Biblio
Electronic power grid is a distributed network used for transferring electricity and power from power plants to consumers. Based on sensor readings and control system signals, power grid states are measured and estimated. As a result, most conventional attacks, such as denial-of-service attacks and random attacks, could be found by using the Kalman filter. However, false data injection attacks are designed against state estimation models. Currently, distributed Kalman filtering is proved effective in sensor networks for detection and estimation problems. Since meters are distributed in smart power grids, distributed estimation models can be used. Thus in this paper, we propose a diffusion Kalman filter for the power grid to have a good performance in estimating models and to effectively detect false data injection attacks.
Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confoundin properties such as high-dimensionality, sparsity and power-law word distributions. In this paper, we propose a novel k-competitive autoencoder, called KATE, for text documents. Due to the competition between the neurons in the hidden layer, each neuron becomes specialized in recognizing specific data patterns, and overall the model can learn meaningful representations of textual data. A comprehensive set of experiments show that KATE can learn better representations than traditional autoencoders including denoising, contractive, variational, and k-sparse autoencoders. Our model also outperforms deep generative models, probabilistic topic models, and even word representation models (e.g., Word2Vec) in terms of several downstream tasks such as document classification, regression, and retrieval.
Additive Manufacturing (AM) uses Cyber-Physical Systems (CPS) (e.g., 3D Printers) that are vulnerable to kinetic cyber-attacks. Kinetic cyber-attacks cause physical damage to the system from the cyber domain. In AM, kinetic cyber-attacks are realized by introducing flaws in the design of the 3D objects. These flaws may eventually compromise the structural integrity of the printed objects. In CPS, researchers have designed various attack detection method to detect the attacks on the integrity of the system. However, in AM, attack detection method is in its infancy. Moreover, analog emissions (such as acoustics, electromagnetic emissions, etc.) from the side-channels of AM have not been fully considered as a parameter for attack detection. To aid the security research in AM, this paper presents a novel attack detection method that is able to detect zero-day kinetic cyber-attacks on AM by identifying anomalous analog emissions which arise as an outcome of the attack. This is achieved by statistically estimating functions that map the relation between the analog emissions and the corresponding cyber domain data (such as G-code) to model the behavior of the system. Our method has been tested to detect potential zero-day kinetic cyber-attacks in fused deposition modeling based AM. These attacks can physically manifest to change various parameters of the 3D object, such as speed, dimension, and movement axis. Accuracy, defined as the capability of our method to detect the range of variations introduced to these parameters as a result of kinetic cyber-attacks, is 77.45%.
Many Linked Open Data applications require fresh copies of RDF data at their local repositories. Since RDF documents constantly change and those changes are not automatically propagated to the LOD applications, it is important to regularly visit the RDF documents to refresh the local copies and keep them up-to-date. For this purpose, crawling strategies determine which RDF documents should be preferentially fetched. Traditional crawling strategies rely only on how an RDF document has been modified in the past. In contrast, we predict on the triple level whether a change will occur in the future. We use the weekly snapshots of the DyLDO dataset as well as the monthly snapshots of the Wikidata dataset. First, we conduct an in-depth analysis of the life span of triples in RDF documents. Through the analysis, we identify which triples are stable and which are ephemeral. We introduce different features based on the triples and apply a simple but effective linear regression model. Second, we propose a novel crawling strategy based on the linear regression model. We conduct two experimental setups where we vary the amount of available bandwidth as well as iteratively observe the quality of the local copies over time. The results demonstrate that the novel crawling strategy outperforms the state of the art in both setups.
OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.
We present a method for key compression in quantumresistant isogeny-based cryptosystems, which allows a reduction in and transmission costs of per-party public information by a factor of two, with no e ect on security. We achieve this reduction by associating a canonical choice of elliptic curve to each j-invariant, and representing elements on the curve as linear combinations with respect to a canonical choice of basis. This method of compressing public information can be applied to numerous isogeny-based protocols, such as key exchange, zero-knowledge identi cation, and public-key encryption. We performed personal computer and ARM implementations of the key exchange with compression and decompression in C and provided timing results, showing the computational cost of key compression and decompression at various security levels. Our results show that isogeny-based cryptosystems achieve by far the smallest possible key sizes among all existing families of post-quantum cryptosystems at practical security levels; e.g. 3073-bit public keys at the quantum 128-bit security level, comparable to (non-quantum) RSA key sizes.
