arXiv subjects

Aziz Mohaisen

Publications and source records attributed to Aziz Mohaisen.

At least 19 recordsLinked to original sources

arXiv2020

Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms

This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.

cs.LG↗

arXiv2019

Phoenix: Towards Persistently Secure, Recoverable, and NVM Friendly Tree of Counters

Emerging Non-Volatile Memories (NVMs) bring a unique challenge to the security community, namely persistent security. As NVM-based memories are expected to restore their data after recovery, the security metadata must be recovered as well. However, persisting all affected security metadata on each memory write would significantly degrade performance and exacerbate the write endurance problem. Moreover, recovery time can increase significantly (up to hours for practical memory sizes) when security metadata are not updated strictly. Counter trees are used in state-of-the-art commercial secure processors, e.g., Intel's Safe Guard Extension (SGX). Counter trees have a unique challenge due to the inability to recover the whole tree from leaves. Thus, to ensure recoverability, all updates to the tree must be persisted, which can be tens of additional writes on each write. The state-of-art scheme, Anubis, enables recoverability but incurs an additional write per cache eviction, i.e., reduces lifetime to approximately half. Additionally, Anubis degrades performance significantly in many cases. In this paper, we propose Phoenix, a practical novel scheme which relies on elegantly reproducing the cache content before a crash, however with minimal overheads. Our evaluation results show that Phoenix reduces persisting security metadata overhead writes from 87\% extra writes (for Anubis) to less than write-back compared to an encrypted system without recovery, thus improving the NVM lifetime by 2x. Overall Phoenix performance is better than the baseline, unlike Anubis which adds 7.9\% (max of 35\%) performance overhead.

cs.CR↗

arXiv2019

You Can Run, But You Cannot Hide: Using Elevation Profiles to Breach Location Privacy through Trajectory Prediction

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Strava and Runkeeper, utilize information for activity tracking, and have recently witnessed a boom in popularity. Those trackers have their own web platforms, and allow users to share activities on such platforms, or even with other social network platforms. To preserve privacy of users while allowing sharing, those platforms allow users to disclose partial information, such as the elevation profile for an activity, which supposedly will not leak the location trajectory. In this work we examine the extent to which publicly available elevation profiles can be used to predict the location trajectory of users. To tackle this problem, we devise three threat settings under which the city, borough, or even a route can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning- and deep learning-based techniques, and achieve a prediction success rate ranging from 59.59% to 95.83%. The findings are alarming, and highlight that sharing information such as elevation profile may have significant privacy risks.

cs.CR↗

arXiv2019

W-Net: A CNN-based Architecture for White Blood Cells Image Classification

Computer-aided methods for analyzing white blood cells (WBC) have become widely popular due to the complexity of the manual process. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge and highly demanded as the distribution of the five types reflects on the condition of the immune system. This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset, obtained from The Catholic University of Korea, that includes 6,562 real images of the five WBC types. W-Net achieves an average accuracy of 97%.

eess.IV↗

arXiv2019

COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection

Despite many attempts, the state-of-the-art of adversarial machine learning on malware detection systems generally yield unexecutable samples. In this work, we set out to examine the robustness of visualization-based malware detection system against adversarial examples (AEs) that not only are able to fool the model, but also maintain the executability of the original input. As such, 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. Therefore, we proposed an approach to generate adversarial examples, COPYCAT, which is specifically designed for malware detection systems considering two main goals; achieving a high misclassification rate and maintaining the executability and functionality of the original input. We designed two main configurations for COPYCAT, namely AE padding and sample injection. While the first configuration results in untargeted misclassification attacks, the sample injection configuration is able to force the model to generate a targeted output, which is highly desirable in the malware attribution setting. We evaluate the performance of COPYCAT through an extensive set of experiments on two malware datasets, and report that we were able to generate adversarial samples that are misclassified at a rate of 98.9% and 96.5% with Windows and IoT binary datasets, respectively, outperforming the misclassification rates in the literature. Most importantly, we report that those AEs were executable unlike AEs generated by off-the-shelf approaches. Our transferability study demonstrates that the generated AEs through our proposed method can be generalized to other models.

cs.CR↗

arXiv2019

A Privacy-Preserving Longevity Study of Tor's Hidden Services

Tor and hidden services have emerged as a practical solution to protect user privacy against tracking and censorship. At the same time, little is known about the lifetime and nature of hidden services. Data collection and study of Tor hidden services is challenging due to its nature of providing privacy. Studying the lifetime of hidden services provides several benefits. For example, it allows investigation of the maliciousness of domains based on their lifetime. Short-lived hidden services are more likely not to be legitimate domains, e.g., used by ransomware, as compared to long-lived domains. In this work, we investigate the lifetime of hidden services by collecting data from a small (2%) subset of all Tor HSDir relays in a privacy-preserving manner. Based on the data collected, we devise protocols and extrapolation techniques to infer the lifetime of hidden services. Moreover we show that, due to Tor's specifics, our small subset of HSDir relays is sufficient to extrapolate lifetime with high accuracy, while respecting Tor user and service privacy and following Tor's research safety guidelines. Our results indicate that a large majority of the hidden services have a very short lifetime. In particular, 50% of all current Tor hidden services have an estimate lifetime of only 10 days or less, and 80% have a lifetime of less than a month.

