arXiv subjects

Asaf Shabtai

Publications and source records attributed to Asaf Shabtai.

At least 127 records · Page 7Linked to original sources

arXiv2020

GIM: Gaussian Isolation Machines

In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data. Samples from outside the distribution may be classified as an existing class with high probability by softmax-based classifiers; such incorrect classifications affect the performance of the classifiers and the applications/systems that depend on them. Previous research aimed at distinguishing training distribution data from out-of-distribution data (OOD) has proposed detectors that are external to the classification method. We present Gaussian isolation machine (GIM), a novel hybrid (generative-discriminative) classifier aimed at solving the problem arising when OOD data is encountered. The GIM is based on a neural network and utilizes a new loss function that imposes a distribution on each of the trained classes in the neural network's output space, which can be approximated by a Gaussian. The proposed GIM's novelty lies in its discriminative performance and generative capabilities, a combination of characteristics not usually seen in a single classifier. The GIM achieves state-of-the-art classification results on image recognition and sentiment analysis benchmarking datasets and can also deal with OOD inputs.

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arXiv2020

DANTE: A framework for mining and monitoring darknet traffic

Trillions of network packets are sent over the Internet to destinations which do not exist. This 'darknet' traffic captures the activity of botnets and other malicious campaigns aiming to discover and compromise devices around the world. In order to mine threat intelligence from this data, one must be able to handle large streams of logs and represent the traffic patterns in a meaningful way. However, by observing how network ports (services) are used, it is possible to capture the intent of each transmission. In this paper, we present DANTE: a framework and algorithm for mining darknet traffic. DANTE learns the meaning of targeted network ports by applying Word2Vec to observed port sequences. Then, when a host sends a new sequence, DANTE represents the transmission as the average embedding of the ports found that sequence. Finally, DANTE uses a novel and incremental time-series cluster tracking algorithm on observed sequences to detect recurring behaviors and new emerging threats. To evaluate the system, we ran DANTE on a full year of darknet traffic (over three Tera-Bytes) collected by the largest telecommunications provider in Europe, Deutsche Telekom and analyzed the results. DANTE discovered 1,177 new emerging threats and was able to track malicious campaigns over time. We also compared DANTE to the current best approach and found DANTE to be more practical and effective at detecting darknet traffic patterns.

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arXiv2020

Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks

In recent years, a variety of effective neural network-based methods for anomaly and cyber attack detection in industrial control systems (ICSs) have been demonstrated in the literature. Given their successful implementation and widespread use, there is a need to study adversarial attacks on such detection methods to better protect the systems that depend upon them. The extensive research performed on adversarial attacks on image and malware classification has little relevance to the physical system state prediction domain, which most of the ICS attack detection systems belong to. Moreover, such detection systems are typically retrained using new data collected from the monitored system, thus the threat of adversarial data poisoning is significant, however this threat has not yet been addressed by the research community. In this paper, we present the first study focused on poisoning attacks on online-trained autoencoder-based attack detectors. We propose two algorithms for generating poison samples, an interpolation-based algorithm and a back-gradient optimization-based algorithm, which we evaluate on both synthetic and real-world ICS data. We demonstrate that the proposed algorithms can generate poison samples that cause the target attack to go undetected by the autoencoder detector, however the ability to poison the detector is limited to a small set of attack types and magnitudes. When the poison-generating algorithms are applied to the popular SWaT dataset, we show that the autoencoder detector trained on the physical system state data is resilient to poisoning in the face of all ten of the relevant attacks in the dataset. This finding suggests that neural network-based attack detectors used in the cyber-physical domain are more robust to poisoning than in other problem domains, such as malware detection and image processing.

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arXiv2019

Defense Methods Against Adversarial Examples for Recurrent Neural Networks

Adversarial examples are known to mislead deep learning models to incorrectly classify them, even in domains where such models achieve state-of-the-art performance. Until recently, research on both attack and defense methods focused on image recognition, primarily using convolutional neural networks (CNNs). In recent years, adversarial example generation methods for recurrent neural networks (RNNs) have been published, demonstrating that RNN classifiers are also vulnerable to such attacks. In this paper, we present a novel defense method, termed sequence squeezing, to make RNN classifiers more robust against such attacks. Our method differs from previous defense methods which were designed only for non-sequence based models. We also implement four additional RNN defense methods inspired by recently published CNN defense methods. We evaluate our methods against state-of-the-art attacks in the cyber security domain where real adversaries (malware developers) exist, but our methods can be applied against other discrete sequence based adversarial attacks, e.g., in the NLP domain. Using our methods we were able to decrease the effectiveness of such attack from 99.9% to 15%.