We present a method for key compression in quantumresistant isogeny-based cryptosystems, which allows a reduction in and transmission costs of per-party public information by a factor of two, with no e ect on security. We achieve this reduction by associating a canonical choice of elliptic curve to each j-invariant, and representing elements on the curve as linear combinations with respect to a canonical choice of basis. This method of compressing public information can be applied to numerous isogeny-based protocols, such as key exchange, zero-knowledge identi cation, and public-key encryption. We performed personal computer and ARM implementations of the key exchange with compression and decompression in C and provided timing results, showing the computational cost of key compression and decompression at various security levels. Our results show that isogeny-based cryptosystems achieve by far the smallest possible key sizes among all existing families of post-quantum cryptosystems at practical security levels; e.g. 3073-bit public keys at the quantum 128-bit security level, comparable to (non-quantum) RSA key sizes.
We propose a secure key generation and distribution scheme for data encryption in classical optical fiber channel. A Delay interferometer (DI) is used to track the random phase fluctuation inside fiber, while the reconfigurable lengths of polarization-maintaining (PM) fiber are set as the source of optical phase fluctuations. The output signals from DI are extracted as the secret key and shared between the two-legal transmitter and receiver. Because of the randomness of local environment and the uniqueness of fiber channel, the phase fluctuation between orthogonal polarization modes (OPMs) can be used as secure keys to enhance the level of security in physical layer. Experimentally, we realize the random key generation and distribution over 25-km standard single-mode fiber (SSMF). Moreover, the proposed key generation scheme has the advantages of low cost, compatible with current optical fiber networks and long distance transmission with optical amplifiers.
Cloud computing paradigm is being used because of its low up-front cost. In recent years, even mobile phone users store their data at Cloud. Customer information stored at Cloud needs to be protected against potential intruders as well as cloud service provider. There is threat to the data in transit and data at cloud due to different possible attacks. Organizations are transferring important information to the Cloud that increases concern over security of data. Cryptography is common approach to protect the sensitive information in Cloud. Cryptography involves managing encryption and decryption keys. In this paper, we compare key management methods, apply key management methods to various cloud environments and analyze symmetric key cryptography algorithms.
Wireless sensor networks offer benefits in several applications but are vulnerable to various security threats, such as eavesdropping and hardware tampering. In order to reach secure communications among nodes, many approaches employ symmetric encryption. Several key management schemes have been proposed in order to establish symmetric keys. The paper presents an innovative key management scheme called random seed distribution with transitory master key, which adopts the random distribution of secret material and a transitory master key used to generate pairwise keys. The proposed approach addresses the main drawbacks of the previous approaches based on these techniques. Moreover, it overperforms the state-of-the-art protocols by providing always a high security level.
In Wireless sensor networks (WSNs), many tiny sensor nodes communicate using wireless links and collaborate with each other. The data collected by each of the nodes is communicated towards the gateway node after carrying out aggregation of the data by different nodes. It is necessary to secure the data collected by the WSN nodes while they communicate among themselves using multi hop wireless links. To meet this objective it is required to make use of energy efficient cryptographic algorithms so that the same can be ported over the resource constrained nodes. It is needed to create trust initially among the WSN nodes while using any of the cryptographic algorithms. Towards this, a key management technique needs to be made use of. Due to the resource constrained nature of the WSN nodes and the remote deployment of the nodes, an implementation of conventional key management techniques is infeasible. This work proposes a key management technique, with its reduced resource overheads, which is highly suited to be used in hierarchical WSN applications. Both Identity based key management (IBK) and probabilistic key pre-distribution schemes are made use of at different hierarchical levels. The proposed key management technique has been implemented using IRIS WSN nodes. A comparison of resource overheads has also been carried out.
