ترغب بنشر مسار تعليمي؟ اضغط هنا

Revisiting and Evaluating Software Side-channel Vulnerabilities and Countermeasures in Cryptographic Applications

238   0   0.0 ( 0 )
 نشر من قبل Tianwei Zhang
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

We systematize software side-channel attacks with a focus on vulnerabilities and countermeasures in the cryptographic implementations. Particularly, we survey past research literature to categorize vulnerable implementations, and identify common strategies to eliminate them. We then evaluate popular libraries and applications, quantitatively measuring and comparing the vulnerability severity, response time and coverage. Based on these characterizations and evaluations, we offer some insights for side-channel researchers, cryptographic software developers and users. We hope our study can inspire the side-channel research community to discover new vulnerabilities, and more importantly, to fortify applications against them.

قيم البحث

اقرأ أيضاً

Internet of Things (IoT) consists of a large number of devices connected through a network, which exchange a high volume of data, thereby posing new security, privacy, and trust issues. One way to address these issues is ensuring data confidentiality using lightweight encryption algorithms for IoT protocols. However, the design and implementation of such protocols is an error-prone task; flaws in the implementation can lead to devastating security vulnerabilities. Here we propose a new verification approach named Encryption-BMC and Fuzzing (EBF), which combines Bounded Model Checking (BMC) and Fuzzing techniques to check for security vulnerabilities that arise from concurrent implementations of cyrptographic protocols, which include data race, thread leak, arithmetic overflow, and memory safety. EBF models IoT protocols as a client and server using POSIX threads, thereby simulating both entities communication. It also employs static and dynamic verification to cover the systems state-space exhaustively. We evaluate EBF against three benchmarks. First, we use the concurrency benchmark from SV-COMP and show that it outperforms other state-of-the-art tools such as ESBMC, AFL, Lazy-CSeq, and TSAN with respect to bug finding. Second, we evaluate an open-source implementation called WolfMQTT. It is an MQTT client implementation that uses the WolfSSL library. We show that tool detects a data race bug, which other approaches are unable to find. Third, to show the effectiveness of EBF, we replicate some known vulnerabilities in OpenSSL and CyaSSL (lately WolfSSL) libraries. EBF can detect the bugs in minimum time.
Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly t hreatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrectly. In this paper, we propose a kind of novel countermeasures based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It shows that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks.
Internet users increasingly rely on commercial virtual private network (VPN) services to protect their security and privacy. The VPN services route the clients traffic over an encrypted tunnel to a VPN gateway in the cloud. Thus, they hide the client s real IP address from online services, and they also shield the users connections from perceived threats in the access networks. In this paper, we study the security of such commercial VPN services. The focus is on how the client applications set up VPN tunnels, and how the service providers instruct users to configure generic client software. We analyze common VPN protocols and implementations on Windows, macOS and Ubuntu. We find that the VPN clients have various configuration flaws, which an attacker can exploit to strip off traffic encryption or to bypass authentication of the VPN gateway. In some cases, the attacker can also steal the VPN users username and password. We suggest ways to mitigate each of the discovered vulnerabilities.
Quantum key distribution (QKD) promises information theoretic secure key as long as the device performs as assumed in the theoretical model. One of the assumptions is an absence of information leakage about individual photon detection outcomes of the receiver unit. Here we investigate the information leakage from a QKD receiver due to photon emission caused by detection events in single-photon detectors (backflash). We test commercial silicon avalanche photodiodes and a photomultiplier tube, and find that the former emit backflashes. We study the spectral, timing and polarization characteristics of these backflash photons. We experimentally demonstrate on a free-space QKD receiver that an eavesdropper can distinguish which detector has clicked inside it, and thus acquire secret information. A set of countermeasures both in theory and on the physical devices are discussed.
Since 2016, all of four major U.S. operators have rolled out nationwide Wi-Fi calling services. They are projected to surpass VoLTE (Voice over LTE) and other VoIP services in terms of mobile IP voice usage minutes in 2018. They enable mobile users t o place cellular calls over Wi-Fi networks based on the 3GPP IMS (IP Multimedia Subsystem) technology. Compared with conventional cellular voice solutions, the major difference lies in that their traffic traverses untrustful Wi-Fi networks and the Internet. This exposure to insecure networks may cause the Wi-Fi calling users to suffer from security threats. Its security mechanisms are similar to the VoLTE, because both of them are supported by the IMS. They include SIM-based security, 3GPP AKA (Authentication and Key Agreement), IPSec (Internet Protocol Security), etc. However, are they sufficient to secure Wi-Fi calling services? Unfortunately, our study yields a negative answer. We conduct the first study of exploring security issues of the operational Wi-Fi calling services in three major U.S. operators networks using commodity devices. We disclose that current Wi-Fi calling security is not bullet-proof and uncover four vulnerabilities which stem from improper standard designs, device implementation issues and network operation slips. By exploiting the vulnerabilities, together with several state-of-the-art computer visual recognition technologies, we devise two proof-of-concept attacks: user privacy leakage and telephony harassment or denial of voice service (THDoS); both of them can bypass the security defenses deployed on mobile devices and the network infrastructure. We have confirmed their feasibility and simplicity using real-world experiments, as well as assessed their potential damages and proposed recommended solutions.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا