Biblio

In this paper, we discuss the digital forensic procedure and techniques for analyzing the local artifacts from four popular Instant Messaging applications in Android. As part of our findings, the user chat messages details and contacts were investigated for each application. By using two smartphones with different brands and the latest Android operating systems as experimental objects, we conducted digital investigations in a forensically sound manner. We summarize our findings regarding the different Instant Messaging chat modes and the corresponding encryption status of artifacts for each of the four applications. Our findings can be helpful to many mobile forensic investigations. Additionally, these findings may present values to Android system developers, Android mobile app developers, mobile security researchers as well as mobile users.

Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement.

This work explores attack and attacker profiles using a VoIP-based Honeypot. We implemented a low interaction honeypot environment to identify the behaviors of the attackers and the services most frequently used. We watched honeypot for 180 days and collected 242.812 events related to FTP, SIP, MSSQL, MySQL, SSH, SMB protocols. The results provide an in-depth analysis about both attacks and attackers profile, their tactics and purposes. It also allows understanding user interaction with a vulnerable honeypot environment.

Traditional security practices focus on negative incentives that attempt to force compliance through constraints, monitoring, and punishment. This paper describes a missing dimension of most organizations' insider threat defense-one that explicitly considers positive incentives for attracting individuals to act in the interests of the organization. Positive incentives focus on properties of the organizational context of workforce management practices - including those relating to organizational supportiveness, coworker connectedness, and job engagement. Without due attention to the organizational context in which insider threats occur, insider misbehaviors may simply reoccur as a natural response to counterproductive or dysfunctional management practices. A balanced combination of positive and negative incentives can improve employees' relationships with the organization and provide a means for employees to better cope with personal and professional stressors. An insider threat program that balances organizational incentives can become an advocate for the workforce and a means for improving employee work life - a welcome message to employees who feel threatened by programs focused on discovering insider wrongdoing.

The CAPTCHA is an effective method commonly used in live interactive proofs on the Internet. The widely used CAPTCHAs are text-based schemes. In this paper, we document how we have broken such text-based scheme used by a website CAPTCHA. We use the sliding window to segment 1001 pieces of CAPTCHA to get 5900 images with single-character useful information, a total of 25 categories. In order to make the convolution neural network learn more image features, we augmented the data set to get 129924 pictures. The data set is trained and tested in AlexNet and GoogLeNet to get the accuracy of 87.45% and 98.92%, respectively. The experiment shows that the optimized network parameters can make the accuracy rate up to 92.7% in AlexNet and 98.96% in GoogLeNet.

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for website to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA, improve CAPTCHA technology, it can also promote the technologies of license plate recognition and handwriting recognition. This paper proposed a method based on Convolutional Neural Network (CNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over-fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion.

Despite decades of research on software diversification, only address space layout randomization has seen widespread adoption. Code randomization, an effective defense against return-oriented programming exploits, has remained an academic exercise mainly due to i) the lack of a transparent and streamlined deployment model that does not disrupt existing software distribution norms, and ii) the inherent incompatibility of program variants with error reporting, whitelisting, patching, and other operations that rely on code uniformity. In this work we present compiler-assisted code randomization (CCR), a hybrid approach that relies on compiler-rewriter cooperation to enable fast and robust fine-grained code randomization on end-user systems, while maintaining compatibility with existing software distribution models. The main concept behind CCR is to augment binaries with a minimal set of transformation-assisting metadata, which i) facilitate rapid fine-grained code transformation at installation or load time, and ii) form the basis for reversing any applied code transformation when needed, to maintain compatibility with existing mechanisms that rely on referencing the original code. We have implemented a prototype of this approach by extending the LLVM compiler toolchain, and developing a simple binary rewriter that leverages the embedded metadata to generate randomized variants using basic block reordering. The results of our experimental evaluation demonstrate the feasibility and practicality of CCR, as on average it incurs a modest file size increase of 11.46% and a negligible runtime overhead of 0.28%, while it is compatible with link-time optimization and control flow integrity.

Code churn has been successfully used to identify defect inducing changes in software development. Our recent analysis of the cross-release code churn showed that several design metrics exhibit moderate correlation with the number of defects in complex systems. The goal of this paper is to explore whether cross-release code churn can be used to identify critical design change and contribute to prediction of defects for software in evolution. In our case study, we used two types of data from consecutive releases of open-source projects, with and without cross-release code churn, to build standard prediction models. The prediction models were trained on earlier releases and tested on the following ones, evaluating the performance in terms of AUC, GM and effort aware measure Pop. The comparison of their performance was used to answer our research question. The obtained results showed that the prediction model performs better when cross-release code churn is included. Practical implication of this research is to use cross-release code churn to aid in safe planning of next release in software development.

The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.