A Mobile Ad hoc Network (MANET) is a spontaneous network consisting of wireless nodes which are mobile and self-configuring in nature. Devices in MANET can move freely in any direction independently and change its link frequently to other devices. MANET does not have centralized infrastructure and its characteristics makes this network vulnerable to various kinds of attacks. Data transfer is a major problem due to its nature of unreliable wireless medium. Commonly used technique for secure transmission in wireless network is cryptography. Use of cryptography key is often involved in most of cryptographic techniques. Key management is main component in security issues of MANET and various schemes have been proposed for it. In this paper, a study on various kinds of key management techniques in MANET is presented.
Personal cryptographic keys are the foundation of many secure services, but storing these keys securely is a challenge, especially if they are used from multiple devices. Storing keys in a centralized location, like an Internet-accessible server, raises serious security concerns (e.g. server compromise). Hardware-based Trusted Execution Environments (TEEs) are a well-known solution for protecting sensitive data in untrusted environments, and are now becoming available on commodity server platforms. Although the idea of protecting keys using a server-side TEE is straight-forward, in this paper we validate this approach and show that it enables new desirable functionality. We describe the design, implementation, and evaluation of a TEE-based Cloud Key Store (CKS), an online service for securely generating, storing, and using personal cryptographic keys. Using remote attestation, users receive strong assurance about the behaviour of the CKS, and can authenticate themselves using passwords while avoiding typical risks of password-based authentication like password theft or phishing. In addition, this design allows users to i) define policy-based access controls for keys; ii) delegate keys to other CKS users for a specified time and/or a limited number of uses; and iii) audit all key usages via a secure audit log. We have implemented a proof of concept CKS using Intel SGX and integrated this into GnuPG on Linux and OpenKeychain on Android. Our CKS implementation performs approximately 6,000 signature operations per second on a single desktop PC. The latency is in the same order of magnitude as using locally-stored keys, and 20x faster than smart cards.
The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.
User Authentication is a difficult problem yet to be addressed accurately. Little or no work is reported in literature dealing with clustering-based anomaly detection techniques for user authentication for keystroke data. Therefore, in this paper, Modified Differential Evolution (MDE) based subspace anomaly detection technique is proposed for user authentication in the context of behavioral biometrics using keystroke dynamics features. Thus, user authentication is posed as an anomaly detection problem. Anomalies in CMU's keystroke dynamics dataset are identified using subspace-based and distance-based techniques. It is observed that, among the proposed techniques, MDE based subspace anomaly detection technique yielded the highest Area Under ROC Curve (AUC) for user authentication problem. We also performed a Wilcoxon Signed Rank statistical test to corroborate our results statistically.
It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.
In the modern day and age, credential based authentication systems no longer provide the level of security that many organisations and their services require. The level of trust in passwords has plummeted in recent years, with waves of cyber attacks predicated on compromised and stolen credentials. This method of authentication is also heavily reliant on the individual user's choice of password. There is the potential to build levels of security on top of credential based authentication systems, using a risk based approach, which preserves the seamless authentication experience for the end user. One method of adding this security to a risk based authentication framework, is keystroke dynamics. Monitoring the behaviour of the users and how they type, produces a type of digital signature which is unique to that individual. Learning this behaviour allows dynamic flags to be applied to anomalous typing patterns that are produced by attackers using stolen credentials, as a potential risk of fraud. Methods from statistics and machine learning have been explored to try and implement such solutions. This paper will look at an Autoencoder model for learning the keystroke dynamics of specific users. The results from this paper show an improvement over the traditional tried and tested statistical approaches with an Equal Error Rate of 6.51%, with the additional benefits of relatively low training times and less reliance on feature engineering.
