Face Recognition in the Scrambled Domain Using MK-RDA

Facial look identity is a vital mission by means of human-interacting structures that goal to be aware of versions within the human’s emotional state. the principle challenge or the crucial part in surveillance society is the privacy-shielding era. because the rapid improvement in the internet international it turns into very essential to scramble the pics in the video or files for the duration of transmission. in this the biometric identity of photographs or faces from scrambled pictures plays a completely tough mission. Numbers of various technology are carried out to provide privateness for the duration of surveillance or during transmission of video however they're lack of essential traits, like reversibility or visible fine maintenance. in lots of scrambling methods the faces are covered by a few animation which may additionally or may not cover all faces or it receives hard to recover pics from this technique. Many guide method also are us used by which we will unscramble an photo but they are no longer powerful that a good deal. to overcome all this matters we proposed a novel approach- Many-Kernel Random Discriminate analysis (MK-RDA) to find out discriminative patterns from chaotic indicators. structures get better accuracy bring about best photos. To PIE and ORL datasets has getting above ninety% accuracy

MuLViS: Multi-Level Encryption Based Security System for Surveillance Videos

Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and autonomous video analytics. Video captured by surveillance cameras in real-time often contains identifiable personal information, which must be privacy protected, sometimes along with the locations of the surveillance and other sensitive information. Within the Surveillance System, these videos are processed and stored on a variety of devices. The processing and storage heterogeneity of those devices, together with their network requirements, make real-time surveillance systems complex and challenging. This paper proposes a surveillance system, named as Multi-Level Video Security (MuLViS) for privacy-protected cameras. Firstly, a Smart Surveillance Security Ontology (SSSO) is integrated within the MuLViS, with the aim of autonomously selecting the privacy level matching the operating device's hardware specifications and network capabilities. Overall, along with its device-specific security, the system leads to relatively fast indexing and retrieval of surveillance video. Secondly, information within the videos are protected at the times of capturing, streaming, and storage by means of differing encryption levels. An extensive evaluation of the system, through visual inspection and statistical analysis of experimental video results, such as by the Encryption Space Ratio (ESR), has demonstrated the aptness of the security level assignments. The system is suitable for surveillance footage protection, which can be made General Data Protection Regulation (GDPR) compliant, ensuring that lawful data access respects individuals' privacy rights

Energy efficient security and privacy management in sensor clouds

Sensor Cloud is a new model of computing for Wireless Sensor Networks, which facilitates resource sharing and enables large scale sensor networks. A multi-user distributed system, however, where resources are shared, has inherent challenges in security and privacy. The data being generated by the wireless sensors in a sensor cloud need to be protected against adversaries, which may be outsiders as well as insiders. Similarly the code which is disseminated to the sensors by the sensor cloud needs to be protected against inside and outside adversaries. Moreover, since the wireless sensors cannot support complex, energy intensive measures, the security and privacy of the data and the code have to be attained by way of lightweight algorithms. In this work, we first present two data aggregation algorithms, one based on an Elliptic Curve Cryptosystem (ECC) and the other based on symmetric key system, which provide confidentiality and integrity of data against an outside adversary and privacy against an in network adversary. A fine grained access control scheme which works on the securely aggregated data is presented next. This scheme uses Attribute Based Encryption (ABE) to achieve this objective. Finally, to securely and efficiently disseminate code in the sensor cloud, we present a code dissemination algorithm which first reduces the amount of code to be transmitted from the base station. It then uses Symmetric Proxy Re-encryption along with Bloom filters and HMACs to protect the code against eavesdropping and false code injection attacks. --Abstract, page iv

THE PERSONALIZATION-PRIVACY PARADOX EXPLORED THROUGH A PRIVACY CALCULUS MODEL AND HOFSTEDE’S MODEL OF CULTURAL DIMENSIONS

The Personalization-Privacy Paradox is a relevant issue for companies today, as it deals with the paradox of customers who on the one hand want to keep their personal data private, but on the other hand desire the personalization benefits that can be gained by giving up that privacy. Many studies in the past have observed the Personalization-Privacy Paradox, but not thoroughly through the lens of a privacy calculus model. This paper uses a privacy calculus model to examine the Personalization-Privacy Paradox using Hofstede’s Six Dimensions of Culture and examines the United States, Germany, and China as case studies of three different cultures. These three cultures all have a great deal of influence in the world and are world opinion leaders but have vast differences in cultural values and beliefs. This paper shows the importance for marketers, designers, and implementers of personalization services to understand diverse cultures and how their varied idioms, beliefs, and values affect how they will perceive benefits and costs of personalization services in their internal privacy calculus. The marked differences in cultural scores and how those cultural beliefs affect the perceptions of personalization and privacy demonstrate that companies looking to expand their services and applications into new markets cannot rely on universal approaches