cs.CR↗

arXiv2019

Secure and Transparent Audit Logs with BlockAudit

Audit logs serve as a critical component in enterprise business systems and are used for auditing, storing, and tracking changes made to the data. However, audit logs are vulnerable to a series of attacks enabling adversaries to tamper data and corresponding audit logs without getting detected. Among them, two well-known attacks are "the physical access attack," which exploits root privileges, and "the remote vulnerability attack," which compromises known vulnerabilities in database systems. In this paper, we present BlockAudit: a scalable and tamper-proof system that leverages the design properties of audit logs and security guarantees of blockchain to enable secure and trustworthy audit logs. Towards that, we construct the design schema of BlockAudit and outline its functional and operational procedures. We implement our design on a custom-built Practical Byzantine Fault Tolerance (PBFT) blockchain system and evaluate the performance in terms of latency, network size, payload size, and transaction rate. Our results show that conventional audit logs can seamlessly transition into BlockAudit to achieve higher security and defend against the known attacks on audit logs.

cs.DC↗

arXiv2019

Exploring the Attack Surface of Blockchain: A Systematic Overview

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, Domain Name System (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

cs.CR↗

arXiv2019

Detecting and Classifying Android Malware using Static Analysis along with Creator Information

Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however previous studies overlooked such information as a feature in detecting and classifying malware, and in attributing malware to creators. Guided by this insight, we propose a method to improve on the performance of Android malware detection by incorporating the creator's information as a feature and classify malicious applications into similar groups. We developed a system that implements this method in practice. Our system enables fast detection of malware by using creator information such as serial number of certificate. Additionally, it analyzes malicious be-haviors and permissions to increase detection accuracy. The system also can classify malware based on similarity scoring. Finally, we showed detection and classification performance with 98% and 90% accuracy respectively.

cs.CR↗

arXiv2019

Examining Adversarial Learning against Graph-based IoT Malware Detection Systems

The main goal of this study is to investigate the robustness of graph-based Deep Learning (DL) models used for Internet of Things (IoT) malware classification against Adversarial Learning (AL). We designed two approaches to craft adversarial IoT software, including Off-the-Shelf Adversarial Attack (OSAA) methods, using six different AL attack approaches, and Graph Embedding and Augmentation (GEA). The GEA approach aims to preserve the functionality and practicality of the generated adversarial sample through a careful embedding of a benign sample to a malicious one. Our evaluations demonstrate that OSAAs are able to achieve a misclassification rate (MR) of 100%. Moreover, we observed that the GEA approach is able to misclassify all IoT malware samples as benign.

cs.CR↗

arXiv2019

Analyzing, Comparing, and Detecting Emerging Malware: A Graph-based Approach

The growth in the number of Android and Internet of Things (IoT) devices has witnessed a parallel increase in the number of malicious software (malware), calling for new analysis approaches. We represent binaries using their graph properties of the Control Flow Graph (CFG) structure and conduct an in-depth analysis of malicious graphs extracted from the Android and IoT malware to understand their differences. Using 2,874 and 2,891 malware binaries corresponding to IoT and Android samples, we analyze both general characteristics and graph algorithmic properties. Using the CFG as an abstract structure, we then emphasize various interesting findings, such as the prevalence of unreachable code in Android malware, noted by the multiple components in their CFGs, and larger number of nodes in the Android malware, compared to the IoT malware, highlighting a higher order of complexity. We implement a Machine Learning based classifiers to detect IoT malware from benign ones, and achieved an accuracy of 97.9% using Random Forests (RF).

cs.CR↗

arXiv2019

Scaling Up Anomaly Detection Using In-DRAM Working Set of Active Flows Table

In the zettabyte era, per-flow measurement becomes more challenging owing to the growth of both traffic volumes and the number of flows. Also, swiftness of detection of anomalies (e.g., DDoS attack, congestion, link failure, and so on) becomes paramount. For fast and accurate anomaly detection, managing an accurate working set of active flows (WSAF) from massive volumes of packet influxes at line rates is a key challenge. WSAF is usually located in a very fast but expensive memory, such as TCAM or SRAM, and thus the number of entries to be stored is quite limited. To cope with the scalability issue of WSAF, we propose to use In-DRAM WSAF with scales, and put a compact data structure called FlowRegulator in front of WSAF to compensate for DRAM's slow access time by substantially reducing massive influxes to WSAF without compromising measurement accuracy. We prototype and evaluated our system in a large scale real-world experiment (connected to monitoring port of our campus main gateway router for 113 hours, and capturing 122.3 million flows). As one key application, FlowRegulator detected heavy hitters with 99.8% accuracy.

