Biblio

The development of a robust strategy for network security is reliant upon a combination of in-house expertise and for completeness attack vectors used by attackers. A honeypot is one of the most popular mechanisms used to gather information about attacks and attackers. However, low-interaction honeypots only emulate an operating system and services, and are more prone to a fingerprinting attack, resulting in severe consequences such as revealing the identity of the honeypot and thus ending the usefulness of the honeypot forever, or worse, enabling it to be converted into a bot used to attack others. A number of tools and techniques are available both to fingerprint low-interaction honeypots and to defend against such fingerprinting; however, there is an absence of fingerprinting techniques to identify the characteristics and behaviours that indicate fingerprinting is occurring. Therefore, this paper proposes a fuzzy technique to correlate the attack actions and predict the probability that an attack is a fingerprinting attack on the honeypot. Initially, an experimental assessment of the fingerprinting attack on the low- interaction honeypot is performed, and a fingerprinting detection mechanism is proposed that includes the underlying principles of popular fingerprinting attack tools. This implementation is based on a popular and commercially available low-interaction honeypot for Windows - KFSensor. However, the proposed fuzzy technique is a general technique and can be used with any low-interaction honeypot to aid in the identification of the fingerprinting attack whilst it is occurring; thus protecting the honeypot from the fingerprinting attack and extending its life.

With the recent advances in information and communication technology, Web and Mobile Internet applications have become a part of our daily lives. These developments have also emerged Information Security concept due to the necessity of protecting information of institutions from Internet attackers. There are many security approaches to provide information security in Enterprise applications. However, using only one of these approaches may not be efficient enough to obtain security. This paper describes a Multi-Layered Framework of implementing two-factor and single sign-on authentication together. The proposed framework generates unique one-time passwords (OTP), which are used to authenticate application data. Nevertheless, using only OTP mechanism does not meet security requirements. Therefore, implementing a separate authentication application which has single sign-on capability is necessary.

Cyber-Physical Systems (CPS) and Internet of Things (IoT) are emerging technologies, which makes the remote sensing and control across heterogeneous network a reality, and has good prospects in industrial applications. Due to the resource constrained feature of CPS devices, the design of security and efficiency balanced authentication scheme for CPS/IoT devices becomes a big challenge in CPS/IoT applications. This paper presents a two-factor authentication with key agreement scheme for CPS/IoT applications. The proposed mechanism integrates IMSI identifier and identity-based remote mutual authentication scheme on BAN logic designs. It supports flawless two-factor and mutual authentication of participants and agreement of session keys for user, device and gateway server. The proposed mechanism also provide user anonymity, it can be adopt in critical applications. Besides, it does not require modifying the software of clients; thus, it is highly flexibly. We believe the proposed mechanism is usable for CPS/IoT applications.

Modern software development and deployment practices encourage complexity and bloat while unintentionally sacrificing efficiency and security. A major driver in this is the overwhelming emphasis on programmers' productivity. The constant demands to speed up development while reducing costs have forced a series of individual decisions and approaches throughout software engineering history that have led to this point. The current state-of-the-practice in the field is a patchwork of architectures and frameworks, packed full of features in order to appeal to: the greatest number of people, obscure use cases, maximal code reuse, and minimal developer effort. The Office of Naval Research (ONR) Total Platform Cyber Protection (TPCP) program seeks to de-bloat software binaries late in the life-cycle with little or no access to the source code or the development process.

Maliciously-injected power load, a.k.a. power attack, has recently surfaced as a new egregious attack vector for dangerously compromising the data center availability. This paper focuses on the emerging threat of power attacks in a multi-tenant colocation data center, an important type of data center where multiple tenants house their own servers and share the power distribution system. Concretely, we discover a novel physical side channel –- a voltage side channel –- which leaks the benign tenants' power usage information at runtime and helps an attacker precisely time its power attacks. The key idea we exploit is that, due to the Ohm's Law, the high-frequency switching operation (40\textasciitilde100kHz) of the power factor correction circuit universally built in today's server power supply units creates voltage ripples in the data center power lines. Importantly, without overlapping the grid voltage in the frequency domain, the voltage ripple signals can be easily sensed by the attacker to track the benign tenants' runtime power usage and precisely time its power attacks. We evaluate the timing accuracy of the voltage side channel in a real data center prototype, demonstrating that the attacker can extract benign tenants' power pattern with a great accuracy (correlation coefficient = 0.90+) and utilize 64% of all the attack opportunities without launching attacks randomly or consecutively. Finally, we highlight a few possible defense strategies and extend our study to more complex three-phase power distribution systems used in large multi-tenant data centers.

