. By exploiting novel techniques and empirical observations, Beewolf can operate on streaming data. We utilize Beewolf to study silent delivery campaigns at scale, on a data set of 33.3 million download events. This investigation yields novel findings, e.g. malware distributed through compromised software update channels, a substantial overlap between the delivery ecosystems for malware and unwanted software, and several types of business relationships within these ecosystems. Beewolf achieves over 92% true positives and fewer than 5% false positives. Moreover, Beewolf can detect suspicious downloaders a median of 165 days ahead of existing anti-virus products and payload-hosting domains a median of 196 days ahead of existing blacklists.

Malware remains an important security threat, as miscreants continue to deliver a variety of malicious programs to hosts around the world. At the heart of all the malware delivery techniques are executable files (known as downloader trojans or droppers) that download other malware. Because the act of downloading software components from the Internet is not inherently malicious, benign and malicious downloaders are difficult to distinguish based only on their content and behavior. In this paper, we introduce the downloader-graph abstraction, which captures the download activity on end hosts, and we explore the growth patterns of benign and malicious graphs. Downloader graphs have the potential of exposing large parts of the malware download activity, which may otherwise remain undetected. By combining telemetry from anti-virus and intrusion-prevention systems, we reconstruct and analyze 19 million downloader graphs from 5 million real hosts. We identify several strong indicators of malicious activity, such as the growth rate, the diameter, and the Internet access patterns of downloader graphs. Building on these insights, we implement and evaluate a machine learning system for malware detection. Our system achieves a 96.0% true-positive rate, with a 1.0% false-positive rate, and detects malware an average of 9.24 days earlier than existing anti-virus products. We also perform an external validation by examining a sample of unlabeled files that our system detects as malicious, and we find that 41.41% are blocked by anti-virus products.

This paper develops the grasping/manipulating simulation system considering the micro/macro collision model. Previous haptics, especially the bilateral control systems, mainly focused on transmitting the sense of touch and position tracking. In order to expand haptic technology, it is necessary to consider the dynamics motion, such as the impulse force against the environment. For this reason, a collision model is implemented into an environment for a grasping/manipulating simulation system. By applying the collision model, the reaction force, which occurs between the robot and the environment, can be predicted. This implies more accurate interpretation of the dynamics between the robot and the object is available. The simulation platform is confirmed to be valid by comparing the experimental result with the simulation result of grasping/manipulating control. From these results, the relation between the environmental parameters and sampling period of the controller is clarified.

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