Biblio
The rapid proliferation of biometrics has led to growing concerns about the security and privacy of the biometric data (template). A biometric uniquely identifies an individual and unlike passwords, it cannot be revoked or replaced since it is unique and fixed for every individual. To address this problem, many biometric template protection methods using fully homomorphic encryption have been proposed. But, most of them (i) are computationally expensive and practically infeasible (ii) do not support operations over real valued biometric feature vectors without quantization (iii) do not support packing of real valued feature vectors into a ciphertext (iv) require multi-shot enrollment of users for improved matching performance. To address these limitations, we propose a secure and privacy preserving method for biometric template protection using fully homomorphic encryption. The proposed method is computationally efficient and practically feasible, supports operations over real valued feature vectors without quantization and supports packing of real valued feature vectors into a single ciphertext. In addition, the proposed method enrolls the users using one-shot enrollment. To evaluate the proposed method, we use three face datasets namely LFW, FEI and Georgia tech face dataset. The encrypted face template (for 128 dimensional feature vector) requires 32.8 KB of memory space and it takes 2.83 milliseconds to match a pair of encrypted templates. The proposed method improves the matching performance by 3 % when compared to state-of-the-art, while providing high template security.
The use of biometrics in security applications may be vulnerable to several challenges of hacking. Thus, the emergence of cancellable biometrics becomes a suitable solution to this problem. This paper presents a one-way cancellable biometric transform that depends on 3D chaotic maps for face and fingerprint encryption. It aims to avoid cloning of original biometrics and allow the templates used by each user in different applications to be variable. The permutations achieved with the chaotic maps guarantee high security of the biometric templates, especially with the 3D implementation of the encryption algorithm. In addition, the paper presents a hardware implementation for this framework. The proposed algorithm also achieves good performance in the presence of low and moderate levels of noise. An experimental version of the proposed cancellable biometric system has been applied on FPGA model. The obtained results achieve a powerful performance of the proposed cancellable biometric system.
Biometric authentication is the preferred authentication scheme in modern computing systems. While it offers enhanced usability, it also requires cautious handling of sensitive users' biometric templates. In this paper, a distributed scheme that eliminates the requirement for a central node that holds users' biometric templates is presented. This is replaced by an Ethereum/IPFS combination to which the templates of the users are stored in a homomorphically encrypted form. The scheme enables the biometric authentication of the users by any third party service, while the actual biometric templates of the user never leave his device in non encrypted form. Secure authentication of users in enabled, while sensitive biometric data are not exposed to anyone. Experiments show that the scheme can be applied as an authentication mechanism with minimal time overhead.
Zero Trust Model ensures each node is responsible for the approval of the transaction before it gets committed. The data owners can track their data while it’s shared amongst the various data custodians ensuring data security. The consensus algorithm enables the users to trust the network as malicious nodes fail to get approval from all nodes, thereby causing the transaction to be aborted. The use case chosen to demonstrate the proposed consensus algorithm is the college placement system. The algorithm has been extended to implement a diversified, decentralized, automated placement system, wherein the data owner i.e. the student, maintains an immutable certificate vault and the student’s data has been validated by a verifier network i.e. the academic department and placement department. The data transfer from student to companies is recorded as transactions in the distributed ledger or blockchain allowing the data to be tracked by the student.
Phishing attacks are the most common form of attacks that can happen over the internet. This method involves attackers attempting to collect data of a user without his/her consent through emails, URLs, and any other link that leads to a deceptive page where a user is persuaded to commit specific actions that can lead to the successful completion of an attack. These attacks can allow an attacker to collect vital information of the user that can often allow the attacker to impersonate the victim and get things done that only the victim should have been able to do, such as carry out transactions, or message someone else, or simply accessing the victim's data. Many studies have been carried out to discuss possible approaches to prevent such attacks. This research work includes three machine learning algorithms to predict any websites' phishing status. In the experimentation these models are trained using URL based features and attempted to prevent Zero-Day attacks by using proposed software proposal that differentiates the legitimate websites and phishing websites by analyzing the website's URL. From observations, the random forest classifier performed with a precision of 97%, a recall 99%, and F1 Score is 97%. Proposed model is fast and efficient as it only works based on the URL and it does not use other resources for analysis, as was the case for past studies.
