Underwater Wireless Sensor Networks (UWSNs) consist of sensor nodes deployed within bodies of water. Wireless connections and the harsh underwater environment make sensors susceptible to a variety of malevolent attacks and security concerns. The fundamental concern of the UWSN is secure and reliable communication with low energy consumption. Many cryptographic solutions have been proposed to deal with such constraints. Signcryption is one of these cryptosystems, integrating signature and encryption to reduce computational costs relative to other cryptosystems. Numerous signcryption schemes based on ElGamal, bilinear pairing, RSA, and Elliptic Curve Cryptography (ECC) have been proposed. The inadequacies of these schemes include increased computation and communication overhead, the absence of certain security features, and a high memory demand. In this study, we introduced a lightweight certificateless signcryption system based on Hyper-elliptic Curve Cryptography (HCC). The system significantly reduces computing and communication costs, making it ideal for resource-constrained environments. Our solution meets the necessary security requirements while maintaining forward secrecy. The security analysis of our scheme indicates that the proposed method is efficient and effective.

As the technology era progresses, many opportunities are brought to the healthcare industry. With the support of technology and Internet of Things platforms, e-healthcare is now more common than ever. However, the sensitive nature of healthcare records makes them vulnerable to many attacks. Therefore, a privacy-enabled framework for cloud-based e-healthcare systems is proposed to achieve privacy-preserved and secured communication in e-healthcare. The analysis of the proposed protocol is presented in this article to demonstrate that it is secure against all well-known security attacks and provides patient anonymity, doctor anonymity, and patient and doctor unlinkability while ensuring data confidentiality. Additionally, the security simulations are performed using the Automated Validation of Internet Security Protocols and Applications tool. We also performed the proposed framework's performance analysis and compared it with existing frameworks. The analysis result indicates that the proposed framework achieves encouraging performance over other frameworks while ensuring security.

Repackaging refers to the core process of unpacking a software package, then repackaging it after a probable modification to the decompiled code and/or to other resource files. In the case of repackaged malware, benign apps are injected with a malicious payload. Repackaged malware pose a serious threat to the Android ecosystem. Moreover, repackaged malware and benign apps share more than 80% of their features, which makes detection a challenging problem. This paper presents a novel technique based on multi-view learning to address this challenge of detecting repackaged malware. Multi-View Learning is a technique where data from multiple distinct feature groups, referred to as views, are fused to improve the model’s generalization performance. In the context of Android, we define views as different components of the app, such as permissions, APIs, sensor usage, etc. We analyzed 15,297 repackaged app pairs and extracted seven different views to represent an app. We perform an ablation study to identify which view(s) contribute more to the classification. The model was trained end-to-end to jointly learn appropriate features and to perform the classification. We show that our approach achieves accuracy and an F1-score of 97.46% and 0.98, respectively.

Finding similar code in software systems can guide several software engineering tasks such as code maintenance, program understanding, and code reuse.Similar code detection has been actively studied in the past.In the paper, we propose a novel approach that leverages compiler optimizations to transform semantically similar code and detect similar programs.The key observation of our work is that the compiler optimizations can be used to smooth out source code level idiosyncrasies introduced by the developers, thus making the optimized programs, for the same task, similar in structure.The similarity in structure can then be used to classify the programs.We conducted experiments on the Google CodeJam dataset to demonstrate the effectiveness of our approach.The experimental results show that our technique can achieve up to 85% accuracy on the program classification task, which is an improvement of more than 25% over the source code level classification.

There are 1.4 billion Android devices around the globe. With so many users and apps comes the question of privacy and security. Some permissions are critical to the operation of any app. A system is required which can tell whether an app X should be given a permission Y. Our basic idea is that an app belonging to a particular category should request permissions most common in that category. Around 130 users contributed to our dataset which had over 3964 records and 830 unique apps in 39 categories. A voting measure based on statistical mean value was used to decide the permissions to be given to particular app.