Biblio

This research-to-practice work-in-progress addresses a new approach to cybersecurity education. The cyber security skills shortage is reaching prevalent proportions. The consensus in the STEM community is that the problem begins at k-12 schools with too few students interested in STEM subjects. One way to ensure a larger pipeline in cybersecurity is to train more high school teachers to not only teach cybersecurity in their schools or integrate cybersecurity concepts in their classrooms but also to promote IT security as an attractive career path. The proposed research will result in developing a unique and novel curriculum and scalable program in the area of cybersecurity and a set of powerful tools for a fun learning experience in cybersecurity education. In this project, we are focusing on the potential to advance research agendas in cybersecurity and train the future generation with cybersecurity skills and answer fundamental research questions that still exist in the blended learning methodologies for cybersecurity education and assessment. Leadership and entrepreneurship skills are also added to the mix to prepare students for real-world problems. Delivery methods, timing, format, pacing and outcomes alignment will all be assessed to provide a baseline for future research and additional synergy and integration with existing cybersecurity programs to expand or leverage for new cybersecurity and STEM educational research. This is a new model for cybersecurity education, leadership, and entrepreneurship and there is a possibility of a significant leap towards a more advanced cybersecurity educational methodology using this model. The project will also provide a prototype for innovation coupled with character-building and ethical leadership.

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.

The readout of large pixellated detectors with good spectroscopic quality represents a challenge for both front-end and back-end electronics. The TRISTAN project for the search of the Sterile neutrino in the keV-scale, envisions the operation of 21 detection modules equipped with a monolithic array of 166 SDDs each, for β-decay spectroscopy in the KATRIN experiment's spectrometer. Since the trace of the sterile neutrino existence would manifest as a kink of \textbackslashtextless; 1ppm in the continuous spectrum, high accuracy in the acquisition is required. Within this framework, we present the design of a multichannel scalable analog processing and DAQ system named Kerberos, aimed to provide a simple and low-cost multichannel readout option in the early phase of the TRISTAN detector development. It is based on three 16-channel integrated programmable analog pulse processors (SFERA ASICs), high linearity ADCs, and an FPGA. The platform is able to acquire data from up to 48 pixels in parallel, providing also different readout and multiplexing strategies. The use of an analog ASIC-based solution instead of a Digital Pulse Processor, represents a viable and scalable processing solution at the price of slightly limited versatility and count rate.

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.

Mobile Ad-hoc Networks (MANETs) are formed when a group of mobile nodes, communicate through wireless links in the absence of central administration. These features make them more vulnerable to several attacks like identity spoofing which leads to identity disclosure. Providing anonymity and privacy for identity are critical issues, especially when the size of such networks scales up. to avoid the centralization problem for key distribution in MANETs. This paper proposes a key distribution scheme for clustered ad-hoc networks. The network is divided into groups of clusters, and each cluster head is responsible for distributing periodically updated security keys among cluster members, for protecting privacy through encryption. Also, an authentication scheme is proposed to ensure the confidentiality of new members to the cluster. The simulation study proves the effectiveness of the proposed scheme in terms of availability and overhead. It scales well for high dense networks and gives less packet drop rate compared to its centralized counterpart in the presence of malicious nodes.

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.

Some of the key challenges in steganography are imperceptibility and resistance to detection of steganalysis algorithms. Zero steganography is an approach to data hiding such that the cover image is not modified. This paper focuses on the generation of stego-key, which is an essential component of this steganographic approach. This approach utilizes DNA sequences and shifting and flipping operations in its binary code representation. Experimental results show that the key generation algorithm has a low cracking probability. The algorithm satisfies the avalanche criterion.

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.

Wireless security is confronted with the complexity of the secret key distribution process, which is difficult to implement on an Ad Hoc network without a key management infrastructure. The symmetric key extraction mechanism from a response channel in a wireless environment is a very promising alternative solution with the simplicity of the key distribution process. Various mechanisms have been proposed for extracting the symmetric key, but many mechanisms produce low rates of the symmetric key due to the high bit differences that occur. This led to the fact that the reconciliation phase was unable to make corrections, as a result of which many key bits were lost, and the time required to obtain a symmetric key was increased. In this paper, we propose the use of an interval approach that divides the response channel into segments at specific intervals to reduce the key bit difference and increase the key rates. The results of tests conducted in the wireless environment show that the use of these mechanisms can increase the rate of the keys up to 35% compared to existing mechanisms.

Securing various data types such as text, image, and video is needed in real-time communications. The transmission of data over an insecure channel is a permanent challenge, especially in mass Internet applications. Preserving confidentiality and integrity of data toward malicious attacks, accidental devastation, change during transfer, or while in storage must be improved. Data Encryption Standard (DES) is considered as a symmetric-key algorithm that is most widely used for various security purposes. In this work, a Key-based Enhancement of the DES (KE-DES) technique for securing text is proposed. The KEDES is implemented by the application of two steps: the first is merging the Odd/Even bit transformation of every key bit in the DES algorithm. The second step is replacing the right-side expansion of the original DES by using Key-Distribution (K-D) function. The K-D allocation consists of 8-bits from Permutation Choice-1 (PC-1) key outcome. The next 32-bits outcomes from the right-side of data, there is also 8-bits outcome from Permutation Choice-2 (PC-2) in each round. The key and data created randomly, in this case, provide adequate security and the KEDES model is considered more efficient for text encryption.

We consider the problem of covert communication when Channel State Information (CSI) is available non-causally, causally, and strictly causally at both transmitter and receiver, as well as the case when channel state information is only available at the transmitter. Covert communication with respect to an adversary referred to as the "warden", is one in which the distribution induced during communication at the channel output observed by the warden is identical to the output distribution conditioned on an innocent channel-input symbol. In contrast to previous work, we do not assume the availability of a shared key at the transmitter and legitimate receiver; instead shared randomness is extracted from the channel state, in a manner that keeps it secret from the warden despite the influence of the channel state on the warden's output. When CSI is available at both transmitter and receiver, we derive the covert capacity region; when CSI is only available at the transmitter, we derive inner and outer bounds on the covert capacity. We also derive the covert capacity when the warden's channel is less noisy with respect to the legitimate receiver. We provide examples for which covert capacity is zero without channel state information, but is positive in the presence of channel state information.

To improve efficiency of stegosystem on blockchain and balance the time consumption of Encode and Decode operations, we propose a new blockchain-based steganography scheme, called Keys as Secret Messages (KASM), where a codebook of mappings between bitstrings and public keys can be pre-calculated by both sides with some secret parameters pre-negotiated before covert communication. By applying properties of elliptic curves and pseudorandom number generators, we realize key derivation of codebook item, and we construct the stegosystem with provable security under chosen hiddentext attack. By comparing KASM with Blockchain Covert Channel (BLOCCE) and testing on Bitcoin protocol, we conclude that our proposed stegosystem encodes hiddentexts faster than BLOCCE does and can decode stegotexts in highly acceptable time. The balanced time consumption of Encode and Decode operations of KASM make it applicable in the scene of duplex communication. At the same time, KASM does not leak sender’s private keys, so sender’s digital currencies can be protected.

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.