The detection of malicious HTTP(S) requests is a pressing concern in cyber security, in particular given the proliferation of HTTP-based (micro-)service architectures. In addition to rule-based systems for known attacks, anomaly detection has been shown to be a promising approach for unknown (zero-day) attacks. This article extends existing work by integrating outlier explanations for individual requests into an end-to-end pipeline. These end-to-end explanations reflect the internal working of the pipeline. Empirically, we show that found explanations coincide with manually labelled explanations for identified outliers, allowing security professionals to quickly identify and understand malicious requests.
Zero-day Web attacks are arguably the most serious threats to Web security, but are very challenging to detect because they are not seen or known previously and thus cannot be detected by widely-deployed signature-based Web Application Firewalls (WAFs). This paper proposes ZeroWall, an unsupervised approach, which works with an existing WAF in pipeline, to effectively detecting zero-day Web attacks. Using historical Web requests allowed by an existing signature-based WAF, a vast majority of which are assumed to be benign, ZeroWall trains a self-translation machine using an encoder-decoder recurrent neural network to capture the syntax and semantic patterns of benign requests. In real-time detection, a zero-day attack request (which the WAF fails to detect), not understood well by self-translation machine, cannot be translated back to its original request by the machine, thus is declared as an attack. In our evaluation using 8 real-world traces of 1.4 billion Web requests, ZeroWall successfully detects real zero-day attacks missed by existing WAFs and achieves high F1-scores over 0.98, which significantly outperforms all baseline approaches.
Microarchitectural Side-Channel Attacks (SCAs) have emerged recently to compromise the security of computer systems by exploiting the existing processors' hardware vulnerabilities. In order to detect such attacks, prior studies have proposed the deployment of low-level features captured from built-in Hardware Performance Counter (HPC) registers in modern microprocessors to implement accurate Machine Learning (ML)-based SCAs detectors. Though effective, such attack detection techniques have mainly focused on binary classification models offering limited insights on identifying the type of attacks. In addition, while existing SCAs detectors required prior knowledge of attacks applications to detect the pattern of side-channel attacks using a variety of microarchitectural features, detecting unknown (zero-day) SCAs at run-time using the available HPCs remains a major challenge. In response, in this work we first identify the most important HPC features for SCA detection using an effective feature reduction method. Next, we propose Phased-Guard, a two-level machine learning-based framework to accurately detect and classify both known and unknown attacks at run-time using the most prominent low-level features. In the first level (SCA Detection), Phased-Guard using a binary classification model detects the existence of SCAs on the target system by determining the critical scenarios including system under attack and system under no attack. In the second level (SCA Identification) to further enhance the security against side-channel attacks, Phased-Guard deploys a multiclass classification model to identify the type of SCA applications. The experimental results indicate that Phased-Guard by monitoring only the victim applications' microarchitectural HPCs data, achieves up to 98 % attack detection accuracy and 99.5% SCA identification accuracy significantly outperforming the state-of-the-art solutions by up to 82 % in zero-day attack detection at the cost of only 4% performance overhead for monitoring.
This paper argues about a new conceptual modeling language for the White-Box (WB) security analysis. In the WB security domain, an attacker may have access to the inner structure of an application or even the entire binary code. It becomes pretty easy for attackers to inspect, reverse engineer, and tamper the application with the information they steal. The basis of this paper is the 14 patterns developed by a leading provider of software protection technologies and solutions. We provide a part of a new modeling language named i-WBS (White-Box Security) to describe problems of WB security better. The essence of White-Box security problem is code security. We made the new modeling language focus on code more than ever before. In this way, developers who are not security experts can easily understand what they need to really protect.