The problem of optimal attack path analysis is one of the hotspots in network security. Many methods are available to calculate an optimal attack path, such as Q-learning algorithm, heuristic algorithms, etc. But most of them have shortcomings. Some methods can lead to the problem of path loss, and some methods render the result un-comprehensive. This article proposes an improved Monte Carlo Graph Search algorithm (IMCGS) to calculate optimal attack paths in target network. IMCGS can avoid the problem of path loss and get comprehensive results quickly. IMCGS is divided into two steps: selection and backpropagation, which is used to calculate optimal attack paths. A weight vector containing priority, host connection number, CVSS value is proposed for every host in an attack path. This vector is used to calculate the evaluation value, the total CVSS value and the average CVSS value of a path in the target network. Result for a sample test network is presented to demonstrate the capabilities of the proposed algorithm to generate optimal attack paths in one single run. The results obtained by IMCGS show good performance and are compared with Ant Colony Optimization Algorithm (ACO) and k-zero attack graph.

In order to evaluate the network security risks and implement effective defenses in industrial control system, a risk assessment method for industrial control systems based on attack graphs is proposed. Use the concept of network security elements to translate network attacks into network state migration problems and build an industrial control network attack graph model. In view of the current subjective evaluation of expert experience, the atomic attack probability assignment method and the CVSS evaluation system were introduced to evaluate the security status of the industrial control system. Finally, taking the centralized control system of the thermal power plant as the experimental background, the case analysis is performed. The experimental results show that the method can comprehensively analyze the potential safety hazards in the industrial control system and provide basis for the safety management personnel to take effective defense measures.

Attack graph approach is a common tool for the analysis of network security. However, analysis of attack graphs could be complicated and difficult depending on the attack graph size. This paper presents an approximate analysis approach for attack graphs based on Q-learning. First, we employ multi-host multi-stage vulnerability analysis (MulVAL) to generate an attack graph for a given network topology. Then we refine the attack graph and generate a simplified graph called a transition graph. Next, we use a Q-learning model to find possible attack routes that an attacker could use to compromise the security of the network. Finally, we evaluate the approach by applying it to a typical IT network scenario with specific services, network configurations, and vulnerabilities.

The use of real-time video streaming is increasing day-by-day, and its security has become a serious issue now. Video encryption is a challenging task because of its large frame size. Video encryption can be done with symmetric key as well as asymmetric key encryption. Among different asymmetric key encryption technique, ECC performs better than other algorithms like RSA in terms of smaller key size and faster encryption and decryption operation. In this work, we have analyzed the performance of 18 different ECC curves and suggested some suitable curves for real-time video encryption.

Public key cryptography or asymmetric keys are widely used in the implementation of data security on information and communication systems. The RSA algorithm (Rivest, Shamir, and Adleman) is one of the most popular and widely used public key cryptography because of its less complexity. RSA has two main functions namely the process of encryption and decryption process. Digital Signature Algorithm (DSA) is a digital signature algorithm that serves as the standard of Digital Signature Standard (DSS). DSA is also included in the public key cryptography system. DSA has two main functions of creating digital signatures and checking the validity of digital signatures. In this paper, the authors compare the computational times of RSA and DSA with some bits and choose which bits are better used. Then combine both RSA and DSA algorithms to improve data security. From the simulation results, the authors chose RSA 1024 for the encryption process and added digital signatures using DSA 512, so the messages sent are not only encrypted but also have digital signatures for the data authentication process.

Deep neural network based steganalysis has developed rapidly in recent years, which poses a challenge to the security of steganography. However, there is no steganography method that can effectively resist the neural networks for steganalysis at present. In this paper, we propose a new strategy that constructs enhanced covers against neural networks with the technique of adversarial examples. The enhanced covers and their corresponding stegos are most likely to be judged as covers by the networks. Besides, we use both deep neural network based steganalysis and high-dimensional feature classifiers to evaluate the performance of steganography and propose a new comprehensive security criterion. We also make a tradeoff between the two analysis systems and improve the comprehensive security. The effectiveness of the proposed scheme is verified with the evidence obtained from the experiments on the BOSSbase using the steganography algorithm of WOW and popular steganalyzers with rich models and three state-of-the-art neural networks.

Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cybersecurity threats and attacks by utilizing data mining techniques in the field of Artificial Intelligence. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection utilizing machine learning algorithms for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.

The field of robotics has matured using artificial intelligence and machine learning such that intelligent robots are being developed in the form of autonomous vehicles. The anticipated widespread use of intelligent robots and their potential to do harm has raised interest in their security. This research evaluates a cyberattack on the machine learning policy of an autonomous vehicle by designing and attacking a robotic vehicle operating in a dynamic environment. The primary contribution of this research is an initial assessment of effective manipulation through an indirect attack on a robotic vehicle using the Q learning algorithm for real-time routing control. Secondly, the research highlights the effectiveness of this attack along with relevant artifact issues.

