Biblio

Information Security is a unified piece of information technology that has emerged as vibrant technology in the last two decades. To manage security, authentication assumes a significant part. Biometric is the physical unique identification as well as authentication for the third party. We have proposed the security model for preventing many attacks so we are used the innermost layer as a 3DES (Triple Encryption standard) cryptography algorithm that is providing 3- key protection as 64-bit and the outermost layer used the MD5 (Message Digest) algorithm. i. e. providing 128-bit protection as well as we is using fingerprint identification as physical security that is used in third-party remote integrity auditing. Remote data integrity auditing is proposed to ensure the uprightness of the information put away in the cloud. Data Storage of cloud services has expanded paces of acknowledgment because of their adaptability and the worry of the security and privacy levels. The large number of integrity and security issues that arise depends on the difference between the customer and the service provider in the sense of an external auditor. The remote data integrity auditing is at this point prepared to be viably executed. In the meantime, the proposed scheme is depending on identity-based cryptography, which works on the convoluted testament of the executives. The safety investigation and the exhibition assessment show that the planned property is safe and productive.

In a Leading field of Information Technology moreover make information Security a unified piece of it. To manage security, Authentication assumes a significant part. Biometric is the physical unique identification as well as Authentication for third party. We are proposed the Security model for preventing many attacks so we are used Inner most layer as a 3DES (Triple Encryption standard) Cryptography algorithm that is providing 3-key protection as 64-bit And the outer most layer used the MD5 (Message Digest) Algorithm. i. e. Providing 128 – bit protection. As well as we are using Fingerprint Identification as a physical Security that used in third party remote integrity auditing, and remote data integrity auditing is proposed to ensure the uprightness of the information put away in the cloud. Data Storage of cloud services has expanded paces of acknowledgment because of their adaptability and the worry of the security and privacy levels. The large number of integrity and security issues that arise depends on the difference between the customer and the service provider in the sense of an external auditor. The remote data integrity auditing is at this point prepared to be viably executed. In the meantime, the proposed scheme is depends on identity-based cryptography, which works on the convoluted testament the executives. The safety investigation and the exhibition assessment show that the planned property is safe and productive.

Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources.

Cloud computing facilitates the access of applications and data from any location by using any device with an internet connection. It enables multiple applications and users to access the same data resources. Cloud based information sharing is a technique that allows researchers to communicate and collaborate, that leads to major new developments in the field. It also enables users to access data over the cloud easily and conveniently. Privacy, authenticity and confidentiality are the three main challenges while sharing data in cloud. There are many methods which support secure data sharing in cloud environment such as Attribute Based Encryption(ABE), Role Based Encryption, Hierarchical Based Encryption, and Identity Based Encryption. ABE provides secure access control mechanisms for integrity. It is classified as Key Policy Attribute Based Encryption(KP-ABE) and Ciphertext Policy Attribute Based Encryption(CP-ABE) based on access policy integration. In KPABE, access structure is incorporated with user's private key, and data are encrypted over a defined attributes. Moreover, in CPABE, access structure is embedded with ciphertext. This paper reviews CP-ABE methods that have been developed so far for achieving secured data sharing in cloud environment.

This paper contains analysis of methods of increasing the information protection from unauthorized access using a multifactor authentication algorithm; figuring out the best, most efficient and secure method of scanning biometric data; development of a method to store and compare a candidate’s and existisng system user’s information in steganographic space. The urgency of the work is confirmed by the need to increase information security of special infocommunication systems with the help of biometric information and protection of this information from intruders by means of steganographic transformation.

