A smart-card-based user authentication scheme for wireless sensor networks (hereafter referred to as a SCA-WSN scheme) is designed to ensure that only users who possess both a smart card and the corresponding password are allowed to gain access to sensor data and their transmissions. Despite many research efforts in recent years, it remains a challenging task to design an efficient SCA-WSN scheme that achieves user anonymity. The majority of published SCA-WSN schemes use only lightweight cryptographic techniques (rather than public-key cryptographic techniques) for the sake of efficiency, and have been demonstrated to suffer from the inability to provide user anonymity. Some schemes employ elliptic curve cryptography for better security but require sensors with strict resource constraints to perform computationally expensive scalar-point multiplications; despite the increased computational requirements, these schemes do not provide user anonymity. In this paper, we present a new SCA-WSN scheme that not only achieves user anonymity but also is efficient in terms of the computation loads for sensors. Our scheme employs elliptic curve cryptography but restricts its use only to anonymous user-to-gateway authentication, thereby allowing sensors to perform only lightweight cryptographic operations. Our scheme also enjoys provable security in a formal model extended from the widely accepted Bellare-Pointcheval-Rogaway (2000) model to capture the user anonymity property and various SCA-WSN specific attacks (e.g., stolen smart card attacks, node capture attacks, privileged insider attacks, and stolen verifier attacks).

Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, “Which features, classifiers, and evaluation metrics are better for malware familial classification”? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered.

Validation methods for hard real-time jobs are usually performed based on the maximum execution time. The actual execution time of jobs are assumed to be known only when the jobs arrive or not known until they finish. A predictable algorithm must guarantee that it can generate a schedule for any set of jobs such that the finish time for the actual execution time is no later than the finish time for the maximum execution time. It is known that any job-level fixed priority algorithm (such as Earliest Deadline First) is predictable. However, job-level dynamic priority algorithms (such as Least Laxity First) may or may not. In this paper, we investigate the predictability of a job-level dynamic priority algorithm EDZL (Earliest Deadline Zero Laxity). We show that EDZL is predictable on the domain of integers regardless of the knowledge of the actual execution times. Based on this result, furthermore, we also show that EDZL can successfully schedule any periodic task set if the total utilization is not greater than (m + 1)/2, where m is the number of processors.

Code obfuscation is a technique that makes it difficult for code analyzers to understand a program by transforming its structures or operations while maintaining its original functionality. Android app developers often employ obfuscation techniques to protect business logic and core algorithm inside their app against reverse engineering attacks. On the other hand, malicious app writers also use obfuscation techniques to avoid being detected by anti-malware software. If malware analysts can mitigate the code obfuscation applied to malicious apps, they can analyze and detect the malicious apps more efficiently. This paper proposes a new tool, Deoptfuscator , to detect obfuscated an Android app and to restore the original source codes. Deoptfuscator detects an app control-flow obfuscated by DexGuard and tries to restore the original control-flows. Deoptfuscator deobfuscates in two steps: it determines whether an control-flow obfuscation technique is applied and then deobfuscates the obfuscated codes. Through experiments, we analyze how similar a deobfuscated app is to the original one and show that the obfuscated app can be effectively restored to the one similar to the original. We also show that the deobfuscated apps run normally.

We present the first provably-secure three-party password-only authenticated key exchange (PAKE) protocol that can run in only two communication rounds. Our protocol is generic in the sense that it can be constructed from any two-party PAKE protocol. The protocol is proven secure in a variant of the widely-accepted model of Bellare, Pointcheval and Rogaway (2000) without any idealized assumptions on the cryptographic primitives used. We also investigate the security of the two-round, three-party PAKE protocol of Wang, Hu and Li (2010) and demonstrate that this protocol cannot achieve implicit key authentication in the presence of an active adversary.