arXiv subjects

Asaf Shabtai

Publications and source records attributed to Asaf Shabtai.

At least 145 records · Page 8Linked to original sources

arXiv2018

Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks

This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks. The study was performed on a SecureWater Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. e suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value.We applied the proposed method by using a variety of deep neural networks architectures including different variants of convolutional and recurrent networks. The test dataset from SWaT included 36 different cyberattacks. The proposed method successfully detects the vast majority of the attacks with a low false positive rate thus improving on previous works based on this data set. The results of the study show that 1D convolutional networks can be successfully applied to anomaly detection in industrial control systems and outperform more complex recurrent networks while being much smaller and faster to train.

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arXiv2018

MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks

Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data. In recent years, autoencoders have been shown to be effective anomaly detectors that train only on "normal" data. Generative adversarial networks (GANs) have been used to generate additional training samples for classifiers, thus making them more accurate and robust. However, in anomaly detection GANs are only used to reconstruct existing samples rather than to generate additional ones. This stems both from the small amount and lack of diversity of anomalous data in most domains. In this study we propose MDGAN, a novel GAN architecture for improving anomaly detection through the generation of additional samples. Our approach uses two discriminators: a dense network for determining whether the generated samples are of sufficient quality (i.e., valid) and an autoencoder that serves as an anomaly detector. MDGAN enables us to reconcile two conflicting goals: 1) generate high-quality samples that can fool the first discriminator, and 2) generate samples that can eventually be effectively reconstructed by the second discriminator, thus improving its performance. Empirical evaluation on a diverse set of datasets demonstrates the merits of our approach.

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arXiv2018

Piping Botnet - Turning Green Technology into a Water Disaster

The current generation of IoT devices is being used by clients and consumers to regulate resources (such as water and electricity) obtained from critical infrastructure (such as urban water services and smart grids), creating a new attack vector against critical infrastructure. In this research we show that smart irrigation systems, a new type of green technology and IoT device aimed at saving water and money, can be used by attackers as a means of attacking urban water services. We present a distributed attack model that can be used by an attacker to attack urban water services using a botnet of commercial smart irrigation systems. Then, we show how a bot running on a compromised device in a LAN can:(1) detect a connected commercial smart irrigation system (RainMachine, BlueSpray, and GreenIQ) within 15 minutes by analyzing LAN's behavior using a dedicated classification model, and (2) launch watering via a commercial smart irrigation system according to an attacker's wishes using spoofing and replay attacks. In addition, we model the damage that can be caused by performing such an attack and show that a standard water tower can be emptied in an hour using a botnet of 1,355 sprinklers and a flood water reservoir can be emptied overnight using a botnet of 23,866 sprinklers. Finally, we discuss countermeasure methods and hypothesize whether the next generation of plumbers will use Kali Linux instead of a monkey wrench.

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arXiv2018

Generation of Automatic and Realistic Artificial Profiles

Online social networks (OSNs) are abused by cyber criminals for various malicious activities. One of the most effective approaches for detecting malicious activity in OSNs involves the use of social network honeypots - artificial profiles that are deliberately planted within OSNs in order to attract abusers. Honeypot profiles have been used in detecting spammers, potential cyber attackers, and advanced attackers. Therefore, there is a growing need for the ability to reliably generate realistic artificial honeypot profiles in OSNs. In this research we present 'ProfileGen' - a method for the automated generation of profiles for professional social networks, giving particular attention to producing realistic education and employment records. 'ProfileGen' creates honeypot profiles that are similar to actual data by extrapolating the characteristics and properties of real data items. Evaluation by 70 domain experts confirms the method's ability to generate realistic artificial profiles that are indistinguishable from real profiles, demonstrating that our method can be applied to generate realistic artificial profiles for a wide range of applications.

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arXiv2018

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM. We also implement GADGET, a software framework to convert any malware binary to a binary undetected by malware classifiers, using the proposed attack, without access to the malware source code.

