Software-based Decoy System for Insider Threats

Decoy technology and the use of deception are useful in securing critical computing systems by confounding and confusing adversaries with fake information. Deception leverages uncertainty forcing adversaries to expend considerable effort to differentiate realistic useful information from purposely planted false information. In this paper, we propose software-based decoy system that aims to deceive insiders, to detect the exfiltration of proprietary source code. The proposed system generates believable Java source code that appear to an adversary to be entirely valuable proprietary software. Bogus software is generated iteratively using code obfuscation techniques to transform original software using various transformation methods. Beacons are also injected into bogus software to detect the exfiltration and to make an alert if the decoy software is touched, compiled or executed. Based on similarity measurement, the experimental results demonstrate that the generated bogus software is different from the original software while maintaining similar complexity to confuse an adversary as to which is real and which is not

The effects of listing status on a firm’s lease accounting: Evidence from South Korea

This study examines how the listing status affects a firm’s choice of lease accounting, using 7,023 firm-year observations that record either an operating or a capital lease from 2001 to 2013 in Korea. We find that unlisted firms are more likely to opt for operating leases, and to have a higher ratio of operating leases than listed firms are. These results indicate that unlisted firms tend to prefer operating leases which can be used as a tool to avoid increasing debt levels and to benefit from off-balance sheet financing (or unrecorded liabilities), compared to listed firms. This study contributes to the current accounting literature as it is the first to provide empirical evidence regarding the impact of the listing status on a firm’s lease accounting

Hyperglycemia identification using ECG in deep learning era

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration

Flexible and Robust Real-Time Intrusion Detection Systems to Network Dynamics

Deep learning-based intrusion detection systems have advanced due to their technological innovations such as high accuracy, automation, and scalability to develop an effective network intrusion detection system (NIDS). However, most of the previous research has focused on model generation through intensive analysis of feature engineering instead of considering real environments. They have limitations to applying the previous methods for a real network environment to detect real-time network attacks. In this paper, we propose a new flexible and robust NIDS based on Recurrent Neural Network (RNN) with a multi-classifier to generate a detection model in real time. The proposed system adaptively and intelligently adjusts the generated model with given system parameters that can be used as security parameters to defend against the attacker’s obfuscation techniques in real time. In the experimental results, the proposed system detects network attacks with a high accuracy and high-speed model upgrade in real-time while showing robustness under an attack

A Blockchain-Based Retribution Mechanism for Collaborative Intrusion Detection

Collaborative intrusion detection approach uses the shared detection signature between the collaborative participants to facilitate coordinated defense. In the context of collaborative intrusion detection system (CIDS), however, there is no research focusing on the efficiency of the shared detection signature. The inefficient detection signature costs not only the IDS resource but also the process of the peer-to-peer (P2P) network. In this paper, we therefore propose a blockchain-based retribution mechanism, which aims to incentivize the participants to contribute to verifying the efficiency of the detection signature in terms of certain distributed consensus. We implement a prototype using Ethereum blockchain, which instantiates a token-based retribution mechanism and a smart contract-enabled voting-based distributed consensus. We conduct a number of experiments built on the prototype, and the experimental results demonstrate the effectiveness of the proposed approach

ECG Biometric Authentication: A Comparative Analysis

Robust authentication and identification methods become an indispensable urgent task to protect the integrity of the devices and the sensitive data. Passwords have provided access control and authentication, but have shown their inherent vulnerabilities. The speed and convenience factor are what makes biometrics the ideal authentication solution as they could have a low probability of circumvention. To overcome the limitations of the traditional biometric systems, electrocardiogram (ECG) has received the most attention from the biometrics community due to the highly individualized nature of the ECG signals and the fact that they are ubiquitous and difficult to counterfeit. However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases. In this paper, we contribute to creating a new large gallery off-the-person ECG datasets that can provide new opportunities for the ECG biometric research community. We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics. Our results have shown that our ECG biometric authentication outperforms existing methods lacking the ability to efficiently extract features, filtering, segmentation, and matching. This is evident by obtaining 100% accuracy for PTB, MIT-BHI, CEBSDB, CYBHI, ECG-ID, and in-house ECG-BG database in spite of noisy, unhealthy ECG signals while performing five-fold cross-validation. In addition, an average of 2.11% EER among 1,694 subjects is obtained

Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks

Performance prediction in wireless mobile networks is essential for diverse purposes in network management and operation. Particularly, the position of mobile devices is crucial to estimating the performance in the mobile communication setting. With its importance, this paper investigates mobile communication performance based on the coordinate information of mobile devices. We analyze a recent 5G data collection and examine the feasibility of location-based performance prediction. As location information is key to performance prediction, the basic assumption of making a relevant prediction is the correctness of the coordinate information of devices given. With its criticality, this paper also investigates the impact of position falsification on the ML-based performance predictor, which reveals the significant degradation of the prediction performance under such attacks, suggesting the need for effective defense mechanisms against location spoofing threats

Structure-function relationships of wheat flavone O-methyltransferase: Homology modeling and site-directed mutagenesis

<p>Abstract</p> <p>Background</p> <p>Wheat (<it>Triticum aestivum </it>L.) <it>O</it>-methyltransferase (TaOMT2) catalyzes the sequential methylation of the flavone, tricetin, to its 3'-methyl- (selgin), 3',5'-dimethyl- (tricin) and 3',4',5'-trimethyl ether derivatives. Tricin, a potential multifunctional nutraceutical, is the major enzyme reaction product. These successive methylations raised the question as to whether they take place in one, or different active sites. We constructed a 3-D model of this protein using the crystal structure of the highly homologous <it>Medicago sativa </it>caffeic acid/5-hydroxyferulic acid <it>O</it>-methyltransferase (MsCOMT) as a template with the aim of proposing a mechanism for multiple methyl transfer reactions in wheat.</p> <p>Results</p> <p>This model revealed unique structural features of TaOMT2 which permit the stepwise methylation of tricetin. Substrate binding is mediated by an extensive network of H-bonds and van der Waals interactions. Mutational analysis of structurally guided active site residues identified those involved in binding and catalysis. The partly buried tricetin active site, as well as proximity and orientation effects ensured sequential methylation of the substrate within the same pocket. Stepwise methylation of tricetin involves deprotonation of its hydroxyl groups by a His262-Asp263 pair followed by nucleophilic attack of SAM-methyl groups. We also demonstrate that Val309, which is conserved in a number of graminaceous flavone OMTs, defines the preference of TaOMT2 for tricetin as the substrate.</p> <p>Conclusions</p> <p>We propose a mechanism for the sequential methylation of tricetin, and discuss the potential application of TaOMT2 to increase the production of tricin as a nutraceutical. The single amino acid residue in TaOMT2, Val309, determines its preference for tricetin as the substrate, and may define the evolutionary differences between the two closely related proteins, COMT and flavone OMT.</p