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In this paper, we provide the design and implementation of UOIT, a text entry method optimized for smartwatches. UOIT uses only one page where a user can see and tap directly for entry without any additional actions, such as zoom-in/zoom-out and swipes, which are required in the existing entry methods. To fully utilize the constrained screen space and to address the "fat finger" problem, we use a technique called "drawing-like typing", which reduces the 26 small alphabetic keys into 13 large keys with a dual input property. To evaluate the performance of UOIT, we conducted two user studies while varying the learning period. In the short-term experiments (i.e., two days), we observed a fast learning curve of users when using the UIOT keyboard. Moreover, with the long-term experiments (i.e., a month), we show that users can type as fast as QWERTY keyboard but with much less errors. Moreover, UOIT outperforms the state-of-the-art keyboard in both speed and error rate.

Given the limited scalability of dynamic analysis, static analysis, such as the use of Control Flow Graph (CFG)-based features, is widely used by machine learning algorithms for malware analysis and detection. However, recent studies have shown these approaches are susceptible to adversarial attacks by adding codes to the binaries with an intention to fool detection systems. This study proposes a malware detection system robust to adversarial attacks. We examine the performance of the state-of-the-art methods against adversarial IoT software crafted using the graph embedding and augmentation techniques; namely, we study the robustness of such methods against two black-box adversarial methods, GEA and SGEA, to generate Adversarial Examples (AEs) with reduced overhead, and keeping their practicality intact. Our comprehensive experimentation with GEA-based AEs show the relation between misclassification and the graph size of the injected sample. Upon optimization and with small perturbation, by use of SGEA, all IoT malware samples are misclassified as benign. This highlights the vulnerability of current detection systems under adversarial settings. With the landscape of possible adversarial attacks, we then propose DL-FHMC, a fine-grained hierarchical learning approach for malware detection and classification, that is robust to AEs with a capability to detect 88.52% of the malicious AEs.

The Linux shell is a command-line interpreter that provides users with a command interface to the operating system, allowing them to perform various functions. Although very useful in building capabilities at the edge, the Linux shell can be exploited, giving adversaries a prime opportunity to use them for malicious activities. With access to IoT devices, malware authors can abuse the Linux shell of those devices to propagate infections and launch large-scale attacks, e.g., DDoS. In this work, we provide a first look at the tasks managed by shell commands in Linux-based IoT malware towards detection. We analyze malicious shell commands found in IoT malware and build a neural network-based model, , to detect malicious shell commands. Namely, we collected a large dataset of shell commands, including malicious commands extracted from 2,891 IoT malware samples and benign commands collected from real-world network traffic analysis and volunteered data from Linux users. Using conventional machine and deep learning-based approaches trained with a term-and character-level features, is shown to achieve an accuracy of more than 99% in detecting malicious shell commands and files (i.e. binaries).

Blockchain applications that rely on the Proof-of-Work (PoW) have increasingly become energy inefficient with a staggering carbon footprint. In contrast, energy efficient alternative consensus protocols such as Proof-of-Stake (PoS) may cause centralization and unfairness in the blockchain system. To address these challenges, we propose a modular version of PoS-based blockchain systems called e-PoS that resists the centralization of network resources by extending mining opportunities to a wider set of stakeholders. Moreover, e-PoS leverages the in-built system operations to promote fair mining practices by penalizing malicious entities. We validate e-PoS ’s achievable objectives through theoretical analysis and simulations. Our results show that e-PoS ensures fairness and decentralization, and can be applied to existing blockchain applications.

Successful software authorship de-anonymization has both software forensics applications and privacy implications. However, the process requires an efficient extraction of authorship attributes. The extraction of such attributes is very challenging, due to various software code formats from executable binaries with different toolchain provenance to source code with different programming languages. Moreover, the quality of attributes is bounded by the availability of software samples to a certain number of samples per author and a specific size for software samples. To this end, this work proposes a deep Learning-based approach for software authorship attribution, that facilitates large-scale, format-independent, language-oblivious, and obfuscation-resilient software authorship identification. This proposed approach incorporates the process of learning deep authorship attribution using a recurrent neural network, and ensemble random forest classifier for scalability to de-anonymize programmers. Comprehensive experiments are conducted to evaluate the proposed approach over the entire Google Code Jam (GCJ) dataset across all years (from 2008 to 2016) and over real-world code samples from 1,987 public repositories on GitHub. The results of our work show high accuracy despite requiring a smaller number of samples per author. Experimenting with source-code, our approach allows us to identify 8,903 GCJ authors, the largest-scale dataset used by far, with an accuracy of 92.3%. Using the real-world dataset, we achieved an identification accuracy of 94.38% for 745 C programmers on GitHub. Moreover, the proposed approach is resilient to language-specifics, and thus it can identify authors of four programming languages (e.g., C, C++, Java, and Python), and authors writing in mixed languages (e.g., Java/C++, Python/C++). Finally, our system is resistant to sophisticated obfuscation (e.g., using C Tigress) with an accuracy of 93.42% for a set of 120 authors. Experimenting with executable binaries, our approach achieves 95.74% for identifying 1,500 programmers of software binaries. Similar results were obtained when software binaries are generated with different compilation options, optimization levels, and removing of symbol information. Moreover, our approach achieves 93.86% for identifying 1,500 programmers of obfuscated binaries using all features adopted in Obfuscator-LLVM tool.

