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The widespread use of smartphones and their constant connection to the Internet makes them vulnerable to phishing attacks. Phishing is the act of sending malicious content such as emails to unsuspecting individuals. Smishing, a hybrid of Short Message Service (SMS) and phishing is a well-known cybersecurity problem in which attackers send malicious SMS messages to their targets. This practice is deceptive and aims to mislead individuals into exposing personal information or completing certain activities through text messages. Although researchers have presented various techniques to detect smishing, there is still a lack of methods to significantly reduce false-positive predictions, which are incorrect classifications of legitimate messages as malicious. The proposed method leverages the effectiveness of deep learning to automatically extract significant features from text messages to determine whether it is smish or legitimate. Comparative analysis is performed with traditional machine learning models to highlight the superiority of deep learning models in smishing attack detection. This work aggregates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Results reveal that the proposed CNN-LSTM architecture shows robust performance by achieving a 0.9974 accuracy score and a high precision score, indicating a low number of false positives in detecting smishing attacks. The model also demonstrated high recall and F1-score, indicating robust performance. The proposed method has a lot of real-world implications, such as helping to design proactive defence mechanisms against smishing attacks and improving cybersecurity in the mobile communication sector.

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets.

The task of finding natural groupings within a dataset exploiting proximity of samples is known as clustering, an unsupervised learning approach. Density-based clustering algorithms, which identify arbitrarily shaped clusters using spatial dimensions and neighbourhood aspects, are sensitive to the selection of parameters. For instance, DENsity CLUstEring (DENCLUE)—a density-based clustering algorithm—requires a trial-and-error approach to find suitable parameters for optimal clusters. Earlier attempts to automate the parameter estimation of DENCLUE have been highly dependent either on the choice of prior data distribution (which could vary across datasets) or by fixing one parameter (which might not be optimal) and learning other parameters. This article addresses this challenge by learning the parameters of DENCLUE through the differential evolution optimisation technique without prior data distribution assumptions. Experimental evaluation of the proposed approach demonstrated consistent performance across datasets (synthetic and real datasets) containing clusters of arbitrary shapes. The clustering performance was evaluated using clustering validation metrics (e.g., Silhouette Score, Davies–Bouldin Index and Adjusted Rand Index) as well as qualitative visual analysis when compared with other density-based clustering algorithms, such as DPC, which is based on weighted local density sequences and nearest neighbour assignments (DPCSA) and Variable KDE-based DENCLUE (VDENCLUE).

Communication of the images over the network calls for an approach to ensure security and ownership protection while exchanging images over the network. This paper proposes an improved blind watermarking scheme capable of offering image authentication, protection, and detachment avoidance between the watermark and its corresponding image. The proposed technique is composed of three modules; generator, protector, and resizer. The generator deals with embedding and extracting the watermark from the host image. Protector deals with encryption and decryption using a high dimensional chaotic system. And resizer deals with compressing and decompressing the watermarked image. The novelty related scientific literature is the encryption of Singular Value Decomposition (SVD) components, and watermark in a criss-cross manner using logistic map along with hyperchaotic system to achieve the confidentiality and avoid false positive problem (FPP). Whereas, embedding into RGB (red, green, blue components) is centered on singular value decomposition and level-2 Discrete Wavelet Transform. The joint operation is performed at sender’s side and receiver’s side, i.e., encryption, embedding, and compression is performed at sender’s side whereas decompression and decryption procedures are performed at the receiver’s side. Additionally, compressing the data reduces the higher bandwidth requirements. The performance of the proposed scheme is based on subjective and objective evaluation. Experimental results such as PSNR (peak signal-to-noise ratio), NC (normalized correlation), BER (bit error rate), CR (compression ratio), NPCR (number of pixel change rate), and SSIM (structural similarity index) indicate that the proposed technique approaches the standard main level The best-observed values for PSNR, SSIM, NC, and CR are 73.2211, 0.998816, 0.9997, and 0.9025 respectively.

The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques.

Recently researchers and companies have shown significant interest in merging blockchain and the Internet of Things (IoT) to create a safe, reliable, and resilient communication platform. However, determining the proper role of blockchain in existing IoT contexts with minimum implications is a challenge. This work suggests a message schedule for a blockchain-based architecture with two access-level setting filters for incoming messages: critical and non-critical. The proposed work of the researchers divides the fog layer into two parts: action clusters and blockchain fog clusters. Similar to the three-layered IoT architecture, the action cluster and the main cloud data center work together for critical message requests. The blockchain fog cluster is dedicated to only the blockchain application’s requirements. In the fog layer, a fog broker is used to schedule critical and non-critical messages in the action and blockchain fog clusters, respectively. The proposed technique is compared to the existing Dual Fog-IoT architecture. The solution is also tested for fog and cloud computing resource utilization. The findings demonstrate that this architecture is feasible for varying percentages of receiving critical and non-critical messages. In addition to the inherent benefits of blockchain, the suggested paradigm reduces the system loss rate and offloads the cloud data center with minimal changes to the existing IoT ecosystem.

Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.