The traditional Proof of Existence blockchain service on the Bitcoin network can be used to verify the existence of any research data at a specific point of time, and to validate the data integrity, without revealing its content. Several variants of the blockchain service exist to certify the existence of data relying on cryptographic fingerprinting, thus enabling an efficient verification of the authenticity of such certifications. However, nowadays research data is continuously changing and being modified through different processing steps in most scientific research workflows such that certifications of individual data objects seem to be constantly outdated in this setting. This paper describes how the blockchain and distributed ledger technology can be used to form a new certification model, that captures the research process as a whole in a more meaningful way, including the description of the used data through its different stages and the associated computational pipeline, code for analysis and the experimental design. The scientific blockchain infrastructure bloxberg, together with a deep learning based analysis from the behavioral science field are used to show the applicability of the approach.

In this work a data provenance system for grid-oriented applications is presented. The proposed Keyless Infrastructure Security Solution (KISS) provides mechanisms to store and maintain digital data fingerprints that can later be used to validate and assert data provenance using a time-based, hash tree mechanism. The developed solution has been designed to satisfy the stringent requirements of the modern power grid including execution time and storage necessities. Its applicability has been tested using a lab-scale, proof-of-concept deployment that secures an energy management system against the attack sequence observed on the 2016 Ukrainian power grid cyberattack. The results demonstrate a strong potential for enabling data provenance in a wide array of applications, including speed-sensitive applications such as those found in control room environments.

As the web keeps increasing in size, the number of vulnerable and poorly-managed websites increases commensurately. Attackers rely on armies of malicious bots to discover these vulnerable websites, compromising their servers, and exfiltrating sensitive user data. It is, therefore, crucial for the security of the web to understand the population and behavior of malicious bots.In this paper, we report on the design, implementation, and results of Aristaeus, a system for deploying large numbers of "honeysites", i.e., websites that exist for the sole purpose of attracting and recording bot traffic. Through a seven-month-long experiment with 100 dedicated honeysites, Aristaeus recorded 26.4 million requests sent by more than 287K unique IP addresses, with 76,396 of them belonging to clearly malicious bots. By analyzing the type of requests and payloads that these bots send, we discover that the average honeysite received more than 37K requests each month, with more than 50% of these requests attempting to brute-force credentials, fingerprint the deployed web applications, and exploit large numbers of different vulnerabilities. By comparing the declared identity of these bots with their TLS handshakes and HTTP headers, we uncover that more than 86.2% of bots are claiming to be Mozilla Firefox and Google Chrome, yet are built on simple HTTP libraries and command-line tools.

Web pages aggressively track users for a variety of purposes from targeted advertisements to enhanced authentication. As browsers move to restrict traditional cookie-based tracking, web pages increasingly move to tracking based on browser fingerprinting. Unfortunately, the state-of-the-art to detect fingerprinting in browsers is often error-prone, resorting to imprecise heuristics and crowd-sourced filter lists. This paper presents EssentialFP, a principled approach to detecting fingerprinting on the web. We argue that the pattern of (i) gathering information from a wide browser API surface (multiple browser-specific sources) and (ii) communicating the information to the network (network sink) captures the essence of fingerprinting. This pattern enables us to clearly distinguish fingerprinting from similar types of scripts like analytics and polyfills. We demonstrate that information flow tracking is an excellent fit for exposing this pattern. To implement EssentialFP we leverage, extend, and deploy JSFlow, a state-of-the-art information flow tracker for JavaScript, in a browser. We illustrate the effectiveness of EssentialFP to spot fingerprinting on the web by evaluating it on two categories of web pages: one where the web pages perform analytics, use polyfills, and show ads, and one where the web pages perform authentication, bot detection, and fingerprinting-enhanced Alexa top pages.

Photo Response Non-Uniformity (PRNU) noise obtained from images or videos is used as a camera fingerprint to attribute visual objects captured by a camera. The PRNU-based source attribution method, however, fails when there is misalignment between the fingerprint and the query object. One example of such a misalignment, which has been overlooked in the field, is caused by the in-camera resizing technique that a video may have been subjected to. This paper investigates the attribution of visual media in the context of matching a video query object to an image fingerprint or vice versa. Specifically this paper focuses on improving camera attribution performance by taking into account the effects of binning, a commonly used in-camera resizing technique applied to video. We experimentally show that the True Positive Rate (TPR) obtained when binning is considered is approximately 3% higher.