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arXiv2019

The Security of IP-based Video Surveillance Systems

IP-based Surveillance systems protect industrial facilities, railways, gas stations, and even one's own home. Therefore, unauthorized access to these systems has serious security implications. In this survey, we analyze the system's (1) threat agents, (2) attack goals, (3) practical attacks, (4) possible attack outcomes, and (5) provide example attack vectors.

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arXiv2019

Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both economics and human life. Hence, these systems have become an attractive target for attacks, both physical and cyber. A number of attack detection methods have been proposed, however they are characterized by a low detection rate, a substantial false positive rate, or are system specific. In this paper, we study an attack detection method based on simple and lightweight neural networks, namely, 1D convolutions and autoencoders. We apply these networks to both the time and frequency domains of the collected data and discuss pros and cons of each approach. We evaluate the suggested method on three popular public datasets and achieve detection rates matching or exceeding previously published detection results, while featuring small footprint, short training and detection times, and generality. We also demonstrate the effectiveness of PCA, which, given proper data preprocessing and feature selection, can provide high attack detection scores in many settings. Finally, we study the proposed method's robustness against adversarial attacks, that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect. Our results show that the proposed method is robust to such evasion attacks: in order to evade detection, the attacker is forced to sacrifice the desired physical impact on the system. This finding suggests that neural networks trained under the constraints of the laws of physics can be trusted more than networks trained under more flexible conditions.

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arXiv2019

When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures

State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation attacks, and despite the plethora of research in this domain, to this day, adversaries still have the upper hand in the cat and mouse game of adversarial example generation methods vs. detection and prevention methods. In this research, we present a novel detection method that uses Shapley Additive Explanations (SHAP) values computed for the internal layers of a DNN classifier to discriminate between normal and adversarial inputs. We evaluate our method by building an extensive dataset of adversarial examples over the popular CIFAR-10 and MNIST datasets, and training a neural network-based detector to distinguish between normal and adversarial inputs. We evaluate our detector against adversarial examples generated by diverse state-of-the-art attacks and demonstrate its high detection accuracy and strong generalization ability to adversarial inputs generated with different attack methods.

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arXiv2019

3D Marketplace: Distributed Attestation of 3D Designs on Blockchain

Additive manufacturing (AM), or 3D printing, is an emerging manufacturing technology that is expected to have far-reaching socioeconomic, environmental, and geopolitical implications. As the use of this technology increases, the need for validation of 3D designs grows. In order to create a marketplace in which 3D designs are traded, it is necessary to develop a platform that enables the attestation of the 3D designs and promotes truthfulness. In this paper, we introduce a novel concept of a distributed marketplace that will support the attestation of 3D printing designs. We build a mathematical trust model that ensures truthfulness among rational, selfish, and independent agents, which is based on a reward/penalty system. The payment for participating in the evaluation is calculated by factoring in agents' reputation and peer feedback. Moreover, we describe the architecture and the implementation of the trust model on blockchain using smart contracts for the creation of a distributed marketplace. Our model relies both on theoretical and practical best practices to create an unique platform that elicit effort and truthfulness from the participants.

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arXiv2019

Challenges for Security Assessment of Enterprises in the IoT Era

For years, attack graphs have been an important tool for security assessment of enterprise networks, but IoT devices, a new player in the IT world, might threat the reliability of this tool. In this paper, we review the challenges that must be addressed when using attack graphs to model and analyze enterprise networks that include IoT devices. In addition, we propose novel ideas and countermeasures aimed at addressing these challenges.

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arXiv2019

Heuristic Approach Towards Countermeasure Selection using Attack Graphs

Selecting the optimal set of countermeasures is a challenging task that involves various considerations and tradeoffs such as prioritizing the risks to mitigate and costs. The vast majority of studies for selecting a countermeasure deployment are based on a limited risk assessment procedure that utilizes the common vulnerability scoring system (CVSS). Such a risk assessment procedure does not necessarily consider the prerequisites and exploitability of a specific asset, cannot distinguish insider from outsider threat actor, and does not express the consequences of exploiting a vulnerability as well as the attacker's lateral movements. Other studies applied a more extensive risk assessment procedure that relies on manual work and repeated assessment. These solutions however, do not consider the network topology and do not specify the optimal position for deploying the countermeasures, and therefore are less practical. In this paper we suggest a heuristic search approach for selecting the optimal countermeasure deployment under a given budget limitation. The proposed method expresses the risk of the system using an extended attack graph modeling, which considers the prerequisites and consequences of exploiting a vulnerability, examines the attacker's potential lateral movements, and express the physical network topology as well as vulnerabilities in network protocols. In addition, unlike previous studies which utilizes attack graph for countermeasure planning, the proposed methods does not require re-generating the attack graph at each stage of the procedure, which is computationally heavy, and therefore it provides a more accurate and practical countermeasure deployment planning process.

