Unmanned aerial vehicles have found applications in fields such as environmental monitoring and the military. Although the collected data in some of these application domains are sensitive, public channels are deployed during the communication process. Therefore, many protocols have been presented to preserve the confidentiality and integrity of the exchanged messages. However, numerous security and performance challenges have been noted in the majority of these protocols. In this paper, an elliptic curve cryptography (ECC) and symmetric key-based protocol is presented. The choice of ECC was informed by its relatively shorter key sizes compared to other asymmetric encryption algorithms such as the Rivest–Shamir–Adleman (RSA) algorithm. Security analysis showed that this protocol provides mutual authentication, session key agreement, untraceability, anonymity, forward key secrecy, backward key secrecy, and biometric privacy. In addition, it is robust against smart card loss, password guessing, known secret session temporary information (KSSTI), privileged insider, side-channeling, impersonation, denial-of-service (DoS), and man-in-the-middle (MitM) attacks. The comparative performance evaluation showed that it has relatively low computation, storage, and communication complexities.

Highly sensitive information about people’s social life and daily activities flows in smart home networks. As such, if attackers can manage to capture or even eavesdrop on this information, the privacy of the users can be compromised. The consequences can be far-reaching, such as knowing the status of home occupancy that can then facilitate burglary. To address these challenges, approaches such as data aggregation and signcryption have been utilized. Elliptic curve cryptography, bilinear pairing, asymmetric key cryptosystem, blockchain, and exponential operations are among the most popular techniques deployed to design these security solutions. However, the computational, storage and communication complexities exhibited by the majority of these techniques are too high. This renders these techniques unsuitable for smart home components such as smart switches and sensors. Some of these schemes have centralized architectures, which present some single points of failure. In this paper, symmetric key authentication procedures are presented for smart home networks. The proposed protocol leverages on cryptographic primitives such as one-way hashing and bitwise exclusive-Or operations. The results indicate that this scheme incurs the lowest communication, storage, and computation costs compared to other related state-of-the-art techniques. Empirically, our protocol reduces the communication and computation complexities by 16.7% and 57.7%, respectively. In addition, it provides backward key secrecy, robust mutual authentication, anonymity, forward key secrecy, and unlinkability. Moreover, it can effectively prevent attacks such as impersonation, session hijacking, denial of service, packet replays, man-in-the-middle, and message eavesdropping.

A mobile phone widely used nowadays and becomes more powerful than a computer for a few years. It can do the same function that a computer can do. Day after day the mobile phone has developed and become more important, but the confidential information remains to keep the challenge of security. This paper proposes a method for keeping the information by hiding text in an image. The proposed method increases randomization depending on the random key. It enhances security via encrypting the covered image with the key. The proposed method implemented by Java Platform Micro Edition (J2ME), since it supports a lot of APIs and features to mobile applications.

Currently, with the rapid developments communication technologies, large number of trustworthy online systems and facilities has been introduced. The cybersecurity is quiet on the rise threat from unauthorized; such security threats can be detected by an intrusion detection system. Thus, enhancing the intrusion detection system is main object of numbers of research and developers for monitoring the network security. Addressing challenges of big data in intrusion detection is one issue faced the researchers and developers due to dimensionality reduction in network data. In this paper, hybrid model is proposed to handle the dimensionality reduction in intrusion detection system. The genetic algorithm was applied as preprocessing steps for selecting most significant features from entire big network dataset. The genetic algorithm was applied to generate subset of relevant features from network data set for handling dimensionality reduction. The Support Vector Machine (SVM) algorithm was processed the relevant features for detecting intrusion. The NSL-KDD standard data was considered to test the performance of the hybrid model. Standard evaluation metrics were employed to presents the results of hybrid model. It is concluded that the empirical results of hybrid outperformed the performance of existing systems.