More Like this

1. DAHash: Distribution Aware Tuning of Password Hashing Costs

   https://doi.org/10.1007/978-3-662-64331-0_20  

   Bai, Wenjie ; Blocki, Jeremiah. ( January 2021 , International Conference on Financial Cryptography and Data Security (FC 2021))

   Borisov, N. (Ed.)

   An attacker who breaks into an authentication server and steals all of the cryptographic password hashes is able to mount an offline-brute force attack against each user’s password. Offline brute-force attacks against passwords are increasingly commonplace and the danger is amplified by the well documented human tendency to select low-entropy password and/or reuse these passwords across multiple accounts. Moderately hard password hashing functions are often deployed to help protect passwords against offline attacks by increasing the attacker’s guessing cost. However, there is a limit to how “hard” one can make the password hash function as authentication servers are resource constrained and must avoid introducing substantial authentication delay. Observing that there is a wide gap in the strength of passwords selected by different users we introduce DAHash (Distribution Aware Password Hashing) a novel mechanism which reduces the number of passwords that an attacker will crack. Our key insight is that a resource-constrained authentication server can dynamically tune the hardness parameters of a password hash function based on the (estimated) strength of the user’s password. We introduce a Stackelberg game to model the interaction between a defender (authentication server) and an offline attacker. Our model allows the defender to optimize the parameters of DAHash e.g., specify how much effort is spent in hashing weak/moderate/high strength passwords. We use several large scale password frequency datasets to empirically evaluate the effectiveness of our differentiated cost password hashing mechanism. We find that the defender who uses our mechanism can reduce the fraction of passwords that would be cracked by a rational offline attacker by up to 15%. 

   more » « less
2. Cyber Threat Intelligence Discovery using Machine Learning from the Dark Web

   https://doi.org/10.58729/1941-6687.1436  

   Zenebe, Azene ( January 2022 , Communications of the IIMA)

   Cyber threat intelligence (CTI) is an actionable information or insight an organization uses to understand potential vulnerabilities it does have and threats it is facing. One important CTI for proactive cyber defense is exploit type with possible values system, web, network, website or Mobile. This study compares the performance of machine learning algorithms in predicating exploit types using form posts in the dark web, which is a semi- structured dataset collected from dark web. The study uses the CRISP data science approach. The results of the study show that machine learning algorithms which are function-based including support vector machine and deep-learning using artificial neural network are more accurate than those algorithms which are based on tree including Random Forest and Decision-Tree for CTI discovery from semi-structured dataset. Future research will include the use of high-performance computing and advanced deep-learning algorithms. 

   more » « less
3. Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study

   https://doi.org/10.3390/fi13070164  

   Gwyn, Tony ; Roy, Kaushik ; Atay, Mustafa ( July 2021 , Future Internet)

   null (Ed.)

   In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based-facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, as well as F1-Score. The most favorable configurations of CNN should be documented as an effective way to potentially augment the current username/password standard by increasing the current method’s security with additional facial biometrics. 

   more » « less
4. Robustify ML-Based Lithography Hotspot Detectors

   https://doi.org/10.1145/3508352.3549389  

   Pan, Jingyu ; Chang, Chen-Chia ; Xie, Zhiyao ; Hu, Jiang ; Chen, Yiran ( October 2022 , 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD))

   Deep learning has been widely applied in various VLSI design automation tasks, from layout quality estimation to design optimization. Though deep learning has shown state-of-the-art performance in several applications, recent studies reveal that deep neural networks exhibit intrinsic vulnerability to adversarial perturbations, which pose risks in the ML-aided VLSI design flow. One of the most effective strategies to improve robustness is regularization approaches, which adjust the optimization objective to make the deep neural network generalize better. In this paper, we examine several adversarial defense methods to improve the robustness of ML-based lithography hotspot detectors. We present an innovative design rule checking (DRC)-guided curvature regularization (CURE) approach, which is customized to robustify ML-based lithography hotspot detectors against white-box attacks. Our approach allows for improvements in both the robustness and the accuracy of the model. Experiments show that the model optimized by DRC-guided CURE achieves the highest robustness and accuracy compared with those trained using the baseline defense methods. Compared with the vanilla model, DRC-guided CURE decreases the average attack success rate by 53.9% and increases the average ROC-AUC by 12.1%. Compared with the best of the defense baselines, DRC-guided CURE reduces the average attack success rate by 18.6% and improves the average ROC-AUC by 4.3%. 

   more » « less
5. Accountable authentication with privacy protection: The Larch system for universal login

   Dauterman, Emma ; Lin, Danny ; Corrigan-Gibbs, Henry ; Mazieres, David ( January 2023 , 17th USENIX Symposium on Operating Systems Design and Implementation)

   Credential compromise is hard to detect and hard to mitigate. To address this problem, we present larch, an accountable authentication framework with strong security and privacy properties. Larch protects user privacy while ensuring that the larch log server correctly records every authentication. Specifically, an attacker who compromises a user’s device cannot authenticate without creating evidence in the log, and the log cannot learn which web service (relying party) the user is authenticating to. To enable fast adoption, larch is backwards-compatible with relying parties that support FIDO2, TOTP, and password-based login. Furthermore, larch does not degrade the security and privacy a user already expects: the log server cannot authenticate on behalf of a user, and larch does not allow relying parties to link a user across accounts. We implement larch for FIDO2, TOTP, and password-based login. Given a client with four cores and a log server with eight cores, an authentication with larch takes 150ms for FIDO2, 91ms for TOTP, and 74ms for passwords (excluding preprocessing, which takes 1.23s for TOTP). 

   more » « less