In the last few decades, the relative simplicity of the logistic map made it a widely accepted point in the consideration of chaos, which is having the good properties of unpredictability, sensitiveness in the key values and ergodicity. Further, the system parameters fit the requirements of a cipher widely used in the field of cryptography, asymmetric and symmetric key chaos based cryptography, and for pseudorandom sequence generation. Also, the hardware-based embedded system is configured on FPGA devices for high performance. In this paper, a novel stream cipher using chaotic logistic map is proposed. The two chaotic logistic maps are coded using Verilog HDL and implemented on commercially available FPGA hardware using Xilinx device: XC3S250E for the part: FT256 and operated at frequency of 62.20 MHz to generate the non-recursive key which is used in key scheduling of pseudorandom number generation (PRNG) to produce the key stream. The realization of proposed cryptosystem in this FPGA device accomplishes the improved efficiency equal to 0.1186 Mbps/slice. Further, the generated binary sequence from the experiment is analyzed for X-power, thermal analysis, and randomness tests are performed using NIST statistical.

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.

Text-based CAPTCHAs are still commonly used to attempt to prevent automated access to web services. By displaying an image of distorted text, they attempt to create a challenge image that OCR software can not interpret correctly, but a human user can easily determine the correct response to. This work focuses on a CAPTCHA used by a popular Chinese language question-and-answer website and how resilient it is to modern machine learning methods. While the majority of text-based CAPTCHAs focus on transcription tasks, the CAPTCHA solved in this work is based on localization of inverted symbols in a distorted image. A convolutional neural network (CNN) was created to evaluate the likelihood of a region in the image belonging to an inverted character. It is used with a feature map and clustering to identify potential locations of inverted characters. Training of the CNN was performed using curriculum learning and compared to other potential training methods. The proposed method was able to determine the correct response in 95.2% of cases of a simulated CAPTCHA and 67.6% on a set of real CAPTCHAs. Potential methods to increase difficulty of the CAPTCHA and the success rate of the automated solver are considered.

This paper explores low cost technical solutions that can help organizations prevent, detect, and respond to insider incidents. Features and functionality associated with insider risk mitigation are presented. A taxonomy for high-level categories of insider threat tools is presented. A discussion of the relationship between the types of tools points out the nuances of insider threat control deployment, and considerations for selecting, implementing, and operating insider threat tools are provided.

In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can offer a cost-effective power management and supply alternative to national power grid connections. RCSMG architectures handle communications over distributed lossy networks to minimize operation costs. However, the unreliable nature of lossy networks makes privacy an important consideration. Existing anonymisation works on data perturbation work mainly by distortion with additive noise. Apply these solutions to RCSMGs is problematic, because deliberate noise additions must be distinguishable both from system and adversarial generated noise. In this paper, we present a brief survey of privacy risks in RCSMGs centered on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.

SQL injection is well known a method of executing SQL queries and retrieving sensitive information from a website connected database. This process poses a threat to those applications which are poorly coded in the today's world. SQL is considered as one of the top 10 vulnerabilities even in 2018. To keep a track of the vulnerabilities that each of the websites are facing, we employ a tool called Acunetix which allows us to find the vulnerabilities of a specific website. This tool also suggests measures on how to ensure preventive measures. Using this implementation, we discover vulnerabilities in an actual website. Such a real-world implementation would be useful for instructional use in a foundational cybersecurity course.

Organizations not only need to defend their IT systems against external cyber attackers, but also from malicious insiders, that is, agents who have infiltrated an organization or malicious members stealing information for their own profit. In particular, malicious insiders can leak a document by simply opening it and taking pictures of the document displayed on the computer screen with a digital camera. Using a digital camera allows a perpetrator to easily avoid a log trail that results from using traditional communication channels, such as sending the document via email. This makes it difficult to identify and prove the identity of the perpetrator. Even a policy prohibiting the use of any device containing a camera cannot eliminate this threat since tiny cameras can be hidden almost everywhere. To address this leakage vector, we propose a novel screen watermarking technique that embeds hidden information on computer screens displaying text documents. The watermark is imperceptible during regular use, but can be extracted from pictures of documents shown on the screen, which allows an organization to reconstruct the place and time of the data leak from recovered leaked pictures. Our approach takes advantage of the fact that the human eye is less sensitive to small luminance changes than digital cameras. We devise a symbol shape that is invisible to the human eye, but still robust to the image artifacts introduced when taking pictures. We complement this symbol shape with an error correction coding scheme that can handle very high bit error rates and retrieve watermarks from cropped and compressed pictures. We show in an experimental user study that our screen watermarks are not perceivable by humans and analyze the robustness of our watermarks against image modifications.

The article describes design and realization of security system based on single-chip microcontrollers. System includes sensor modules for unauthorized entrance detection based on magnetic contact, measuring carbon monoxide level, movement detection and measuring temperature and humidity. System also includes control unit, control panel and development board Arduino with ethernet interface connected for web server implementation.