The Domain Name System Security Extension (DNSSEC) leverages public-key cryptography to provide data integrity, source authentication, and denial of existence for DNS responses. To complement DNSSEC operations, DNSSEC Look-aside Validation (DLV) is designed for alternative off-path validation. Although DNS privacy attracts a lot of attention, the privacy implications of DLV are not fully investigated and understood. In this paper, we take a first in-depth look into DLV, highlighting its lax specifications and privacy implications. By performing extensive experiments over datasets of domain names under comprehensive experimental settings, our findings firmly confirm the privacy leakages caused by DLV. We discover that a large number of domains that should not be sent to DLV servers are being leaked. We explore the root causes, including the lax specifications of DLV. We also propose two approaches to fix the privacy leakages. Our approaches require trivial modifications to the existing DNS standards, and we demonstrate their cost in terms of latency and communication.

Smartphones have become crucial for our daily life activities and are increasingly loaded with our personal information to perform several sensitive tasks, including, mobile banking and communication, and are used for storing private photos and files. Therefore, there is a high demand for applying usable authentication techniques that prevent unauthorized access to sensitive information. In this article, we propose AUTo Sen , a deep-learning-based active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user. Unlike conventional approaches, AUTo Sen is based on deep learning to identify user distinct behavior from the embedded sensors with and without the user’s interaction with the smartphone. We investigate different deep learning architectures in modeling and capturing users’ behavioral patterns for the purpose of authentication. Moreover, we explore the sufficiency of sensory data required to accurately authenticate users. We evaluate AUTo Sen on a real-world data set that includes sensors data of 84 participants’ smartphones collected using our designed data-collection application. The experiments show that AUTo Sen operates accurately using readings of only three sensors (accelerometer, gyroscope, and magnetometer) with a high authentication frequency, e.g., one authentication attempt every 0.5 s. Using sensory data of one second enables an authentication F1-score of approximately 98%, false acceptance rate (FAR) of 0.95%, false rejection rate (FRR) of 6.67%, and equal error rate (EER) of 0.41%. While using sensory data of half a second enables an authentication F1-score of 97.52%, FAR of 0.96%, FRR of 8.08%, and EER of 0.09%. Moreover, we investigate the effects of using different sensory data at variable sampling periods on the performance of the authentication models under various settings and learning architectures.

In this paper, we introduce a powerful hardware-based rogue access point (PrAP), which can relay back and forth traffic between a legitimate AP and a wireless station, and act as a man-in-the-middle attacker. Our PrAP is built of two dedicated wireless routers interconnected physically, and can relay traffic rapidly between a station and a legitimate AP. Through experiments, we demonstrate that the state-of-the-art time-based rogue AP (rAP) detectors cannot detect our PrAP, although perhaps effective against software-based rAP. In demonstrating that, we unveil new insight into fundamentals of time-based detectors for software-based rAPs and their operation: such techniques are only capable of detecting rAPs due to the speed of wireless AP bridging. To address the threat of such PrAPs, we propose a new tool for network administrators, a PrAP-Hunter based on intentional channel interference. Our PrAP-Hunter is highly accurate, even under heavy traffic scenarios. Using a high-performance (desktop) and low-performance (mobile phone) experimental setups of our PrAP-Hunter in various deployment scenarios, we demonstrate close to 100 percent of detection rate, compared to 60 percent detection rate by the state-of-the-art. We show that our PrAP-Hunter is fast (takes 5-10 seconds), does not require any prior knowledge, and can be deployed in the wild by real-world experiments at 10 coffee shops.

Recently, the Blockchain-based cryptocurrency market witnessed enormous growth. Bitcoin, the leading cryptocurrency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this article, we study Bitcoin and Ethereum and explore features in their network that explain their price hikes. We gather data and analyze user and network activity that highly impact the price of these cryptocurrencies. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that help us to determine the demand and supply dynamics in a cryptocurrency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Based on our experimental results using two large datasets for validation, we confirm that our approach provides an accuracy of up to 99% for Bitcoin and Ethereum price prediction in both instances.

