Phishing attacks by malicious URL/web links are common nowadays. The user data, such as login credentials and credit card numbers can be stolen by their careless clicking on these links. Moreover, this can lead to installation of malware on the target systems to freeze their activities, perform ransomware attack or reveal sensitive information. Recently, GAN-based models have been attractive for anti-phishing URLs. The general motivation is using Generator network (G) to generate fake URL strings and Discriminator network (D) to distinguish the real and the fake URL samples. This is operated in adversarial way between G and D so that the synthesized URL samples by G become more and more similar to the real ones. From the perspective of cybersecurity defense, GAN-based motivation can be exploited for D as a phishing URL detector or classifier. This means after training GAN on both malign and benign URL strings, a strong classifier/detector D can be achieved. From the perspective of cyberattack, the attackers would like to to create fake URLs that are as close to the real ones as possible to perform phishing attacks. This makes them easier to fool users and detectors. In the related proposals, GAN-based models are mainly exploited for anti-phishing URLs. There have been no evaluations specific for GAN-generated fake URLs. The attacker can make use of these URL strings for phishing attacks. In this work, we propose to use TLD (Top-level Domain) and SSIM (Structural Similarity Index Score) scores for evaluation the GAN-synthesized URL strings in terms of the structural similariy with the real ones. The more similar in the structure of the GAN-generated URLs are to the real ones, the more likely they are to fool the classifiers. Different GAN models from basic GAN to others GAN extensions of DCGAN, WGAN, SEQGAN are explored in this work. We show from the intensive experiments that D classifier of basic GAN and DCGAN surpasses other GAN models of WGAN and SegGAN. The effectiveness of the fake URL patterns generated from SeqGAN is the best compared to other GAN models in both structural similarity and the ability in deceiving the phishing URL classifiers of LSTM (Long Short Term Memory) and RF (Random Forest).

Biometric authentication has become an essential security aspect in today's digitized world. As limitations of the Unimodal biometric system increased, the need for multimodal biometric has become more popular. More research has been done on multimodal biometric systems for the past decade. sclera and periocular biometrics have gained more attention. The segmentation of sclera is a complex task as there is a chance of losing some of the features of sclera vessel patterns. In this paper we proposed a patch-based sclera and periocular segmentation. Experiments was conducted on sclera patches, periocular patches and sclera-periocular patches. These sclera and periocular patches are trained using deep learning neural networks. The deep learning network CNN is applied individually for sclera and periocular patches, on a combination of three Data set. The data set has images with occlusions and spectacles. The accuracy of the proposed sclera-periocular patches is 97.3%. The performance of the proposed patch-based system is better than the traditional segmentation methods.

Information security is an important part of the current interactive world. It is very much essential for the end-user to preserve the confidentiality and integrity of their sensitive data. As such, information encoding is significant to defend against access from the non-authorized user. This paper is presented with an aim to build a system with a fusion of Cryptography and Steganography methods for scrambling the input image and embed into a carrier media by enhancing the security level. Elliptic Curve Cryptography (ECC) is helpful in achieving high security with a smaller key size. In this paper, ECC with modification is used to encrypt and decrypt the input image. Carrier media is transformed into frequency bands by utilizing Discrete Wavelet Transform (DWT). The encrypted hash of the input is hidden in high-frequency bands of carrier media by the process of Least-Significant-Bit (LSB). This approach is successful to achieve data confidentiality along with data integrity. Data integrity is verified by using SHA-256. Simulation outcomes of this method have been analyzed by measuring performance metrics. This method enhances the security of images obtained with 82.7528db of PSNR, 0.0012 of MSE, and SSIM as 1 compared to other existing scrambling methods.

