Phishing attacks are a growing threat to online security, with increasingly sophisticated and frequent tactics. This rise in cyber threats underscores the need for advanced detection methods. While the Internet is crucial for modern communication and commerce, it also exposes users to risks such as phishing, spamming, malware, and performance degradation attacks. Among these, malicious URLs, commonly embedded in static links within emails and websites, are a significant challenge in identifying and mitigating these attacks. This study proposes TrackPhish, a novel lightweight application that predicts URL legitimacy without visiting the associated website. The proposed model combines traditional word embeddings (Word2Vec, FastText, GloVe) with transformer models (BERT, RoBERTa, GPT-2) to create a comprehensive feature set fed into a Deep Learning (DL) model for detecting phishing URLs. The integration of these embeddings captures semantic relationships and contextual understanding of the text, generating a robust feature set enhanced by an attention mechanism to choose relevant features. The refined features are then used to train a One-Dimensional Convolutional Neural Network (1D CNN) model for phishing URL detection. The proposed model offers key advantages over existing methods, including independence from third-party features, adaptability for client-side deployment, and target-independent detection. Experimental results demonstrate the model’s effectiveness, achieving 95.41% accuracy with a low false positive rate of 1.44% on our dataset and an impressive 98.55% accuracy on benchmark datasets, outperforming existing baseline models. The proposed model represents a significant advancement over traditional methods, enhancing online security against phishing URLs.

Abstract Image Secret Sharing (ISS) is a cryptographic technique used to distribute secret images among multiple users. However, current Visual Secret Sharing (VSS) schemes produce a halftone image with only 50% contrast when reconstructing the original image. To overcome this limitation, the Randomized Image Secret Sharing (RISS) scheme was introduced. RISS achieves a higher contrast of 70% when extracting the secret image but comes with a high computational cost. This research paper presents a novel approach called Graphics Processing Unit (GPU)-based Randomized Image Secret Sharing (GRISS), which utilizes data parallelism within the RISS pipeline. The proposed technique also incorporates an Autoencoder-based Single Image Super-Resolution (ASISR) to enhance the contrast of the recovered image. The performance of GRISS is evaluated against RISS, and the contrast of the ASISR images is compared to current benchmark models. The results demonstrate that GRISS outperforms state-of-the-art models in both efficiency and effectiveness.

Key pre‐distribution in wireless sensor networks is an important area of research due to limited resource availability in sensor nodes. In this study, the authors propose a novel key pre‐distribution scheme for wireless sensor networks based on combinatorial design. The proposed scheme is used to assign secret keys to the sensor nodes so that they can securely communicate among themselves. It also aims to decrease the key storage overhead and improve the overall resiliency of the network. They use the ratio of links broken/affected and the ratio of nodes disconnected to measure the resiliency when some sensor nodes are compromised in the network. They observed that the proposed scheme reduces the key storage overhead in the network while maintaining the desired connectivity among all the sensor nodes. Further, the proposed scheme is more resilient than the majority of existing schemes present in the literature.

Randomised visual secret sharing is an encryption technique that utilises block‐based progressive visual secret sharing and discrete cosine transform (DCT) based reversible data embedding technique to recover a secret image. The recovery method is based on progressive visual secret sharing, which recovers the secret image block by block. The existing block based schemes achieve the highest contrast level of 50% for noise‐like and meaningful shares. The proposed scheme achieves a contrast level of 70–90% for noise‐like and 70–80% for meaningful shares. The enhancement of contrast is achieved by embedding additional information in the shares using DCT‐based reversible data embedding technique. Experimental results showed that the proposed scheme restores the secret image with better visual quality in terms of human visual system based parameters.

A cryptographic hash fuction is used to protect the authenticity of information. Commonly used hash functions require huge computational overhead which is not available in most of the IOT devices Hence light weight cryptographic hash functions such as ARMADILLO, DM-PRESENT, GLUON, PHOTON, QUARK, SIPHASH, SPNHASH, SPONGENTA [1-3, 5, 6, 8-10], have been proposed in the literature for IOT device applications. Permutation operations in these algorithms are time consuming operations. In this paper, we are modifying the permutation operation of DM-PRESENT and SPONGENT algorithms using GRP instruction to achieve faster execution of algorithms. Using our technique we are able to save 465 cycles for DM-PRESENT(for 128- bits) and 670 cycles for SPONGENT(for 80-bits) algorithm over table look up implementation. In comparison to SHIFT-AND-OR technique of implementation we are able to save 7,688 cycles for DM-PRESENT(for 128- bits) and 11,160 for SPONGENT(for 80-bits) algorithms.

Data aggregation in wireless sensor network is implemented to reduce the communication overhead and to reduce bandwidth utilization. Data confidentiality requires the sensor node to transmit the data in a secure manner so that the adversary is unable to read the data or transmit false data even if it compromises some of the sensor nodes or aggregation node. In this paper a distributed aggregation protocol using homomorphic trapdoor permutation is proposed. This protocol distributes the responsibility of key generation , aggregation and verification to different nodes to reduce the overall power consumption of the sensor network. The peer verification scheme is also proposed as a part of the protocol. Peer verification ensures the authentication of the data and sender node in the network, by at least k peer nodes. Security of the proposed protocol is analyzed against passive and active adversary model.

As the password based authentication systems are not able to meet the performance because they can be stolen, forgotten, cracked, sniffed and tampered with. Lateral thinking to this problem evolved the use of biometrics to authenticate the person uniquely. Since, the templates are stored in a centralized database there is possibility of tampering of templates. The objective of this paper is to combine cryptography with biometrics by apply minor changes to the iris templates to transform them and store in a database, and hence forth even the system is compromised, the template is safe from the wrong hands thus to improve the security of the system in a network. For determining the performance of the system digitized grayscale eye images from CASIA 1.0[3] iris image set is used. This authentication system consists of an automatic recognition of iris template using the password provided by the user. The system performance is tested using different key sizes 128 bit, 256 bit and 512 bit keys. We used Hamming distance for classification of iris templates, and templates are accepted to match if the statistical independence was failed. Experimental results showed that the system performed with perfect recognition on a set of 756 images and resulted in an accuracy rate of 96%.

Electronic Payments have gained tremendous popularity in the modern world. Credit/debit cards and online payments are in widespread use. Bringing electronic payments to the mobile world offers huge utility for mobile users. Lack of standardized protocols, interoperability and security are major roadblocks in developing a mobile payment infrastructure. A scheme called SEMOPS (Secure Mobile Payment Service) has already been proposed by A. Vilmos and S. Karnouskos. This proposed SEMOPS architecture addresses these problems. However, it will work inefficiently for micropayments due to a lot of computation and communication for every payment. Good micropayment support is extremely important for mobile payment systems to succeed. This work focusses on enabling efficient micropayment support in SEMOPS scheme. An analysis of the security and efficiency of the proposed method is given in this paper. Our new proposed method has been found to be very efficient for micropayments in SEMOPS.