cs.NI↗

arXiv2019

Analyzing Endpoints in the Internet of Things Malware

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 larger 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.

cs.NI↗

arXiv2019

Exploring Spatial, Temporal, and Logical Attacks on the Bitcoin Network

In this paper, we explore the partitioning attacks on the Bitcoin network, which is shown to exhibit spatial bias, and temporal and logical diversity. Through data-driven study we highlight: 1) the centralization of Bitcoin nodes across autonomous systems, indicating the possibility of BGP attacks, 2)the non-uniform consensus among nodes, that can be exploited to partition the network, and 3)the diversity in the Bitcoin software usage that can lead to privacy attacks. Atop the prior work, which focused on spatial partitioning, our work extends the analysis of the Bitcoin network to understand the temporal and logical effects on the robustness of the Bitcoin network.

cs.NI↗

arXiv2019

Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering

Using runtime execution artifacts to identify malware and its associated family is an established technique in the security domain. Many papers in the literature rely on explicit features derived from network, file system, or registry interaction. While effective, the use of these fine-granularity data points makes these techniques computationally expensive. Moreover, the signatures and heuristics are often circumvented by subsequent malware authors. In this work, we propose Chatter, a system that is concerned only with the order in which high-level system events take place. Individual events are mapped onto an alphabet and execution traces are captured via terse concatenations of those letters. Then, leveraging an analyst labeled corpus of malware, n-gram document classification techniques are applied to produce a classifier predicting malware family. This paper describes that technique and its proof-of-concept evaluation. In its prototype form, only network events are considered and eleven malware families are used. We show the technique achieves 83%-94% accuracy in isolation and makes non-trivial performance improvements when integrated with a baseline classifier of combined order features to reach an accuracy of up to 98.8%.

cs.CR↗

arXiv2018

Countering Selfish Mining in Blockchains

Selfish mining is a well known vulnerability in blockchains exploited by miners to steal block rewards. In this paper, we explore a new form of selfish mining attack that guarantees high rewards with low cost. We show the feasibility of this attack facilitated by recent developments in blockchain technology opening new attack avenues. By outlining the limitations of existing countermeasures, we highlight a need for new defense strategies to counter this attack, and leverage key system parameters in blockchain applications to propose an algorithm that enforces fair mining. We use the expected transaction confirmation height and block publishing height to detect selfish mining behavior and develop a network-wide defense mechanism to disincentivize selfish miners. Our design involves a simple modifications to transactions' data structure in order to obtain a "truth state" used to catch the selfish miners and prevent honest miners from losing block rewards.

cs.CR↗

arXiv2018

Towards Blockchain-Driven, Secure and Transparent Audit Logs

Audit logs serve as a critical component in the enterprise business systems that are used for auditing, storing, and tracking changes made to the data. However, audit logs are vulnerable to a series of attacks, which enable adversaries to tamper data and corresponding audit logs. In this paper, we present BlockAudit: a scalable and tamper-proof system that leverages the design properties of audit logs and security guarantees of blockchains to enable secure and trustworthy audit logs. Towards that, we construct the design schema of BlockAudit, and outline its operational procedures. We implement our design on Hyperledger and evaluate its performance in terms of latency, network size, and payload size. Our results show that conventional audit logs can seamlessly transition into BlockAudit to achieve higher security, integrity, and fault tolerance.

cs.CR↗

arXiv2018

End-to-End Analysis of In-Browser Cryptojacking

In-browser cryptojacking involves hijacking the CPU power of a website's visitor to perform CPU-intensive cryptocurrency mining, and has been on the rise, with 8500% growth during 2017. While some websites advocate cryptojacking as a replacement for online advertisement, web attackers exploit it to generate revenue by embedding malicious cryptojacking code in highly ranked websites. Motivated by the rise of cryptojacking and the lack of any prior systematic work, we set out to analyze malicious cryptojacking statically and dynamically, and examine the economical basis of cryptojacking as an alternative to advertisement. For our static analysis, we perform content-, currency-, and code-based analyses. Through the content-based analysis, we unveil that cryptojacking is a wide-spread threat targeting a variety of website types. Through a currency-based analysis we highlight affinities between mining platforms and currencies: the majority of cryptojacking websites use Coinhive to mine Monero. Through code-based analysis, we highlight unique code complexity features of cryptojacking scripts, and use them to detect cryptojacking code among benign and other malicious JavaScript code, with an accuracy of 96.4%. Through dynamic analysis, we highlight the impact of cryptojacking on system resources, such as CPU and battery consumption (in battery-powered devices); we use the latter to build an analytical model that examines the feasibility of cryptojacking as an alternative to online advertisement, and show a huge negative profit/loss gap, suggesting that the model is impractical. By surveying existing countermeasures and their limitations, we conclude with long-term countermeasures using insights from our analysis.

cs.CR↗