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arXiv2018

Detection of Malicious and Low Throughput Data Exfiltration Over the DNS Protocol

In the presence of security countermeasures, a malware designed for data exfiltration must do so using a covert channel to achieve its goal. Among existing covert channels stands the domain name system (DNS) protocol. Although the detection of covert channels over the DNS has been thoroughly studied in the last decade, previous research dealt with a specific subclass of covert channels, namely DNS tunneling. While the importance of tunneling detection is not undermined, an entire class of low throughput DNS exfiltration malware remained overlooked. The goal of this study is to propose a method for detecting both tunneling and low-throughput data exfiltration over the DNS. Towards this end, we propose a solution composed of a supervised feature selection method, and an interchangeable, and adjustable anomaly detection model trained on legitimate traffic. In the first step, a one-class classifier is applied for detecting domain-specific traffic that does not conform with the normal behavior. Then, in the second step, in order to reduce the false positive rate resulting from the attempt to detect the low-throughput data exfiltration we apply a rule-based filter that filters data exchange over DNS used by legitimate services. Our solution was evaluated on a medium-scale recursive DNS server logs, and involved more than 75,000 legitimate uses and almost 2,000 attacks. Evaluation results shows that while DNS tunneling is covered with at least 99% recall rate and less than 0.01% false positive rate, the detection of low throughput exfiltration is more difficult. While not preventing it completely, our solution limits a malware attempting to avoid detection with at most a 1kb/h of payload under the limitations of the DNS syntax (equivalent to five credit cards details, or ten user credentials per hour) which reduces the effectiveness of the attack.

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arXiv2018

Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. However, a drawback of neural networks is the amount of resources needed to train them. Many network gateways and routers devices, which could potentially host an NIDS, simply do not have the memory or processing power to train and sometimes even execute such models. More importantly, the existing neural network solutions are trained in a supervised manner. Meaning that an expert must label the network traffic and update the model manually from time to time. In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner. Kitsune's core algorithm (KitNET) uses an ensemble of neural networks called autoencoders to collectively differentiate between normal and abnormal traffic patterns. KitNET is supported by a feature extraction framework which efficiently tracks the patterns of every network channel. Our evaluations show that Kitsune can detect various attacks with a performance comparable to offline anomaly detectors, even on a Raspberry PI. This demonstrates that Kitsune can be a practical and economic NIDS.

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arXiv2018

Shattered Trust: When Replacement Smartphone Components Attack

Phone touchscreens, and other similar hardware components such as orientation sensors, wireless charging controllers, and NFC readers, are often produced by third-party manufacturers and not by the phone vendors themselves. Third-party driver source code to support these components is integrated into the vendor's source code. In contrast to 'pluggable' drivers, such as USB or network drivers, the component driver's source code implicitly assumes that the component hardware is authentic and trustworthy. As a result of this trust, very few integrity checks are performed on the communications between the component and the device's main processor. In this paper, we call this trust into question, considering the fact that touchscreens are often shattered and then replaced with aftermarket components of questionable origin. We analyze the operation of a commonly used touchscreen controller. We construct two standalone attacks, based on malicious touchscreen hardware, that function as building blocks toward a full attack: a series of touch injection attacks that allow the touchscreen to impersonate the user and exfiltrate data, and a buffer overflow attack that lets the attacker execute privileged operations. Combining the two building blocks, we present and evaluate a series of end-to-end attacks that can severely compromise a stock Android phone with standard firmware. Our results make the case for a hardware-based physical countermeasure.

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arXiv2018

Incentivized Delivery Network of IoT Software Updates Based on Trustless Proof-of-Distribution