To ensure quality of service and user experience, large Internet companies often monitor various Key Performance Indicators (KPIs) of their systems so that they can detect anomalies and identify failure in real time. However, due to a large number of various KPIs and the lack of high-quality labels, existing KPI anomaly detection approaches either perform well only on certain types of KPIs or consume excessive resources. Therefore, to realize generic and practical KPI anomaly detection in the real world, we propose a KPI anomaly detection framework named iRRCF-Active, which contains an unsupervised and white-box anomaly detector based on Robust Random Cut Forest (RRCF), and an active learning component. Specifically, we novelly propose an improved RRCF (iRRCF) algorithm to overcome the drawbacks of applying original RRCF in KPI anomaly detection. Besides, we also incorporate the idea of active learning to make our model benefit from high-quality labels given by experienced operators. We conduct extensive experiments on a large-scale public dataset and a private dataset collected from a large commercial bank. The experimental resulta demonstrate that iRRCF-Active performs better than existing traditional statistical methods, unsupervised learning methods and supervised learning methods. Besides, each component in iRRCF-Active has also been demonstrated to be effective and indispensable.
We leverage deep learning algorithms on various user behavioral information gathered from end-user devices to classify a subject of interest. In spite of the ability of these techniques to counter spoofing threats, they are vulnerable to adversarial learning attacks, where an attacker adds adversarial noise to the input samples to fool the classifier into false acceptance. Recently, a handful of mature techniques like Fast Gradient Sign Method (FGSM) have been proposed to aid white-box attacks, where an attacker has a complete knowledge of the machine learning model. On the contrary, we exploit a black-box attack to a behavioral biometric system based on gait patterns, by using FGSM and training a shadow model that mimics the target system. The attacker has limited knowledge on the target model and no knowledge of the real user being authenticated, but induces a false acceptance in authentication. Our goal is to understand the feasibility of a black-box attack and to what extent FGSM on shadow models would contribute to its success. Our results manifest that the performance of FGSM highly depends on the quality of the shadow model, which is in turn impacted by key factors including the number of queries allowed by the target system in order to train the shadow model. Our experimentation results have revealed strong relationships between the shadow model and FGSM performance, as well as the effect of the number of FGSM iterations used to create an attack instance. These insights also shed light on deep-learning algorithms' model shareability that can be exploited to launch a successful attack.
Wireless Sensor Network (WSN) is a heterogeneous type of network consisting of scattered sensor nodes and working together for data collection, processing, and transmission functions[1], [2]. Because WSN is widely used in vital matters, aspects of its security must also be considered. There are many types of attacks that might be carried out to disrupt WSN networks. The methods of attack that exist in WSN include jamming attack, tampering, Sybil attack, wormhole attack, hello flood attack, and, blackhole attack[3]. Blackhole attacks are one of the most dangerous attacks on WSN networks. Enhanced Check Agent method is designed to detect black hole attacks by sending a checking agent to record nodes that are considered black okay. The implementation will be tested right on a wireless sensor network using ZigBee technology. Network topology uses a mesh where each node can have more than one routing table[4]. The Enhanced Check Agent method can increase throughput to 100 percent.
With the advent of networking technologies and increasing network attacks, Intrusion Detection systems are apparently needed to stop attacks and malicious activities. Various frameworks and techniques have been developed to solve the problem of intrusion detection, still there is need for new frameworks as per the challenging scenario of enormous scale in data size and nature of attacks. Current IDS systems pose challenges on the throughput to work with high speed networks. In this paper we address the issue of high computational overhead of anomaly based IDS and propose the solution using discretization as a data preprocessing step which can drastically reduce the computation overhead. We propose method to provide near real time detection of attacks using only basic flow level features that can easily be extracted from network packets.