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arXiv2019

Extending Attack Graphs to Represent Cyber-Attacks in Communication Protocols and Modern IT Networks

An attack graph is a method used to enumerate the possible paths that an attacker can execute in the organization network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the representation of network protocol vulnerabilities, and thus it cannot be used to model common network attacks such as ARP poisoning, DNS spoofing, and SYN flooding. Second, it does not support advanced types of communication such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this paper, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols (e.g., Bluetooth), (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service, as well as attacks on advanced types of communication. We demonstrate the proposed model on a testbed implementing a simplified network architecture comprised of both IT and industrial components.

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arXiv2019

Evaluating the Information Security Awareness of Smartphone Users

Information security awareness (ISA) is a practice focused on the set of skills, which help a user successfully mitigate a social engineering attack. Previous studies have presented various methods for evaluating the ISA of both PC and mobile users. These methods rely primarily on subjective data sources such as interviews, surveys, and questionnaires that are influenced by human interpretation and sincerity. Furthermore, previous methods for evaluating ISA did not address the differences between classes of social engineering attacks. In this paper, we present a novel framework designed for evaluating the ISA of smartphone users to specific social engineering attack classes. In addition to questionnaires, the proposed framework utilizes objective data sources: a mobile agent and a network traffic monitor; both of which are used to analyze the actual behavior of users. We empirically evaluated the ISA scores assessed from the three data sources (namely, the questionnaires, mobile agent, and network traffic monitor) by conducting a long-term user study involving 162 smartphone users. All participants were exposed to four different security challenges that resemble real-life social engineering attacks. These challenges were used to assess the ability of the proposed framework to derive a relevant ISA score. The results of our experiment show that: (1) the self-reported behavior of the users differs significantly from their actual behavior; and (2) ISA scores derived from data collected by the mobile agent or the network traffic monitor are highly correlated with the users' success in mitigating social engineering attacks.

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arXiv2019

VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks

The purpose of the automatic dependent surveillance broadcast (ADS-B) technology is to serve as a replacement for the current radar-based, air traffic control systems. Despite the considerable time and resources devoted to designing and developing the system, the ADS-B is well known for its lack of security mechanisms. Attempts to address these security vulnerabilities have been made in previous studies by modifying the protocol's current architecture or by using additional hardware components. These solutions, however, are considered impractical because of 1) the complex regulatory process involving avionic systems, 2) the high costs of using hardware components, and 3) the fact that the ADS-B system itself is already deployed in most aircraft and ground stations around the world. In this paper, we propose VizADS-B, an alternative software-based security solution for detecting anomalous ADS-B messages, which does not require any alteration of the current ADS-B architecture or the addition of sensors. According to the proposed method, the information obtained from all aircraft within a specific geographical area is aggregated and represented as a stream of images. Then, a convolutional LSTM encoder-decoder model is used for analyzing and detecting anomalies in the sequences of images. In addition, we propose an explainability technique, designed specifically for convolutional LSTM encoder-decoder models, which is used for providing operative information to the pilot as a visual indicator of a detected anomaly, thus allowing the pilot to make relevant decisions. We evaluated our proposed method on five datasets by injecting and subsequently identifying five different attacks. Our experiments demonstrate that most of the attacks can be detected based on spatio-temporal anomaly detection approach.

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arXiv2019

Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup

Today, telecommunication service providers (telcos) are exposed to cyber-attacks executed by compromised IoT devices connected to their customers' networks. Such attacks might have severe effects not only on the target of attacks but also on the telcos themselves. To mitigate those risks we propose a machine learning based method that can detect devices of specific vulnerable IoT models connected behind a domestic NAT, thereby identifying home networks that pose a risk to the telco's infrastructure and availability of services. As part of the effort to preserve the domestic customers' privacy, our method relies on NetFlow data solely, refraining from inspecting the payload. To promote future research in this domain we share our novel dataset, collected in our lab from numerous and various commercial IoT devices.