The prevalence of IoT devices makes them an ideal target for attackers. To reduce the risk of attacks vendors routinely deliver security updates (patches) for their devices. The delivery of security updates becomes challenging due to the issue of scalability as the number of devices may grow much quicker than vendors' distribution systems. Previous studies have suggested a permissionless and decentralized blockchain-based network in which nodes can host and deliver security updates, thus the addition of new nodes scales out the network. However, these studies do not provide an incentive for nodes to join the network, making it unlikely for nodes to freely contribute their hosting space, bandwidth, and computation resources. In this paper, we propose a novel decentralized IoT software update delivery network in which participating nodes referred to as distributors) are compensated by vendors with digital currency for delivering updates to devices. Upon the release of a new security update, a vendor will make a commitment to provide digital currency to distributors that deliver the update; the commitment will be made with the use of smart contracts, and hence will be public, binding, and irreversible. The smart contract promises compensation to any distributor that provides proof-of-distribution, which is unforgeable proof that a single update was delivered to a single device. A distributor acquires the proof-of-distribution by exchanging a security update for a device signature using the Zero-Knowledge Contingent Payment (ZKCP) trustless data exchange protocol. Eliminating the need for trust between the security update distributor and the security consumer (IoT device) by providing fair compensation, can significantly increase the number of distributors, thus facilitating rapid scale out.

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arXiv2018

N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders

The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks. In order to mitigate this new threat there is a need to develop new methods for detecting attacks launched from compromised IoT devices and differentiate between hour and millisecond long IoTbased attacks. In this paper we propose and empirically evaluate a novel network based anomaly detection method which extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic emanating from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two of the most widely known IoT based botnets, Mirai and BASHLITE. Our evaluation results demonstrated our proposed method's ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices which were part of a botnet.

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arXiv2018

Vesper: Using Echo-Analysis to Detect Man-in-the-Middle Attacks in LANs

The Man-in-the-Middle (MitM) attack is a cyber-attack in which an attacker intercepts traffic, thus harming the confidentiality, integrity, and availability of the network. It remains a popular attack vector due to its simplicity. However, existing solutions are either not portable, suffer from a high false positive rate, or are simply not generic. In this paper, we propose Vesper: a novel plug-and-play MitM detector for local area networks. Vesper uses a technique inspired from impulse response analysis used in the domain of acoustic signal processing. Analogous to how echoes in a cave capture the shape and construction of the environment, so to can a short and intense pulse of ICMP echo requests model the link between two network hosts. Vesper uses neural networks called autoencoders to model the normal patterns of the echoed pulses, and detect when the environment changes. Using this technique, Vesper is able to detect MitM attacks with high accuracy while incurring minimal network overhead. We evaluate Vesper on LANs consisting of video surveillance cameras, servers, and PC workstations. We also investigate several possible adversarial attacks against Vesper, and demonstrate how Vesper mitigates these attacks.

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arXiv2017

Using LSTM Encoder-Decoder Algorithm for Detecting Anomalous ADS-B Messages

Although the ADS-B system is going to play a major role in the safe navigation of airplanes and air traffic control (ATC) management, it is also well known for its lack of security mechanisms. Previous research has proposed various methods for improving the security of the ADS-B system and mitigating associated risks. However, these solutions typically require the use of additional participating nodes (or sensors) (e.g., to verify the location of the airplane by analyzing the physical signal) or modification of the current protocol architecture (e.g., adding encryption or authentication mechanisms.) Due to the regulation process regarding avionic systems and the fact that the ADS-B system is already deployed in most airplanes, applying such modifications to the current protocol at this stage is impractical. In this paper we propose an alternative security solution for detecting anomalous ADS-B messages aimed at the detection of spoofed or manipulated ADS- B messages sent by an attacker or compromised airplane. The proposed approach utilizes an LSTM encoder-decoder algorithm for modeling flight routes by analyzing sequences of legitimate ADS-B messages. Using these models, aircraft can autonomously evaluate received ADS-B messages and identify deviations from the legitimate flight path (i.e., anomalies). We examined our approach on six different flight route datasets to which we injected different types of anomalies. Using our approach we were able to detect all of the injected attacks with an average false alarm rate of 4.3% for all of datasets.