The Internet of Things (IoT) is one of the highly influencing and promising technologies of today’s world, consisting of sensor devices. The internet smoothly changes from an internet of people towards an Internet of Things (IoT), which allows different things and objects to connect wirelessly. Things and objects are grouped into IoT subgroups in the IoT system, which are called clusters, and each cluster is controlled by a central authority and checked by the broker’s help. A concept of keeping backup data is used to increase the lifespan of IoT subgroups by avoiding re-clustering overhead for smooth transmission of packets and increasing availability concerns. A novel approach is used for the selection of cluster head/broker and backup nodes simultaneously. Cluster head and Backup Storage Point node (BSP) remain the same unless and until the residual power of the broker/cluster head is greater than the threshold energy. A novel Energy Efficient Message scheduling algorithm EAAFTMS (An Energy-Aware Available and Fault-Tolerant System with Message Scheduling in IoT) is incorporated at broker node for smooth transmission of messages. This proposed approach is not only solving availability issues over the wireless network but also proved to be energy efficient by prolonging the battery-powered network lifetime. Simulation results prove EAAFTMS, many folds better than benchmark protocols. This system ensures fault-tolerant and available schemes for IoT systems while stabilizing the energy of the overall system. The results shown prove the effectiveness and efficiency of the proposed system.

Abstract This paper reports the enhanced performance of cholesterol oxidase (COx) conjugated CdSe/ZnS quantum dots (QDs) by using water-soluble mercaptoacitic acid (MAA) as linker. The functionalized MAA-CdSe/ZnS QDs conjugated in four different dilutions of cholesterol oxidase significantly affected QDs photoluminescence intensities, which affected the process of charge transfer from QDs to MAA. The conjugation of COx to MAA-QDs in increased dilutions resulted in the regain of PL intensities, which were attributed to the passivation of MAA HOMO/LUMO states. The electrochemical impedance spectroscopy and cyclic voltammetry of the conjugated QDs were performed to get study the charge transfer mechanism. The 1:1000 diluted COx conjugated MAA-CdSe/ZnS QDs was found to have the lowest charge transfer resistance of 228 Ω, the highest diffusion (~ 1.39 × 10–13 cm2/s) and charge transfer rates (~ 4.5 × 10–6 s−1) between the electrode and the redox species. The current study demonstrated the sensitivity of electrochemical and optical based detection on the alkaline.

Objective: To assess the effects of daily oral iron supplementation during pregnancy on the histomorphology of rat placenta. Study Design: Randomized controlled trial. Place and Duration of Study: NIH and Army Medical College Rawalpindi, from Oct 2010 to Jan 2011. Material and Methods: Thirty two adult female and ten adult male Sprague Dawley rats were taken. After pregnancy was confirmed they were divided randomly into two equal groups and Ferrous Sulphate (FeSO4) was administered at the dose of 0 mg/Kg body weight (control) and 10 mg/Kg body weight (Experimental), starting from gestation day 0 (GD 0). Half of the animals from each group were sacrificed at GD 17 and half at GD 20. The placentae were separated; sections were taken and processed for light microscopic examination. In H&E and PAS stained slides, thickness of labyrinthine zone, trophospongium zone and fetomaternal barrier was measured and number of giant cells were counted. Results: The results were assessed at GD 17 and GD 20. The results showed no significant difference in the histomorphology of placenta at GD 17. At GD 20, significant decrease in thickness of labyrinthine zone and trophospongium zone and significant reduction in the number of giant cells in experimental group as compared to the control group was noted. There is no significant difference in the thickness of fetomaternal barrier. Conclusion: Adequate iron supplementation is crucial for maintaining healthy pregnancy. Blood volume increases during pregnancy, so extra iron is needed to make more hemoglobin that is critical for the normal development of placenta especially duing the last trimester. There should be increased awareness regarding the need of healthy diet and iron supplementation at the beginning of the pregnancy.

Communication security deals with attributes such as confidentiality, integrity, and availability. The current strategies used to achieve covertness of communication employs encryption. Encryption techniques minimize eavesdropping on the conversation between the conversing parties by transforming the message into an unreadable form. However, it does not prevent or discourage eavesdroppers from stealing and attempting to decrypt the encrypted messages using a brute-force attack or by randomly guessing the key. The probability of the eavesdropper acquiring the key and recovering the message is high as he/she can distinguish a correct key from incorrect keys based on the output of the decryption. This is because a message has some structure-texts, pictures, and videos. Thus, an attempt at decrypting with a wrong key yields random gibberish that does not comply with the expected structure. Furthermore, the consistent increase in computational power implies that stolen encrypted data may gradually debilitate to a brute-force attack. Thus, causing the eavesdropper to learn the content of the message. To this end, the objective of this research is to reinforce the current encryption measures with a decoy-based deception model where the eavesdropper is discouraged from stealing encrypted message by confounding his resources and time. Our proposed model leverages its foundation from decoys, deception, and artificial intelligence. An instant messaging application was developed and integrated with the proposed model as a proof of concept. Further details regarding the design, analysis, and implementation of the proposed model are substantiated. The result shows that the proposed model reinforces state-of-the-art encryption schemes and will serve as an effective component for discouraging eavesdropping and curtailing brute-force attack on encrypted messages.