With the rapid development of the Internet, cyberspace security has become a potentially huge problem. At the same time, the disclosure of cyberspace vulnerabilities is getting faster and faster. Traditional protection methods based on known features cannot effectively defend against new network attacks. Network attack is no more a single vulnerability exploit, but an APT attack based on multiple complicated methods. Cyberspace attacks have become ``rationalized'' on the surface. Currently, there are a lot of researches about visualization of attack paths, but there is no an overall plan to reproduce the attack path. Most researches focus on the detection and characterization individual based on single behavior cyberspace attacks, which loose it's abilities to help security personnel understand the complete attack behavior of attackers. The key factors of this paper is to collect the attackers' aggressive behavior by reverse retrospective method based on the actual shooting range environment. By finding attack nodes and dividing offensive behavior into time series, we can characterize the attacker's behavior path vividly and comprehensively.

Advanced Persistent Threats (APT) consist of most skillful hackers who employ sophisticated techniques to stealthily gain unauthorized access to private networks and exfiltrate sensitive data. When their existence is discovered, organizations - if they can sustain business continuity - mostly have to perform forensics activities to assess the damage of the attack and discover the extent of sensitive data leakage. In this paper, we construct a novel framework to pinpoint sensitive data that may have been leaked in such an attack. Our framework consists of creating baseline fingerprints for each workstation for setting normal activity, and we consider the change in the behavior of the network overall. We compare the accused fingerprint with sensitive database information by utilizing both Levenstein distance and TF-IDF/cosine similarity resulting in a similarity percentage. This allows us to pinpoint what part of data was exfiltrated by the perpetrators, where in the network the data originated, and if that data is sensitive to the private company's network. We then perform feasibility experiments to show that even these simple methods are feasible to run on a network representative of a mid-size business.

In recent years, biometric authentication, such as fingerprint and face recognition, has become widespread in smartphones. However, fingerprint and face authentication have the problem that they cannot be used depending on the condition of the user's fingers or face. Therefore, we have been investigating a new biometric authentication system using pinna as a personal authentication system for smart phones. We have studied a personal authentication system using the Pinna Related Transfer Function (PRTF), which is an acoustic transfer function measured from the pinna. However, since the position of the smartphone changes every time it is placed on the ear, there is a problem that the authentication rate decreases. In this paper, we propose a multimodal personal authentication system using PRTF, pinna images, and smartphone location information, and verify its effectiveness. The results show that the proposed authentication system can improve the robustness against the fluctuation of the smartphone location.

Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas. These replicas can simulate the degrading effects on original audio signals by applying small time offsets and various types of distortions, such as background noise and room/microphone impulse responses. In the segment-level search task, where the conventional audio fingerprinting systems used to fail, our system using 10x smaller storage has shown promising results. Our code and dataset are available at https://mimbres.github.io/neural-audio-fp/.

In recent years, one of the important and interesting tasks has become the protection of civilian and military objects from unmanned aerial vehicles (UAVs) carrying a potential threat. To solve this problem, it is required to detect UAVs and activate protective systems. UAVs can be represented as aerodynamic objects of the monoplane or multicopter type with acoustic fingerprints. In this paper we consider algorithm for UAV acoustic detection and recognition system. Preliminary results of analysis of experimental data show effectiveness of proposed approach.