Virtualization offers the possibility of hosting services of multiple customers on shared hardware. When more than one Virtual Machine (VM) run on the same host, memory deduplication can save physical memory by merging identical pages of the VMs. However, this comes at the cost of leaking information between VMs. Based on that, we propose a novel timing-based side-channel attack that allows to identify software versions running in co-resident VMs or on the host. Our attack tests for the existence of memory pages in co-resident VMs that are unique among all versions of the respective software. Our evaluation results indicate that with few repetitions of our attack we can precisely identify software versions within reasonable time frames.

Virtual Machine Introspection (VMI) is an emerging family of techniques for extracting data from virtual machines without the use of active monitoring probes within the target machines themselves. In VMI based systems, the data is collected at the hypervisor-level by analyzing the state of virtual machines. This has the benefit of making collection harder to detect and block by malware as there is nothing in the machine indicating that monitoring is taking place. In this paper we present Nitro Web, a web-based monitoring system for virtual machines that uses virtual machine introspection for data collection. The platform is capable of detecting and visualizing system call activity taking place within virtual machines in real-time. The secondary purpose of this paper is to offer an introduction to Nitro virtual machine introspection framework that we have been involved in developing. In this paper, we reflect on how Nitro Framework can be used for building applications making use of VMI data.

Design of anonymous authentication scheme is one of the most important challenges in Vehicular Ad hoc Networks (VANET). Most of the existing schemes have high computational and communication overhead and they do not meet security requirements. Recently, Azees et al. have introduced an Efficient Anonymous Authentication with Conditional Privacy-Preserving (EAAP) scheme for VANET and claimed that it is secure. In this paper, we show that this protocol is vulnerable against replay attack, impersonation attack and message modification attack. Also, we show that the messages sent by a vehicle are linkable. Therefore, an adversary can easily track the vehicles. In addition, it is shown that vehicles face with some problems when they enter in a new Trusted Authority (TA) range. As a solution, we propose a new authentication protocol which is more secure than EAAP protocol without increasing its computational and communication overhead.

Inter-vehicle communications disclose rich information about vehicle whereabouts. Pseudonymous authentication secures communication while enhancing user privacy thanks to a set of anonymized certificates, termed pseudonyms. Vehicles switch the pseudonyms (and the corresponding private key) frequently; we term this pseudonym transition process. However, exactly because vehicles can in principle change their pseudonyms asynchronously, an adversary that eavesdrops (pseudonymously) signed messages, could link pseudonyms based on the times of pseudonym transition processes. In this poster, we show how one can link pseudonyms of a given vehicle by simply looking at the timing information of pseudonym transition processes. We also propose "mix-zone everywhere": time-aligned pseudonyms are issued for all vehicles to facilitate synchronous pseudonym update; as a result, all vehicles update their pseudonyms simultaneously, thus achieving higher user privacy protection.

This paper presents the development and configuration of a virtually air-gapped cloud environment in AWS, to secure the production software workloads and patient data (ePHI) and to achieve HIPAA compliance.

While existing research has explored the trade-off between security and performance, these efforts primarily focus on software consumers and often overlook the effectiveness and productivity of software producers. In this paper, we highlight an established security practice, air-gap isolation, and some challenges it uniquely instigates. To better understand and start quantifying the impacts of air-gap isolation on software development productivity, we conducted a survey at a commercial software company: Analytical Graphics, Inc. Based on our insights of dealing with air-gap isolation daily, we suggest some possible directions for future research. Our goal is to bring attention to this neglected area of research and to start a discussion in the SE community about the struggles faced by many commercial and governmental organizations.

We provide a systemization of knowledge of the recent progress made in addressing the crucial problem of deep learning on encrypted data. The problem is important due to the prevalence of deep learning models across various applications, and privacy concerns over the exposure of deep learning IP and user's data. Our focus is on provably secure methodologies that rely on cryptographic primitives and not trusted third parties/platforms. Computational intensity of the learning models, together with the complexity of realization of the cryptography algorithms hinder the practical implementation a challenge. We provide a summary of the state-of-the-art, comparison of the existing solutions, as well as future challenges and opportunities.