Two-factor authentication (2FA) popularly works by verifying something the user knows (a password) and something she possesses (a token, popularly instantiated with a smart phone). Conventional 2FA systems require extra interaction like typing a verification code, which is not very user-friendly. For improved user experience, recent work aims at zero-effort 2FA, in which a smart phone placed close to a computer (where the user enters her username/password into a browser to log into a server) automatically assists with the authentication. To prove her possession of the smart phone, the user needs to prove the phone is on the login spot, which reduces zero-effort 2FA to co-presence detection. In this paper, we propose SoundAuth, a secure zero-effort 2FA mechanism based on (two kinds of) ambient audio signals. SoundAuth looks for signs of proximity by having the browser and the smart phone compare both their surrounding sounds and certain unpredictable near-ultrasounds; if significant distinguishability is found, SoundAuth rejects the login request. For the ambient signals comparison, we regard it as a classification problem and employ a machine learning technique to analyze the audio signals. Experiments with real login attempts show that SoundAuth not only is comparable to existent schemes concerning utility, but also outperforms them in terms of resilience to attacks. SoundAuth can be easily deployed as it is readily supported by most smart phones and major browsers.

This paper proposes an architecture of Secure Shell (SSH) honeypot using port knocking and Intrusion Detection System (IDS) to learn the information about attacks on SSH service and determine proper security mechanisms to deal with the attacks. Rapid development of information technology is directly proportional to the number of attacks, destruction, and data theft of a system. SSH service has become one of the popular targets from the whole vulnerabilities which is existed. Attacks on SSH service have various characteristics. Therefore, it is required to learn these characteristics by typically utilizing honeypots so that proper mechanisms can be applied in the real servers. Various attempts to learn the attacks and mitigate them have been proposed, however, attacks on SSH service are kept occurring. This research proposes a different and effective strategy to deal with the SSH service attack. This is done by combining port knocking and IDS to make the server keeps the service on a closed port and open it under user demand by sending predefined port sequence as an authentication process to control the access to the server. In doing so, it is evident that port knocking is effective in protecting SSH service. The number of login attempts obtained by using our proposed method is zero.

Assertions are helpful in program analysis, such as software testing and verification. The most challenging part of automatically recommending assertions is to design the assertion patterns and to insert assertions in proper locations. In this paper, we develop Weak-Assert, a weakness-oriented assertion recommendation toolkit for program analysis of C code. A weakness-oriented assertion is an assertion which can help to find potential program weaknesses. Weak-Assert uses well-designed patterns to match the abstract syntax trees of source code automatically. It collects significant messages from trees and inserts assertions into proper locations of programs. These assertions can be checked by using program analysis techniques. The experiments are set up on Juliet test suite and several actual projects in Github. Experimental results show that Weak-Assert helps to find 125 program weaknesses in 26 actual projects. These weaknesses are confirmed manually to be triggered by some test cases.

We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.

This paper investigates the use of deep reinforcement learning (DRL) in the design of a "universal" MAC protocol referred to as Deep-reinforcement Learning Multiple Access (DLMA). The design framework is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". While the scope of DARPA SC2 is broad and involves the redesign of PHY, MAC, and Network layers, this paper's focus is narrower and only involves the MAC design. In particular, we consider the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The DRL agents of DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the rewards - in accordance with the DRL algorithmic framework - a DRL agent can learn the optimal MAC strategy for harmonious co-existence with TDMA and ALOHA nodes. In particular, the use of neural networks in DRL (as opposed to traditional reinforcement learning) allows for fast convergence to optimal solutions and robustness against perturbation in hyper- parameter settings, two essential properties for practical deployment of DLMA in real wireless networks.

The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.

Wireless cameras are widely deployed in surveillance systems for security guarding. However, the privacy concerns associated with unauthorized videotaping, are drawing an increasing attention recently. Existing detection methods for unauthorized wireless cameras are either limited by their detection accuracy or requiring dedicated devices. In this paper, we propose DeWiCam, a lightweight and effective detection mechanism using smartphones. The basic idea of DeWiCam is to utilize the intrinsic traffic patterns of flows from wireless cameras. Compared with traditional traffic pattern analysis, DeWiCam is more challenging because it cannot access the encrypted information in the data packets. Yet, DeWiCam overcomes the difficulty and can detect nearby wireless cameras reliably. To further identify whether a camera is in an interested room, we propose a human-assisted identification model. We implement DeWiCam on the Android platform and evaluate it with extensive experiments on 20 cameras. The evaluation results show that DeWiCam can detect cameras with an accuracy of 99% within 2.7 s.

In this paper, the rotor unbalanced magnetic pull (UMP) characteristics of different field winding inter-turn short-circuit (FWISC) positions in turbo-generator are studied. Firstly, the qualitative analysis on the air gap magnetic flux density (MFD), as well as the rotor UMPs in X-direction and Y-direction, is carried out. Then the finite element numerical simulations are respectively taken to calculate the quantitative data of rotor UMP under normal condition and three different short-circuit positions. Finally, the variation rules based on rotor UMP characteristics by experimental analysis are obtained. It is shown that the occurrence of FWISC will induce generally fundamental-frequency UMP acting on the rotor in X-direction. Moreover, the different positions of FWISC are found to be sensitive to the rotor UMP amplitudes. The closer the short-circuit position is to the big teeth, the larger the rotor UMP amplitudes in X-direction will be.