In this paper, we systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains. Towards this goal, we attribute attack viability in the attack surface to 1) the Blockchain cryptographic constructs, 2) the distributed architecture of the systems using Blockchain, and 3) the Blockchain application context. To each of those contributing factors, we outline several attacks, including selfish mining, the 51% attack, DNS attacks, distributed denial-of-service (DDoS) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), Blockchain forks, orphaned and stale blocks, block ingestion, wallet thefts, smart contract attacks, and privacy attacks. We also explore the causal relationships between these attacks to demonstrate how various attack vectors are connected to one another. A secondary contribution of this work is outlining effective defense measures taken by the Blockchain technology or proposed by researchers to mitigate the effects of these attacks and patch associated vulnerabilities.

Proxy servers act as an intermediary and a gateway between users and other servers on the Internet, and have many beneficial applications targeting the privacy of users, including bypassing server-side blocking, regional restrictions, etc. Despite the beneficial applications of proxies, they are also used by adversaries to hide their identity and to launch many attacks. As such, many websites restrict access from proxies, resulting in blacklists to filter out those proxies and to aid in their blocking. In this work, we explore the ecosystem of proxies by understanding their affinities and distributions comparatively. We compare residential and open proxies in various ways, including country-level and city-level analyses to highlight their geospatial distributions, similarities, and differences against a large number of blacklists and categories therein, i.e., spam and maliciousness analysis, to understand their characteristics and attributes. We conclude that, while aiming to achieve the same goal, residential and open proxies still have distinct characteristics warranting considering them separately for the role they play in the larger Internet ecosystem. Moreover, we highlight the correlation of proxy locality distribution and five country-level characteristics, such as their Internet censorship, political stability, and Gross Domestic Product (GDP).

The centralized zone data service (CZDS) was introduced by the Internet Corporation for Assigned Names and Numbers (ICANN) to facilitate sharing and access to zone data of the new generic Top-Level Domains (gTLDs). CZDS aims to improve the security and transparency of the naming system of the Internet. In this paper, we investigate CZDS’s transparency by measurement and evaluation. By requesting access to zone data of all gTLDs listed in the CZDS portal, we analyze various aspects of CZDS, including access status, responsiveness and provided reasons for granting access or denial. Among other findings, we find that while a large percent of the gTLD admins respond within a reasonable time, more than 10% of them have a long request-to-decision waiting time, and sometimes requests go unanswered even after six months of a request. Furthermore, we find that denial cases were for unjustified reasons, where administrators who denied the requests have asked for information that was already provided in the request form. We discuss implications, and how to enforce better outcomes of CZDS using insight from our measurement and evaluation.

The steady growth in the number of deployed Internet of Things (IoT) devices has been paralleled with an equal growth in the number of malicious software (malware) targeting those devices. In this paper, we build a detection mechanism of IoT malware utilizing control flow graphs (CFGs). To motivate for our detection mechanism, we contrast the underlying characteristics of IoT malware to other types of malware—Android malware, which are also Linux-based—across multiple features. The preliminary analyses reveal that the Android malware have high density, strong closeness and betweenness, and a larger number of nodes. We show that IoT malware samples have a large number of edges despite a smaller number of nodes, which demonstrate a richer flow structure and higher complexity. We utilize those various characterizing features as a modality to build a highly effective deep learning-based detection model to detect IoT malware. To test our model, we use CFGs of about 6000 malware and benign IoT disassembled samples, and show a detection accuracy of $\approx 99.66$ %.

The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the front-line of malicious attacks caused by malicious software. Machine learning (ML) algorithms, alongside the traditional signature-based methods, are typically used to detect malicious activities and behaviors. However, they are susceptible to malware evolution and sophistication, making them limited to the patterns that they have been trained upon. In this work, we systematically examine the state-of-the-art malware detection approaches using various representations, under a range of adversarial settings. Our preliminary analyses highlight the instability of the learning algorithms in learning patterns that distinguish the benign from the malicious. Our mutations with functionality-preserving operations, e.g., software stripping and binary padding, significantly deteriorate the accuracy of malware detectors.

Social media has become an essential part of the daily routines of children and adolescents. Moreover, enormous efforts have been made to ensure the psychological and emotional well-being of young users as well as their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records and studied the presence of five age-inappropriate categories and the amount of exposure to each category. Using natural language processing and machine learning techniques, we constructed ensemble classifiers that achieved high accuracy in detecting inappropriate comments. Our results show a large percentage of worrisome comments with inappropriate content: we found 11% of the comments on children's videos to be toxic, highlighting the importance of monitoring comments, particularly on children's platforms.

Blockchain-based audit systems use “Practical Byzantine Fault Tolerance” (PBFT) consensus protocol which suffers from a high message complexity and low scalability. Alternatives to PBFT have not been tested in blockchain-based audit systems since no blockchain testbed supports the execution and benchmarking of different consensus protocols in a unified testing environment. In this paper, we address this gap by developing a blockchain testbed capable of executing and testing five consensus protocols in a blockchain network; namely PBFT, Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Elapsed Time (PoET), and Clique. We carry out performance evaluation of those consensus algorithms using data from a real-world audit system. Our results show that the Clique protocol is best suited for blockchain-based audit systems, based on scalability features.