Customer Edge Switching (CES) is an experimental Internet architecture that provides reliable and resilient multi-domain communications. It provides resilience against security threats because domains negotiate inbound and outbound policies before admitting new traffic. As CES and its signalling protocols are being prototyped, there is a need for independent testing of the CES architecture. Hence, our research goal is to develop an automated test framework that CES protocol designers and early adopters can use to improve the architecture. The test framework includes security, functional, and performance tests. Using the Robot Framework and STRIDE analysis, in this paper we present this automated security test framework. By evaluating sample test scenarios, we show that the Robot Framework and our CES test suite have provided productive discussions about this new architecture, in addition to serving as clear, easy-to-read documentation. Our research also confirms that test automation can be useful to improve new protocol architectures and validate their implementation.
An identity authentication scheme is proposed combining with biometric encryption, public key cryptography of homomorphism and predicate encryption technology under the cloud computing environment. Identity authentication scheme is proposed based on the voice and homomorphism technology. The scheme is divided into four stages, register and training template stage, voice login and authentication stage, authorization stage, and audit stage. The results prove the scheme has certain advantages in four aspects.
Kings Eye is a platform independent situational awareness prototype for smart devices. Platform independence is important as there are more and more soldiers bringing their own devices, with different operating systems, into the field. The concept of Bring Your Own Device (BYOD) is a low-cost approach to equipping soldiers with situational awareness tools and by this it is important to facilitate and evaluate such solutions.
Today's cellular core, which connects the radio access network to the Internet, relies on fixed hardware appliances placed at a few dedicated locations and uses relatively static routing policies. As such, today's core design has key limitations—it induces inefficient provisioning tradeoffs and is poorly equipped to handle overload, failure scenarios, and diverse application requirements. To address these limitations, ongoing efforts envision "clean slate" solutions that depart from cellular standards and routing protocols; e.g., via programmable switches at base stations and per-flow SDN-like orchestration. The driving question of this work is to ask if a clean-slate redesign is necessary and if not, how can we design a flexible cellular core that is minimally disruptive. We propose KLEIN, a design that stays within the confines of current cellular standards and addresses the above limitations by combining network functions virtualization with smart resource management. We address key challenges w.r.t. scalability and responsiveness in realizing KLEIN via backwards-compatible orchestration mechanisms. Our evaluations through data-driven simulations and real prototype experiments using OpenAirInterface show that KLEIN can scale to billions of devices and is close to optimal for wide variety of traffic and deployment parameters.
Nowadays, the rapid development of the Internet of Things facilitates human life and work, while it also brings great security risks to the society due to the frequent occurrence of various security issues. IoT device has the characteristics of large-scale deployment and single responsibility application, which makes it easy to cause a chain reaction and results in widespread privacy leakage and system security problems when the software vulnerability is identified. It is difficult to guarantee that there is no security hole in the IoT operating system which is usually designed for MCU and has no kernel mode. An alternative solution is to identify the security issues in the first time when the system is hijacked and suspend the suspicious task before it causes irreparable damage. This paper proposes KLRA (A Kernel Level Resource Auditing Tool) for IoT Operating System Security This tool collects the resource-sensitive events in the kernel and audit the the resource consumption pattern of the system at the same time. KLRA can take fine-grained events measure with low cost and report the relevant security warning in the first time when the behavior of the system is abnormal compared with daily operations for the real responsibility of this device. KLRA enables the IoT operating system for MCU to generate the security early warning and thereby provides a self-adaptive heuristic security mechanism for the entire IoT system.
Nowadays, a major challenge to network security is malicious codes. However, manual extraction of features is one of the characteristics of traditional detection techniques, which is inefficient. On the other hand, the features of the content and behavior of the malicious codes are easy to change, resulting in more inefficiency of the traditional techniques. In this paper, a K-Means Clustering Analysis is proposed based on Adaptive Weights (AW-MMKM). Identifying malicious codes in the proposed method is based on four types of network behavior that can be extracted from network traffic, including active, fault, network scanning, and page behaviors. The experimental results indicate that the AW-MMKM can detect malicious codes efficiently with higher accuracy.