Machine learning (ML) algorithms provide a good solution for many security sensitive applications, they themselves, however, face the threats of adversary attacks. As a key problem in machine learning, how to design robust feature selection algorithms against these attacks becomes a hot issue. The current researches on defending evasion attacks mainly focus on wrapped adversarial feature selection algorithm, i.e., WAFS, which is dependent on the classification algorithms, and time cost is very high for large-scale data. Since mRMR (minimum Redundancy and Maximum Relevance) algorithm is one of the most popular filter algorithms for feature selection without considering any classifier during feature selection process. In this paper, we propose a novel adversary-aware feature selection algorithm under filter model based on mRMR, named FAFS. The algorithm, on the one hand, takes the correlation between a single feature and a label, and the redundancy between features into account; on the other hand, when selecting features, it not only considers the generalization ability in the absence of attack, but also the robustness under attack. The performance of four algorithms, i.e., mRMR, TWFS (Traditional Wrapped Feature Selection algorithm), WAFS, and FAFS is evaluated on spam filtering and PDF malicious detection in the Perfect Knowledge attack scenarios. The experiment results show that FAFS has a better performance under evasion attacks with less time complexity, and comparable classification accuracy.

Along with the rapid development of hardware security techniques, the revolutionary growth of countermeasures or attacking methods developed by intelligent and adaptive adversaries have significantly complicated the ability to create secure hardware systems. Thus, there is a critical need to (re)evaluate existing or new hardware security techniques against these state-of-the-art attacking methods. With this in mind, this paper presents a novel framework for incorporating active learning techniques into hardware security field. We demonstrate that active learning can significantly improve the learning efficiency of physical unclonable function (PUF) modeling attack, which samples the least confident and the most informative challenge-response pair (CRP) for training in each iteration. For example, our experimental results show that in order to obtain a prediction error below 4%, 2790 CRPs are required in passive learning, while only 811 CRPs are required in active learning. The sampling strategies and detailed applications of PUF modeling attack under various environmental conditions are also discussed. When the environment is very noisy, active learning may sample a large number of mislabeled CRPs and hence result in high prediction error. We present two methods to mitigate the contradiction between informative and noisy CRPs.

In order to cater the growing spectrum demands of large scale future 5G Internet of Things (IoT) applications, Dynamic Spectrum Access (DSA) based networks are being proposed as a high-throughput and cost-effective solution. However the lack of understanding of DSA paradigm's inherent security vulnerabilities on IoT networks might become a roadblock towards realizing such spectrum aware 5G vision. In this paper, we make an attempt to understand how such inherent DSA vulnerabilities in particular Spectrum Sensing Data Falsification (SSDF) attacks can be exploited by collaborative group of selfish adversaries and how that can impact the performance of spectrum aware IoT applications. We design a utility based selfish adversarial model mimicking collaborative SSDF attack in a cooperative spectrum sensing scenario where IoT networks use dedicated environmental sensing capability (ESC) for spectrum availability estimation. We model the interactions between the IoT system and collaborative selfish adversaries using a leader-follower game and investigate the existence of equilibrium. Using simulation results, we show the nature of adversarial and system utility components against system variables. We also explore Pareto-optimal adversarial strategy design that maximizes the attacker utility for varied system strategy spaces.

In this paper, security of secret key extraction scheme is evaluated for private communication between legitimate wireless devices. Multiple adversaries that distribute around these legitimate wireless devices eavesdrop on the data transmitted between them, and deduce the secret key. Conditional min-entropy given the view of those adversaries is utilized as security evaluation metric in this paper. Besides, the wiretap channel model and hidden Markov model (HMM) are regarded as the channel model and a dynamic programming approach is used to approximate conditional min- entropy. Two algorithms are proposed to mathematically calculate the conditional min- entropy by combining the Viterbi algorithm with the Forward algorithm. Optimal method with multiple adversaries (OME) algorithm is proposed firstly, which has superior performance but exponential computation complexity. To reduce this complexity, suboptimal method with multiple adversaries (SOME) algorithm is proposed, using performance degradation for the computation complexity reduction. In addition to the theoretical analysis, simulation results further show that the OME algorithm indeed has superior performance as well as the SOME algorithm has more efficient computation.

With an increase in targeted attacks such as advanced persistent threats (APTs), enterprise system defenders require comprehensive frameworks that allow them to collaborate and evaluate their defense systems against such attacks. MITRE has developed a framework which includes a database of different kill-chains, tactics, techniques, and procedures that attackers employ to perform these attacks. In this work, we leverage natural language processing techniques to extract attacker actions from threat report documents generated by different organizations and automatically classify them into standardized tactics and techniques, while providing relevant mitigation advisories for each attack. A naïve method to achieve this is by training a machine learning model to predict labels that associate the reports with relevant categories. In practice, however, sufficient labeled data for model training is not always readily available, so that training and test data come from different sources, resulting in bias. A naïve model would typically underperform in such a situation. We address this major challenge by incorporating an importance weighting scheme called bias correction that efficiently utilizes available labeled data, given threat reports, whose categories are to be automatically predicted. We empirically evaluated our approach on 18,257 real-world threat reports generated between year 2000 and 2018 from various computer security organizations to demonstrate its superiority by comparing its performance with an existing approach.