Deep learning algorithms have been widely used for security applications, including malware detection and classification. Recent results have shown that those algorithms are vulnerable to adversarial examples, whereby a small perturbation in the input sample may result in misclassification. In this paper, we systematically tackle the problem of adversarial examples detection in the control flow graph (CFG) based classifiers for malware detection using Soteria. Unique to Soteria, we use both density-based and level-based labels for CFG labeling to yield a consistent representation, a random walk-based traversal approach for feature extraction, and n-gram based module for feature representation. End-to-end, Soteria’s representation ensures a simple yet powerful randomization property of the used classification features, making it difficult even for a powerful adversary to launch a successful attack. Soteria also employs a deep learning approach, consisting of an auto-encoder for detecting adversarial examples, and a CNN architecture for detecting and classifying malware samples. We evaluate the performance of Soteria, using a large dataset consisting of 16,814 IoT samples, and demonstrate its superiority in comparison with state-of-the-art approaches. In particular, Soteria yields an accuracy rate of 97.79% for detecting AEs, and 99.91% overall accuracy for classification malware families.

The state-of-the-art of adversarial machine learning on malware detection systems generally yield unexecutable samples. In this work, we make the case for understanding the robustness of visualization-based malware detection system against adversarial examples (AEs) that not only are able to fool models, but also maintain the executability of the original input. To motivate for our vision, we first investigate the application of existing off-the-shelf adversarial attack approaches on malware detection systems through which we found that those approaches do not necessarily maintain the functionality of the original inputs. Then, we discuss an approach for achieving a high misclassification rate and maintaining the executability and functionality of the original input. We use visualization-based malware detection as an example to highlight the gap between blue-sky research that focuses on aspect of the learning process, and call for more practical techniques that respect the semantics of the underlying applications.

The Onion Router (Tor) is designed to support an anonymous communication through end-to-end encryption. To prevent vulnerability of side channel attacks (e.g. website fingerprinting), dummy packet injection modules have been embedded in Tor to conceal trace patterns that are associated with the individual websites. However, recent study shows that current Website Fingerprinting (WF) defenses still generate patterns that may be captured and recognized by the deep learning technology. In this paper, we conduct in-depth analyses of two state-of-the-art WF defense approaches. Then, based on our new observations and insights, we propose a novel defense mechanism using a per-burst injection technique, called Deep Fingerprinting Defender (DFD), against deep learning-based WF attacks. The DFD has two operation modes, one-way and two-way injection. DFD is designed to break the inherent patterns preserved in Tor user’s traces by carefully injecting dummy packets within every burst. We conducted extensive experiments to evaluate the performance of DFD over both closed-world and open-world settings. Our results demonstrate that these two configurations can successfully break the Tor network traffic pattern and achieve a high evasion rate of 86.02% over one-way client-side injection rate of 100%, a promising improvement in comparison with state-of-the-art adversarial trace’s evasion rate of 60%. Moreover, DFD outperforms the state-of-the-art alternatives by requiring lower bandwidth overhead; 14.26% using client-side injection.

Enterprises are increasingly considering security as an added cost, making it necessary for those enterprises to see a tangible incentive in adopting security measures. Despite data breach laws, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramifications of security breaches and vulnerabilities. In this paper, we study the hidden cost of software vulnerabilities reported in the National Vulnerability Database (NVD) through stock price analysis. We perform a high-fidelity data augmentation to ensure data reliability and to estimate vulnerability disclosure dates as a baseline for estimating the implication of software vulnerabilities. We further build a model for stock price prediction using the nonlinear autoregressive neural network with exogenous factors (NARX) Neural Network model to estimate the effect of vulnerability disclosure on the stock price. Compared to prior work, which relies on linear regression models, our approach is shown to provide better prediction performance. Our analysis also shows that the effect of vulnerabilities on vendors varies, and greatly depends on the specific software industry. Whereas some industries are shown statistically to be affected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not affected at all.

Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point-of-entry and continuously through the usage session. This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods (28 studies), gait-based methods (19 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (34 studies). The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones' embedded sensors, including insights and open challenges for adoption, usability, and performance., 19 pages

DDoS attacks are an immense threat to online services, and numerous studies have been done to detect and defend against them. DDoS attacks, however, are becoming more sophisticated and launched with different purposes, making the detection and instant defense as important as analyzing the behavior of the attack during and after it takes place. Studying and modeling the Spatio-temporal evolvement of DDoS attacks is essential to predict, assess, and combat the problem, since recent studies have shown the emergence of wider and more powerful adversaries. This work aims to model seven Spatio-temporal behavioral characteristics of DDoS attacks, including the attack magnitude, the adversaries’ botnet information, and the attack’s source locality down to the organization. We leverage four state-of-the-art deep learning methods to construct an ensemble of models to capture and predict behavioral patterns of the attack. The proposed ensemble operates in two frequencies, hourly and daily, to actively model and predict the attack behavior and evolvement, and oversee the effect of implementing a defense mechanism.