Dynamic Information Flow Tracking (DIFT), also called Dynamic Taint Analysis (DTA), is a technique for tracking the information as it flows through a program's execution. Specifically, some inputs or data get tainted and then these taint marks (tags) propagate usually at the instruction-level. While DIFT has been a fundamental concept in computer and network security for the past decade, it still faces open challenges that impede its widespread application in practice; one of them being the indirect flow propagation dilemma: should the tags involved in an indirect flow, e.g., in a control or address dependency, be propagated? Propagating all these tags, as is done for direct flows, leads to overtainting (all taintable objects become tainted), while not propagating them leads to undertainting (information flow becomes incomplete). In this paper, we analytically model that decisioning problem for indirect flows, by considering various tradeoffs including undertainting versus overtainting, importance of heterogeneous code semantics and context. Towards tackling this problem, we design MITOS, a distributed-optimization algorithm, that: decides about the propagation of indirect flows by properly weighting all these tradeoffs, is of low-complexity, is scalable, is able to flexibly adapt to different application scenarios and security needs of large distributed systems. Additionally, MITOS is applicable to most DIFT systems that consider an arbitrary number of tag types, and introduces the key properties of fairness and tag-balancing to the DIFT field. To demonstrate MITOS's applicability in practice, we implement and evaluate MITOS on top of an open-source DIFT, and we shed light on the open problem. We also perform a case-study scenario with a real in-memory only attack and show that MITOS improves simultaneously (i) system's spatiotemporal overhead (up to 40%), and (ii) system's fingerprint on suspected bytes (up to 167%) compared to traditional DIFT, even though these metrics usually conflict.

Data security is becoming more and more important in data exchange. Once the data is leaked, it will pose a great threat to the privacy and property security of users. Copyright authentication and data provenance have become an important requirement of the information security defense mechanism. In order to solve the collusion leakage of the data distributed by organization and the low efficiency of tracking the leak provenance after the data is destroyed, this paper proposes a concatenated-group digital fingerprint coding based on CFF code and Reed-solomon (RS) that can resist collusion attacks and corresponding detection algorithm. The experiments based on an asymmetric anti-collusion fingerprint protocol show that the proposed method has better performance to resist collusion attacks than similar non-grouped fingerprint coding and effectively reduces the percentage of misjudgment, which verifies the availability of the algorithm and enriches the means of organization data security audit.

Signature-based malware detection is not always effective at detecting polymorphic variants of known malware. Malware signatures are devised to counter known threats, which also limits efficacy against new forms of malware. However, existing signatures do present the ability to classify malware based upon known malicious behavior which occurs on a victim computer. In this paper we present a method of classifying malware by family type through behavioral analysis, where the frequency of system function calls is used to fingerprint the actions of specific malware families. This in turn allows us to demonstrate a machine learning classifier which is capable of distinguishing malware by family affiliation with high accuracy.

This paper is focused on the topic of the use of biometrics framework and strategy for secure access identity management of cloud computing services. This paper present's a description of cloud computing security issues and explored a review of previous works that represented various ideas for a cloud access framework. This paper discusses threats like a malicious insider, data breaches, and describes ways to protect them. It describes an innovative way portrayed a framework that fingerprint access-based authentication to protect Cloud services from unauthorized access and DOS, DDoS attacks. This biometrics-based framework as an extra layer of protection, added then it can be robust to prevent unauthorized access to cloud services.

The training process of deep learning model is costly. As such, deep learning model can be treated as an intellectual property (IP) of the model creator. However, a pirate can illegally copy, redistribute or abuse the model without permission. In recent years, a few Deep Neural Networks (DNN) IP protection works have been proposed. However, most of existing works passively verify the copyright of the model after the piracy occurs, and lack of user identity management, thus cannot provide commercial copyright management functions. In this paper, a novel user fingerprint management and DNN authorization control technique based on backdoor is proposed to provide active DNN IP protection. The proposed method can not only verify the ownership of the model, but can also authenticate and manage the user's unique identity, so as to provide a commercially applicable DNN IP management mechanism. Experimental results on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets show that the proposed method can achieve high detection rate for user authentication (up to 100% in the three datasets). Illegal users with forged fingerprints cannot pass authentication as the detection rates are all 0 % in the three datasets. Model owner can verify his ownership since he can trigger the backdoor with a high confidence. In addition, the accuracy drops are only 0.52%, 1.61 % and -0.65% on CIFAR-10, CIFAR-100 and Fashion-MNIST, respectively, which indicate that the proposed method will not affect the performance of the DNN models. The proposed method is also robust to model fine-tuning and pruning attacks. The detection rates for owner verification on CIFAR-10, CIFAR-100 and Fashion-MNIST are all 100% after model pruning attack, and are 90 %, 83 % and 93 % respectively after model fine-tuning attack, on the premise that the attacker wants to preserve the accuracy of the model.