Remote access technologies encrypt data to enforce policies and ensure protection. Attackers leverage such techniques to launch carefully crafted evasion attacks introducing malware and other unwanted traffic to the internal network. Traditional security controls such as anti-virus software, firewall, and intrusion detection systems (IDS) decrypt network traffic and employ signature and heuristic-based approaches for malware inspection. In the past, machine learning (ML) approaches have been proposed for specific malware detection and traffic type characterization. However, decryption introduces computational overheads and dilutes the privacy goal of encryption. The ML approaches employ limited features and are not objectively developed for remote access security. This paper presents a novel ML-based approach to encrypted remote access attack detection using a weighted random forest (W-RF) algorithm. Key features are determined using feature importance scores. Class weighing is used to address the imbalanced data distribution problem common in remote access network traffic where attacks comprise only a small proportion of network traffic. Results obtained during the evaluation of the approach on benign virtual private network (VPN) and attack network traffic datasets that comprise verified normal hosts and common attacks in real-world network traffic are presented. With recall and precision of 100%, the approach demonstrates effective performance. The results for k-fold cross-validation and receiver operating characteristic (ROC) mean area under the curve (AUC) demonstrate that the approach effectively detects attacks in encrypted remote access network traffic, successfully averting attackers and network intrusions.

In the cloud computing platform, DDoS (Distributed Denial-of-service) attacks are one of the most commonly occurring attacks. Research studies on DDoS mitigation rarely considered the data shift problem in real-time implementation. Concurrently, existing studies have attempted to perform DDoS attack detection. Nevertheless, they have been deficient regarding the detection rate. Hence, the proposed study proposes a novel DDoS mitigation scheme using LCDT-M (Log-Cluster DDoS Tree Mitigation) framework for the hybrid cloud environment. LCDT-M detects and mitigates DDoS attacks in the Software-Defined Network (SDN) based cloud environment. The LCDT-M comprises three algorithms: GFS (Greedy Feature Selection), TLMC (Two Log Mean Clustering), and DM (Detection-Mitigation) based on DT (Decision Tree) to optimize the detection of DDoS attacks along with mitigation in SDN. The study simulated the defined cloud environment and considered the data shift problem during the real-time implementation. As a result, the proposed architecture achieved an accuracy of about 99.83%, confirming its superior performance.

Activities in network traffic can be broadly classified into two categories: normal and malicious. Malicious activities are harmful and their detection is necessary for security reasons. The intrusion detection process monitors network traffic to identify malicious activities in the system. Any algorithm that divides objects into two categories, such as good or bad, is a binary class predictor or binary classifier. In this paper, we utilized the Nearest Neighbor Distance Variance (NNDV) classifier for the prediction of intrusion. NNDV is a binary class predictor and uses the concept of variance on the distance between objects. We used KDD CUP 99 dataset to evaluate the NNDV and compared the predictive accuracy of NNDV with that of the KNN or K Nearest Neighbor classifier. KNN is an efficient general purpose classifier, but we only considered its binary aspect. The results are quite satisfactory to show that NNDV is comparable to KNN. Many times, the performance of NNDV is better than KNN. We experimented with normalized and unnormalized data for NNDV and found that the accuracy results are generally better for normalized data. We also compared the accuracy results of different cross validation techniques such as 2 fold, 5 fold, 10 fold, and leave one out on the NNDV for the KDD CUP 99 dataset. Cross validation results can be helpful in determining the parameters of the algorithm.

Researchers have been paying close attention to the wireless sensor (WSN) networks area because of its variety of applications, including industrial management, human detection, and health care management. In Wireless Sensor (WSN) Network, consumption of efficient energy is a challenging problem. Many clustering techniques were used for balancing the load of WSN network. In clustering, the cluster head (CH) is selected as a relay node with greater power which is compared with the nodes of non-CH. In the existing system, it uses LBC-COFL algorithm to reduce the energy consumption problem. To overcome this problem, the proposed system uses Quasi oppositional based Jaya load balancing strategy with cluster head (QOJ-LCH) selection protocol to boost the lifespan of network and energy consumption. The QOJ-LCH method improves the relay nodes life and shares the load on relay nodes equitably across the network to enhance the lifespan. It also reduces the load-balancing problems in Wireless Sensor networks. It uses two routing methods single-hop and multiple-hop. The proposed QOJ-LCH with cluster head selection method enhances the network’s lifespan, total amount of power utilization and the active sensor devices present in the Single-hop routing ,it worked with 1600 rounds in network and 300 sensor nodes, for Multiple-hop routing, it worked with 1800 rounds in network and 350 sensor nodes. It achieves better performance, scalability and reliability.