The outbreak of COVID-19 pandemic raised health and economic concerns. With social distancing along with other measures that are enforced in an attempt to limit the spread of the virus, our life has dramatically changed. During this period, the web and social media platforms have become the main medium for communication, expression, and entertainment. Such platforms are a rich source of information, enabling researchers to better understand how the pandemic affected the users’ everyday life, including interaction with and perception of different topics. In this study, we focus on understanding the shift in the behavior of Twitter users, a major social media platform used by millions daily to share thoughts and discussions. In particular, we collected 26 million tweets for a period of seven months, three months before the pandemic outbreak, and four months after. Using topic modeling and state-of-the-art deep learning techniques, the trending topics within the tweets on monthly-bases, including their sentiment and user’s perception, were analyzed. This study highlights the change of the public behavior and concerns during the pandemic. Users expressed their concerns on health services, with an increase of 59.24% in engagement, and economical effects of the pandemic (34.43% increase). Topics such as online shopping have had a remarkable increase in popularity, perhaps due to the social distancing, while crime and sports topics witnessed a decrease. Overall, various topics related to COVID-19 have witnessed an improved sentiment, alluding to users adoption to the pandemic and associated topics of the public discourse.

We present a new Personal Identification Number (PIN) entry method for smartphones that can be used in security-critical applications, such as smartphone banking. The proposed “Two-Thumbs-Up” (TTU) scheme is resilient against observation attacks such as shoulder-surfing and camera recording, and guides users to protect their PIN information from eavesdropping by shielding the challenge area on the touch screen. To demonstrate the feasibility of TTU, we conducted a user study for TTU, and compared it with existing authentication methods (Normal PIN, Black and White PIN, and ColorPIN) in terms of usability and security. The study results demonstrate that TTU is more secure than other PIN entry methods in the presence of an observer recording multiple authentication sessions.

In the near future, mobile payment systems based on smartphones are expected to be widely applied in various environments, including transit services. When passengers use mass transit, their private information, such as their identity and route, may be made available to some related organizations, such as transit agencies, financial institutions, mobile carriers, and providers of smart cards, among others, even when the passengers may not want their information to be revealed. To protect passenger privacy, this paper proposes a privacy-preserving transit payment system based on traceable signatures, identity-based signatures, and anonymous signatures. In addition to passenger privacy, the proposed system facilitates the proactive blocking of misbehaving passengers, free-transfer services (or transfer discount), and postpaid programs. We demonstrate that the performance of this system is good enough for immediate deployment based on various experiments.

With the rapid development of Software-Defined Networking (SDN) advocating a centralized view of networks, efficient and reliable Distributed Denial of Service (DDoS) defenses are necessary to protect the centralized SDN controller. In this work, we explore the robustness of DL-based DDoS defenses in SDN against adversarial learning attacks. First, we investigate generic off-the-shelf adversarial attacks to test the robustness of DDoS defenses in SDN. Then, we propose Flow-Merge for realistic adversarial flows while achieving a high evasion rate. The evaluation shows that the proposed Flow-Merge is able to force the DL-based DDoS defenses to misclassify 100% of benign flows as malicious.

The lack of security measures among the Internet of Things (IoT) devices and their persistent online connection give adversaries a prime opportunity to target them or even abuse them as intermediary targets in larger attacks such as distributed denial-of-service (DDoS) campaigns. In this paper, we analyze IoT malware and focus on the endpoints reachable on the public Internet, and play an essential part in the IoT malware ecosystem. Namely, we analyze endpoints acting as dropzones and their targets to gain insights into the underlying dynamics in this ecosystem, such as the affinity between the dropzones and their target IP addresses, and the different patterns among endpoints. Towards this goal, we reverse-engineer 2,423 IoT malware samples and extract strings from them to obtain IP addresses. We further gather information about these endpoints from public Internet-wide scanners, such as Shodan and Censys. For the masked IP addresses, we examine the Classless Inter-Domain Routing (CIDR) networks accumulating to more than 100 million (≈78.2% of total active public IPv4 addresses) endpoints.

With the rapid development of Software-Defined Networking (SDN) advocating a centralized view of networks, efficient and reliable Distributed Denial of Service (DDoS) defenses are necessary to protect the centralized SDN controller. Recently, an amalgamation of work has realized such defenses using Deep Learning (DL) based algorithms. Although DL-based algorithms are generally prone to adversarial learning attacks, the extent to which those attacks are applicable to DDoS defenses in SDN is unexamined. In this work, we explore the robustness of DL-based DDoS defenses in SDN against adversarial learning attacks. First, we investigate generic off-the-shelf adversarial attacks to test the robustness of DDoS defenses in SDN, and demonstrate that while they lead to misclassification, these attacks do not preserve the characteristics of flows. As a result, we propose Flow-Merge for realistic adversarial flows while achieving a high evasion rate, with both targeted and untargeted misclassification attacks. The proposed Flow-Merge is able to force the DL-based DDoS defenses to misclassify 100% of benign flows as malicious, while preserving original characteristics of flows. Using state-of-the-art defenses, we show that the adversarial flows generated using Flow-Merge are difficult to detect, with only 49.31% detection rate when using adversarial training.