The time-varying properties of the wireless channel are a powerful source of information that can complement and enhance traditional security mechanisms. Therefore, we propose a cross-layer authentication mechanism that combines physical layer channel information and traditional authentication mechanism in LTE. To verify the feasibility of the proposed mechanism, we build a cross-layer authentication system that extracts the phase shift information of a typical UE and use the ensemble learning method to train the fingerprint map based on OAI LTE. Experimental results show that our cross-layer authentication mechanism can effectively prompt the security of LTE system.

Browsers collects information for better user experience by allowing JavaScript's and other extensions. Advertiser and other trackers take advantage on this useful information to tracked users across the web from remote devices on the purpose of individual unique identifications the so-called browser fingerprinting. Our work explores the diversity and stability of browser fingerprint by modifying the rule-based algorithm. Browser fingerprint rely only from the gathered data through browser, it is hard to tell that this piece of information still the same when upgrades and or downgrades are happening to any browsers and software's without user consent, which is stability and diversity are the most important usage of generating browser fingerprint. We implemented device fingerprint to identify consenting visitors in our website and evaluate individual devices attributes by calculating entropy of each selected attributes. In this research, it is noted that we emphasize only on data collected through a web browser by employing twenty (20) attributes to identify promising high value information to track how device information evolve and consistent in a period of time, likewise, we manually selected device information for evaluation where we apply the modified rules. Finally, this research is conducted and focused on the devices having the closest configuration and device information to test how devices differ from each other after several days of using on the basis of individual user configurations, this will prove in our study that every device is unique.

In order to design an end-to-end data security preservation mechanism, this paper first proposes a grid terminal data security management model based on master-slave Blockchain, including grid terminal, slave Blockchain, and main Blockchain. Among them, the grid terminal mainly completes data generation and data release, the receiving of data and the distributed signature of data are mainly completed from the slave Blockchain, and the main Blockchain mainly completes the intelligent storage of data. Secondly, the data security management mechanism of grid terminal based on master-slave Blockchain is designed, including data distribution process design, data receiving process design, data distributed signature design and data intelligent storage process design. Finally, taking the identity registration and data storage process of the grid terminal as an example, the workflow of the data security management mechanism of the grid terminal based on the master-slave Blockchain is described in detail.

With the emergence of advanced technology, the user authentication methods have also been improved. Authenticating the user, several secure and efficient approaches have been introduced, but the biometric authentication method is considered much safer as compared to password-driven methods. In this paper, we explore the risks, concerns, and methods by installing well-known open-source software used in Unibiometric analysis by the partners of The National Institute of Standards and Technology (NIST). Not only are the algorithms used all open source but it comes with test data and several internal open source utilities necessary to process biometric data.

Individualization of anonymous identities using artificial intelligence - enables innovative human-computer interaction through the personalization of communication which is, at the same time, individual and anonymous. This paper presents possible approach for individualization of anonymous identities in real time. It uses computer vision and artificial intelligence to automatically detect and recognize person's age group, gender, human body measures, proportions and other specific personal characteristics. Collected data constitutes the so-called person's biometric footprint and are linked to a unique (but still anonymous) identity that is recorded in the computer system, along with other information that make up the profile of the person. Identity anonymization can be achieved by appropriate asymmetric encryption of the biometric footprint (with no additional personal information being stored) and integrity can be ensured using blockchain technology. Data collected in this manner is GDPR compliant.