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

Objective: To study the effect of diltiazem on monosodium glutamate induced histomorphological changes in the fallopian tube. Study Design: Randomized controlled trial. Material and Methods: In this experimental study 30 adult female Sprague Dawley rats of average weight of 500g were randomly divided into three groups A (control group), B and C with 10 rats in each. The experimental group B was given 0.08 mg/kg of monosodium glutamate (MSG) orally and experimental group C was given 0.08 mg/kg of MSG and 10 mg/kg of diltiazem in distilled water for 14 days. The control group (A) received only laboratory diet prepared at National Institute of Health (NIH) Islamabad with liberal water intake. Blood was taken from each animal through intracardiac route. The rats were sacrificed on 15th day of the experiment. The fallopian tubes were carefully dissected out and fixed in 10% formalin for routine histological procedures. Results: The fallopian tubes of the control group A showed normal histological features, a well-defined tubal wall consisted of three distinct layers. Oviducts of group B also showed three distinct layers. The cells forming the external serosal layer were flat with centrally located nuclei giving the appearance of simple. The cells forming the intermediate muscular layer were fusiform in appearance with centrally placed nuclei giving the appearance of smooth muscle cells. The cells in the inner mucosal layer were taller than broader with oval nuclei giving the appearance of simple columnar epithelium resting on the basement membrane. There were marked vacuolations appearing in the stroma cells. The blood vessels in stroma were dilated. The histological sections of oviducts of group C showed three layers: serosa, myosalpinx, endosalpinx the external serosal layer were flat with centrally located nuclei. At the inner side of this layer, there were lying irregularly scattered thin fibers, blood vessels and irregular shaped cells. The stromal cells were loosely arranged and in all animals of this group there was no vacuolations in stroma of this group. The mean serum estrogen level for group A was 83.53 ± 9.87 pmol/l. The mean serum estrogen level in group B was 136.97 ± 12.51 pmol/l. The mean serum estrogen level in group C was 64.79 ± 17.85 pmol/l. Conclusion: The findings obtained from our study after administration of MSG and diltiazem showed stromal vacuolations in the fallopian tube of rat leading to change in its functional aspect and impairing the female fertility while treatment with diltiazem prevents these effects.

Objective: To observe the preventive effects of Punica granatum (pomegranate) juice and extract on weight and volume of steroid induced mice kidneys. Study Design: Lab based randomized control trial. Place and Duration of Study: Department of Anatomy, Army Medical College (AMC) Rawalpindi, in cooperation with National Institute of Health (NIH) Islamabad, from Apr to May 2015. Material and Methods: Forty male and female healthy (BALB/c strain) mice with average weight of 25-30gms were divided into four groups, having ten animals each. Group A was a control group and groups B, C and D were experimental groups. Mice in experimental groups were injected Nandrolone decanoate (ND) at the dosage of 1 mg/100 gm of body weight, intramuscularly (I/M) in the hind limb once a week for 8 weeks. Mice in experimental group C were also given pomegranate juice (PJ) at the dosage of 3ml/kg of body weight through oral gavage tube for 8 weeks daily and mice in experimental group D were given pomegranate peel extract (PPE) at the dosage of 200mg/kg of body weight by oral gavage tube for 8 weeks daily. Intergroup comparison of results of experimental groups B, C and D with each other and with control group A was done. Results: The weight and volume of kidneys were significantly different in ND treated experimental group B and difference in both these parameters was seen in pomegranate administered experimental groups C and D. Conclusion: Pomegranate in both forms, as pomegranate juice and peels extract, equally prevent the weight and volume of steroid induced kidneys. Keywords: Pomegranate, Steroids, Volume, Weight.

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

Background: To study the reasons of acceptability, efficacy,short term and long term side effects of Levonorgestrel Subdermal implant as long term reversible contraceptive. Methods: In this descriptive study women (n=13)were counselled regarding Levonorgestrel SDI. Levonorgestrel SDI was inserted sub-dermally in the upper arm under local anaesthesia. Information was collected regarding study specific variables. Follow up was performed at 1 and 6 months and 12,24 and 36 months. Results: Maximum number of patients wanting levonorgestrel subdermal implants were between 30-35 years. Majority of women opting for this procedure had 3-4 live issues. Majority (44%) of the women choose this method based on personal information. Most common side effect was heavy and irregular vaginal bleeding 18.7% after 6 months, and 17.3% after 12 months. Patients advised medical treatment for heavy menstrual bleeding did not respond well(cyclical progestogens and tranexamic acid). Fifteen percent of users got SDI removed by end of 12 months, mainly due to above side effects. The 3- year cumulative pregnancy rate was zero. Conclusion: Levonorgestrel SDI can be an acceptable, convenient and effective long term reversible contraceptive method. Menstrual disturbances and feeling of weight gain are the major reported side effects, leading to discontinuation of the method.