As the number of wireless device deployments grows, it is desirable to share the highly limited wireless bandwidth efficiently and cooperatively. In WLAN, using a central controller for resource sharing and management is a common practice in mid-size and large-size network. However, in case of small businesses (i.e., restaurants, coffee shops, etc.), business owners cannot afford to obtain the controller. To realize the bandwidth sharing among Access Points (AP) in a distribute manner, seamless handoff of mobile devices (i.e., smartphones and tablets) between small-business owned Access Points (APs) via association control and maintain stable TCP connection are essential but quite challenging. This poster proposes a novel way to cooperatively share wireless resources among the APs. This includes an efficient association control between stations and APs, dedicated virtual access point per station, and tunneling support maintaining existing TCP connection after relocation to another AP.

The lack of security measures in the Internet of Things (IoT) devices and their persistent online connectivity give adversaries an opportunity to target them or abuse them as intermediary targets for volumetric attacks such as Distributed Denial-of-Service (DDoS) campaigns. In this paper, we analyze IoT malware with a focus on endpoints to understand the affinity between the dropzones and their target IP addresses, and to understand the different patterns among them. Towards this goal, we reverse-engineer 2,423 IoT malware samples to obtain IP addresses. We further augment additional information about the endpoints from Internet-wide scanners, including Shodan and Censys. We then perform a deep data-driven analysis of the dropzones and their target IP addresses and further examine the attack surface of the target device space.

Internet of Things (IoT) has become widely adopted in many fields, including industry, social networks, health care, and smart homes, connecting billions of IoT devices through the internet. Understanding and studying IoT malware through analysis using various approaches, such as Control Flow Graph (CFG)-based features and then applying deep learning detection, are widely explored. In this study, we investigate the robustness of such models against adversarial attacks. Our approach crafts the adversarial IoT software using the Subgraph Embedding and Augmentation (SGEA) method that reduces the embedded size required to cause misclassification. Intensive experiments are conducted to evaluate the performance of the proposed method. We observed that SGEA approach is able to misclassify all IoT malware samples as benign by embedding an average size of 6.8 nodes. This highlights that the current detection systems are prone to adversarial examples attacks; thus, there is a need to build more robust systems to detect such manipulated features generated by adversarial examples.

A digital legal seal device according to an embodiment includes a sensor part including a plurality of sensors for scanning a two-dimensional barcode; a processing part for generating a hash-based message authentication code from barcode data obtained by scanning the two-dimensional barcode through the plurality of sensors; and a display part for displaying a part of the hash-based message authentication code generated from the barcode data. So, it is possible to protect messages from hackers.

Juels와 Sudan의 퍼지 볼트 기법은 기법이 갖는 오류 내성 때문에 많은 연구에 사용 되어오고 있다. 그러나 이들의 퍼지 볼트 기법은 그들의 논문에서 영화 애호가 문제를 예를 들었음에도 불구하고, 사람들이 일반적으로 갖는 선호도(preference)의 차이에 대한 고려가 존재하지 않는다. 한편, Nyang과 Lee는 안전하고 성능이 좋은 얼굴인증 시스템을 만들기 위해서, 얼굴 특징이 서로 다른 가중치를 갖도록 얼굴 특징과 퍼지 볼트(vault) 사이에 특별한 연관 구조를 갖는 얼굴 인증 시스템(이른바, 퍼지 얼굴 볼트)을 소개하였다. 그러나 그들의 기법은 일반적인 특징 추출 기법들이 클래스 내부/간 차이를 최적화하려는 특성이 있기 때문에 인증 실패율을 성공적으로 낮추지 못할 것으로 쉽게 예상할 수 있다. 이 논문에서는 퍼지 볼트의 유연성을 제공해주기 위하여 Nyang과 Lee의 퍼지 볼트기반의 얼굴 인증 시스템에서 가중치 아이디어를 다른 방식으로 구현한 버킷(bucket) 구조와 사용자 선호도와 시스템 구현 간 관계를 공식화하는 세 가지 분포 함수에 대해서 소개한다. 또한 이를 바탕으로 선호도 매치메이커(preference matchmaker) 기법을 제안하며, 영화 데이터베이스를 이용하여 이러한 매치메이커의 연산 성능을 확인해본다.