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arXiv2019

Deployment Optimization of IoT Devices through Attack Graph Analysis

The Internet of things (IoT) has become an integral part of our life at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity of vendors, and proprietary firmware. Moreover, short range communication protocols (e.g., Bluetooth or ZigBee) open additional opportunities for the lateral movement of an attacker within an organization. Thus, the type and location of IoT devices may significantly change the level of network security of the organizational network. In this paper, we quantify the level of network security based on an augmented attack graph analysis that accounts for the physical location of IoT devices and their communication capabilities. We use the depth-first branch and bound (DFBnB) heuristic search algorithm to solve two optimization problems: Full Deployment with Minimal Risk (FDMR) and Maximal Utility without Risk Deterioration (MURD). An admissible heuristic is proposed to accelerate the search. The proposed method is evaluated using a real network with simulated deployment of IoT devices. The results demonstrate (1) the contribution of the augmented attack graphs to quantifying the impact of IoT devices deployed within the organization on security, and (2) the effectiveness of the optimized IoT deployment.

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arXiv2019

SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps

The evolution of drone technology in the past nine years since the first commercial drone was introduced at CES 2010 has caused many individuals and businesses to adopt drones for various purposes. We are currently living in an era in which drones are being used for pizza delivery, the shipment of goods, and filming, and they are likely to provide an alternative for transportation in the near future. However, drones also pose a significant challenge in terms of security and privacy within society (for both individuals and organizations), and many drone related incidents are reported on a daily basis. These incidents have called attention to the need to detect and disable drones used for malicious purposes and opened up a new area of research and development for academia and industry, with a market that is expected to reach $1.85 billion by 2024. While some of the knowledge used to detect UAVs has been adopted for drone detection, new methods have been suggested by industry and academia alike to deal with the challenges associated with detecting the very small and fast flying objects. In this paper, we describe new societal threats to security and privacy created by drones, and present academic and industrial methods used to detect and disable drones. We review methods targeted at areas that restrict drone flights and analyze their effectiveness with regard to various factors (e.g., weather, birds, ambient light, etc.). We present the challenges arising in areas that allow drone flights, introduce the methods that exist for dealing with these challenges, and discuss the scientific gaps that exist in this area. Finally, we review methods used to disable drones, analyze their effectiveness, and present their expected results. Finally, we suggest future research directions.

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arXiv2019

MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses

Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names through which bots can establish a resilient communication channel with their command and control servers. Recent publications presented deep learning, character-level classifiers that are able to detect algorithmically generated domain (AGD) names with high accuracy, and correspondingly, significantly reduce the effectiveness of DGAs for botnet communication. In this paper we present MaskDGA, a practical adversarial learning technique that adds perturbation to the character-level representation of algorithmically generated domain names in order to evade DGA classifiers, without the attacker having any knowledge about the DGA classifier's architecture and parameters. MaskDGA was evaluated using the DMD-2018 dataset of AGD names and four recently published DGA classifiers, in which the average F1-score of the classifiers degrades from 0.977 to 0.495 when applying the evasion technique. An additional evaluation was conducted using the same classifiers but with adversarial defenses implemented: adversarial re-training and distillation. The results of this evaluation show that MaskDGA can be used for improving the robustness of the character-level DGA classifiers against adversarial attacks, but that ideally DGA classifiers should incorporate additional features alongside character-level features that are demonstrated in this study to be vulnerable to adversarial attacks.

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arXiv2018

Analysis of Location Data Leakage in the Internet Traffic of Android-based Mobile Devices

In recent years we have witnessed a shift towards personalized, context-based applications and services for mobile device users. A key component of many of these services is the ability to infer the current location and predict the future location of users based on location sensors embedded in the devices. Such knowledge enables service providers to present relevant and timely offers to their users and better manage traffic congestion control, thus increasing customer satisfaction and engagement. However, such services suffer from location data leakage which has become one of today's most concerning privacy issues for smartphone users. In this paper we focus specifically on location data that is exposed by Android applications via Internet network traffic in plaintext (i.e., without encryption) without the user's awareness. We present an empirical evaluation, involving the network traffic of real mobile device users, aimed at: (1) measuring the extent of location data leakage in the Internet traffic of Android-based smartphone devices; and (2) understanding the value of this data by inferring users' points of interests (POIs). This was achieved by analyzing the Internet traffic recorded from the smartphones of a group of 71 participants for an average period of 37 days. We also propose a procedure for mining and filtering location data from raw network traffic and utilize geolocation clustering methods to infer users' POIs. The key findings of this research center on the extent of this phenomenon in terms of both ubiquity and severity; we found that over 85\% of devices of users are leaking location data, and the exposure rate of users' POIs, derived from the relatively sparse leakage indicators, is around 61%.

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