Cybersecurity: Past, Present and Future

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

Multimodal Biometric Systems for Personal Identification and Authentication using Machine and Deep Learning Classifiers

Multimodal biometrics, using machine and deep learning, has recently gained interest over single biometric modalities. This interest stems from the fact that this technique improves recognition and, thus, provides more security. In fact, by combining the abilities of single biometrics, the fusion of two or more biometric modalities creates a robust recognition system that is resistant to the flaws of individual modalities. However, the excellent recognition of multimodal systems depends on multiple factors, such as the fusion scheme, fusion technique, feature extraction techniques, and classification method. In machine learning, existing works generally use different algorithms for feature extraction of modalities, which makes the system more complex. On the other hand, deep learning, with its ability to extract features automatically, has made recognition more efficient and accurate. Studies deploying deep learning algorithms in multimodal biometric systems tried to find a good compromise between the false acceptance and the false rejection rates (FAR and FRR) to choose the threshold in the matching step. This manual choice is not optimal and depends on the expertise of the solution designer, hence the need to automatize this step. From this perspective, the second part of this thesis details an end-to-end CNN algorithm with an automatic matching mechanism. This thesis has conducted two studies on face and iris multimodal biometric recognition. The first study proposes a new feature extraction technique for biometric systems based on machine learning. The iris and facial features extraction is performed using the Discrete Wavelet Transform (DWT) combined with the Singular Value Decomposition (SVD). Merging the relevant characteristics of the two modalities is used to create a pattern for an individual in the dataset. The experimental results show the robustness of our proposed technique and the efficiency when using the same feature extraction technique for both modalities. The proposed method outperformed the state-of-the-art and gave an accuracy of 98.90%. The second study proposes a deep learning approach using DensNet121 and FaceNet for iris and faces multimodal recognition using feature-level fusion and a new automatic matching technique. The proposed automatic matching approach does not use the threshold to ensure a better compromise between performance and FAR and FRR errors. However, it uses a trained multilayer perceptron (MLP) model that allows people’s automatic classification into two classes: recognized and unrecognized. This platform ensures an accurate and fully automatic process of multimodal recognition. The results obtained by the DenseNet121-FaceNet model by adopting feature-level fusion and automatic matching are very satisfactory. The proposed deep learning models give 99.78% of accuracy, and 99.56% of precision, with 0.22% of FRR and without FAR errors. The proposed and developed platform solutions in this thesis were tested and vali- dated in two different case studies, the central pharmacy of Al-Asria Eye Clinic in Dubai and the Abu Dhabi Police General Headquarters (Police GHQ). The solution allows fast identification of the persons authorized to access the different rooms. It thus protects the pharmacy against any medication abuse and the red zone in the military zone against the unauthorized use of weapons

Ransomware: A New Era of Digital Terrorism

This work entails the study of ten nasty ransomwares to reveal out the analytical similarities and differences among them, which will help in understanding the mindset of cyber crooks crawling over the dark net. It also reviews the traps used by ransomware for its distribution and side by side examining the new possibilities of its dispersal. It conclude by divulging inter-relationship between various distribution approaches adopted by ransomwares and some attentive measures to hinder the ransomware and supporting alertness as ultimate tool of defense at user’s hand

Security-centric analysis and performance investigation of IEEE 802.16 WiMAX

fi=vertaisarvioitu|en=peerReviewed

Progress in the Challenge to Regulate Online Pharmacies

Imagine for a moment that after borrowing a credit card, a teenager strolls down the block to the local pharmacy. At the pharmacy, a doctor is at the door waiting and willing to prescribe anything to anyone. After answering a few questions, the teenager receives his prescription, where he takes it to the drug counter and places an order for a dangerous amount of painkillers. Imagine further that the teenager develops an addiction to the drugs and purchases an increased dosage each visit until finally, the teen dies from an overdose from the easily obtained prescription drugs. The situation described above is drawn from a real event. Nearly the very same chain of events happened to seventeen year old Ryan Haight. The only difference was that Ryan never even had to leave his home. Ryan visited an online pharmacy and obtained a prescription from a doctor he had never met for drugs he did not need. Using his father\u27s credit card, Ryan had the drugs delivered to his home. Tragically, Ryan became addicted to the drugs and eventually died of an overdose at age eighteen