As ransomware attacks have been prevalent, it becomes crucial to make anti-ransomware solutions that defend against ransomwares. In this article, we propose a new ransomware defense system, called SSD-Insider++ , which prevents users’ files from being damaged by ransomware attacks. SSD-Insider++ is embedded into an SSD controller as a form of firmware. By being separated from a host machine, it not only provides more robust data protection than software-based ones which are vulnerable to evasion attacks, but also offers interoperability with various platforms. SSD-Insider++ is composed of two novel features, ransomware detection and perfect data recovery, which are tightly integrated with each other. The detection algorithm observes I/O patterns of a host system and decides whether the host is being attacked by ransomwares in an early stage. Once an encryption attack is detected, the recovery algorithm is triggered to recover original files by leveraging a delayed deletion feature of an SSD at a low cost. Our experimental results show that SSD-Insider++ achieves high accuracy of detecting ransomwares with 0 percent FRR/FAR in most cases and provides an instant data recovery with 0 percent data loss. The overhead of running SSD-Insider++ is negligible – only 80 $n$ n s and 226 $n$ n s are spent more for handling 4-KB reads and writes, respectively.

Efficient extraction of code authorship attributes is key for successful identification. However, the extraction of such attributes is very challenging, due to various programming language specifics, the limited number of available code samples per author, and the average code lines per file, among others. To this end, this work proposes a Deep Learning-based Code Authorship Identification System (DL-CAIS) for code authorship attribution that facilitates large-scale, language-oblivious, and obfuscation-resilient code authorship identification. The deep learning architecture adopted in this work includes TF-IDF-based deep representation using multiple Recurrent Neural Network (RNN) layers and fully-connected layers dedicated to authorship attribution learning. The deep representation then feeds into a random forest classifier for scalability to de-anonymize the author. Comprehensive experiments are conducted to evaluate DL-CAIS over the entire Google Code Jam (GCJ) dataset across all years (from 2008 to 2016) and over real-world code samples from 1987 public repositories on GitHub. The results of our work show the high accuracy despite requiring a smaller number of files per author. Namely, we achieve an accuracy of 96% when experimenting with 1,600 authors for GCJ, and 94.38% for the real-world dataset for 745 C programmers. Our system also allows us to identify 8,903 authors, the largest-scale dataset used by far, with an accuracy of 92.3%. Moreover, our technique is resilient to language-specifics, and thus it can identify authors of four programming languages (e.g. C, C++, Java, and Python), and authors writing in mixed languages (e.g. Java/C++, Python/C++). Finally, our system is resistant to sophisticated obfuscation (e.g. using C Tigress) with an accuracy of 93.42% for a set of 120 authors.

Distributed Denial-of-Service (DDoS) is a big threat to the security and stability of Internet-based services today. Among the recent advanced application-layer DDoS attacks, the Very Short Intermittent DDoS (VSI-DDoS) is the attack, which can bypass existing detection systems and significantly degrade the QoS experienced by users of web services. However, in order for the VSI-DDoS attack to work effectively, bots participating in the attack should be tightly synchronized, an assumption that is difficult to be met in reality. In this paper, we conducted a quantitative analysis to understand how a minimal deviation from perfect synchronization in botnets affects the performance and effectiveness of the VSI-DDoS attack. We found that VSI-DDoS became substantially less effective. That is, it lost 85.7% in terms of effectiveness under about 90ms synchronization inaccuracy, which is a very small inaccuracy under normal network conditions.

Ransomware is a malware that encrypts victim's data, where the decryption key is released after a ransom is paid by the data owner to the attacker. Many ransomware attacks were reported recently, making anti-ransomware a crucial need in security operation, and an issue for the security community to tackle. In this paper, we propose a new approach to defending against ransomware inside NAND flash-based SSDs. To realize the idea of defense-inside-SSDs, both a lightweight detection technique and a perfect recovery algorithm to be used as a part of SSDs firmware should be developed. To this end, we propose a new set of lightweight behavioral features on ran-somware's overwriting pattern, which are invariant across various ransomwares. Our features rely on observing the block I/O request headers only, and not the payload. For perfect and instant recovery, we also propose using the delayed deletion feature of SSDs, which is intrinsic to NAND flash. To demonstrate their feasibility, we implement our algorithms atop an open-channel SSD as a working prototype called SSD-Insider. In experiments using eight real-world and two in-house ransomwares with various background applications running, SSD-Insider achieved a detection accuracy 0% FRR/FAR in most scenarios, and only 5% FAR when heavy overwriting resembling ransomware's data wiping occurs. SSD-Insider detects ransomware activity within 10s, and recovers instantly an infected SSD within 1s with 0% data loss. The additional software overheads incurred by the SSD-Insider is just 147 ns and 254 ns for 4-KB reads and writes, respectively, which is negligible considering NAND chip latency (50-1000 µs).

Vulnerabilities have a detrimental effect on end-users and enterprises, both direct and indirect; including loss of private data, intellectual property, the competitive edge, performance, etc. Despite the growing software industry and a push towards a digital economy, enterprises are increasingly considering security as an added cost, which makes it necessary for those enterprises to see a tangible incentive in adopting security. Furthermore, despite data breach laws that are in place, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramifications of security breaches and vulnerabilities.

The controllable linkability of group signatures introduced by Hwang et al. enables an entity who has a linking key to find whether or not two group signatures were generated by the same signer, while preserving the anonymity. This functionality is very useful in many applications that require the linkability but still need the anonymity, such as sybil attack detection in a vehicular ad hoc network and privacy-preserving data mining. In this paper, we present a new group signature scheme supporting the controllable linkability. The major advantage of this scheme is that the signature length is very short, even shorter than this in the best-known group signature scheme without supporting the linkability. We have implemented our scheme in both a Linux machine with an Intel Core2 Quad and an iPhone4. We compare the results with a number of existing group signature schemes. We also prove security features of our scheme, such as anonymity, traceability, nonframeability, and linkability, under a random oracle model.

Widespread use of smart devices accompanies increase of use of access point (AP), which enables the connection to the wireless network. If the appropriate security is not served when a user tries to connect the wireless network through an AP, various security problems can arise due to the rogue APs. In this paper, we are going to examine the threat by evil-twin, which is a kind of rogue APs. Most of recent researches for detecting rogue APs utilize the measured time difference, such as round trip time (RTT), between the evil-twin and authorized APs. These methods, however, suffer from the low detection rate in the network congestion. Due to these reasons, in this paper, we suggest a new factor, packet inter-arrival time (PIAT), in order to detect evil-twins. By using both RTT and PIAT as the learning factors for the support vector machine (SVM), we determine the non-linear metric to classify evil-twins and authorized APs. As a result, we can detect evil-twins with the probability of up to 96.5% and at least 89.75% even when the network is congested.

In this paper, we introduce a transaction authentication solution that provides compatibility with any banking transactions. Our solution is based on a novel visual cryptographic scheme that supports multiple uses of a single static share, unlike existing techniques in the literature of visual cryptography. To support multiple uses, we utilize complementary colors in the subtractive color mixing model. In our solution, a user is asked to overlap an issued visual cryptography card on an encrypted image that is rendered on a screen to check transaction information and transaction authentication number. We analyze the security of our scheme against various attacks and show its effectiveness using a usability study.

The nefarious practice of Typosquatting involves deliberately registering Internet domain names containing typographical errors that primarily target popular domain names, in an effort to redirect users to unintended destinations or stealing traffic for monetary gain. Typosquatting has existed for well over two decades and continues to be a credible threat to this day. As recently shown in the online magazine Slate.com [19], cybercriminals have attempted to distribute malware through Netflix.om, a typosquatted variant of the popular streaming site Netflix.com that uses the country code top-level domain (ccTLD) for Oman (.om). While much of the prior work has examined various typosquatting techniques and how they change over time, none have considered how effective they are in deceiving users. In this paper, we attempt to fill in this gap by conducting a user study that exposes subjects to several uniform resource locators (URLs) in an attempt to determine the effectiveness of several typosquatting techniques that are prevalent in the wild. We also attempt to determine if the security education and awareness of cybercrimes such as typosquatting will affect the behavior of Internet users. Ultimately, we found that subjects tend to correctly identify typosquatting which adds characters to the domain names, while the most effective techniques to deceive users involves permutations and substitutions of characters. We also found that subjects generally performed better and faster at identifying typosquatted domain names after being thoroughly educated about them, and that certain attributes such as Age and Education affect their behavior when exposed to them.

This paper introduces a new surveillance platform which is equipped with multiple parallel deep learning frameworks. The deep learning frameworks are used for the face recognition of input image and video streams from CCTV cameras in security applications. Each deep learning framework has its own accuracy (related to recognition performance) and operation time (related to system stability) those are in tradeoff relationship. Based on this system architecture, a new dynamic control algorithm which selects one deep learning framework for time-average security-level (i.e., machine learning accuracy for recognition and classification) maximization under the consideration of system stability. The performance of the proposed algorithm was evaluated and also verified that it achieves desired performance.

DNSSEC Look-aside Validation (DLV) is examined, highlighting its lax specifications and privacy implications. By performing extensive experiments over datasets of domain names under comprehensive experimental settings, our findings firmly confirm the privacy leakages caused by DLV. We discover that a large number of domains that should not be sent to DLV servers are being leaked. We explore the root causes, and propose two approaches to fix the privacy leakages.

In this work, we propose an SDN-based WLAN monitoring and management framework called RFlow+ and devise a two-level counting mechanism, namely a distributed local counter (on-site and real-time) and a central collector (a summation of local counters). Building on that, we proposed a highly scalable monitoring and management framework to handle immediate actions based on short-term (e.g., 50 ms) monitoring and eventual actions based on long-term (e.g., 1 month) monitoring. The former uses the local view of each access point (AP), and the latter uses the global view of the collector.