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arXiv2017

Detection of Unauthorized IoT Devices Using Machine Learning Techniques

Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption of such devices, their diversity, standardization obstacles, and inherent mobility, organizations require an intelligent mechanism capable of automatically detecting suspicious IoT devices connected to their networks. In particular, devices not included in a white list of trustworthy IoT device types (allowed to be used within the organizational premises) should be detected. In this research, Random Forest, a supervised machine learning algorithm, was applied to features extracted from network traffic data with the aim of accurately identifying IoT device types from the white list. To train and evaluate multi-class classifiers, we collected and manually labeled network traffic data from 17 distinct IoT devices, representing nine types of IoT devices. Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases. Some IoT device types were identified quicker than others (e.g., sockets and thermostats were successfully detected within five TCP sessions of connecting to the network). Perfect detection of unauthorized IoT device types was achieved upon analyzing 110 consecutive sessions; perfect classification of white listed types required 346 consecutive sessions, 110 of which resulted in 99.49% accuracy. Further experiments demonstrated the successful applicability of classifiers trained in one location and tested on another. In addition, a discussion is provided regarding the resilience of our machine learning-based IoT white listing method to adversarial attacks.

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arXiv2017

Protecting Military Avionics Platforms from Attacks on MIL-STD-1553 Communication Bus

MIL-STD-1553 is a military standard that defines the physical and logical layers, and a command/response time division multiplexing of a communication bus used in military and aerospace avionic platforms for more than 40 years. As a legacy platform, MIL-STD-1553 was designed for high level of fault tolerance while less attention was taken with regard to security. Recent studies already addressed the impact of successful cyber attacks on aerospace vehicles that are implementing MIL-STD-1553. In this study we present a security analysis of MIL-STD-1553. In addition, we present a method for anomaly detection in MIL-STD-1553 communication bus and its performance in the presence of several attack scenarios implemented in a testbed, as well as results on real system data. Moreover, we propose a general approach towards an intrusion detection system (IDS) for a MIL-STD-1553 communication bus.

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arXiv2017

SIPHON: Towards Scalable High-Interaction Physical Honeypots

In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about the security of networked embedded devices. In this work, we focus on the adaptation of Honeypots for improving the security of IoTs. Low-interaction honeypots are used so far in the context of IoT. Such honeypots are limited and easily detectable, and thus, there is a need to find ways how to develop high-interaction, reliable, IoT honeypots that will attract skilled attackers. In this work, we propose the SIPHON architecture - a Scalable high-Interaction Honeypot platform for IoT devices. Our architecture leverages IoT devices that are physically at one location and are connected to the Internet through so-called wormholes distributed around the world. The resulting architecture allows exposing few physical devices over a large number of geographically distributed IP addresses. We demonstrate the proposed architecture in a large scale experiment with 39 wormhole instances in 16 cities in 9 countries. Based on this setup, six physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months. A preliminary analysis of the collected traffic indicates that devices in some cities attracted significantly more traffic than others (ranging from 600 000 incoming TCP connections for the most popular destination to less than 50000 for the least popular). We recorded over 400 brute-force login attempts to the web-interface of our devices using a total of 1826 distinct credentials, from which 11 attempts were successful. Moreover, we noted login attempts to Telnet and SSH ports some of which used credentials found in the recently disclosed Mirai malware.

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arXiv2017

Classification of Smartphone Users Using Internet Traffic

Today, smartphone devices are owned by a large portion of the population and have become a very popular platform for accessing the Internet. Smartphones provide the user with immediate access to information and services. However, they can easily expose the user to many privacy risks. Applications that are installed on the device and entities with access to the device's Internet traffic can reveal private information about the smartphone user and steal sensitive content stored on the device or transmitted by the device over the Internet. In this paper, we present a method to reveal various demographics and technical computer skills of smartphone users by their Internet traffic records, using machine learning classification models. We implement and evaluate the method on real life data of smartphone users and show that smartphone users can be classified by their gender, smoking habits, software programming experience, and other characteristics.

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arXiv2016

Anomaly Detection Using the Knowledge-based Temporal Abstraction Method

The rapid growth in stored time-oriented data necessitates the development of new methods for handling, processing, and interpreting large amounts of temporal data. One important example of such processing is detecting anomalies in time-oriented data. The Knowledge-Based Temporal Abstraction method was previously proposed for intelligent interpretation of temporal data based on predefined domain knowledge. In this study we propose a framework that integrates the KBTA method with a temporal pattern mining process for anomaly detection. According to the proposed method a temporal pattern mining process is applied on a dataset of basic temporal abstraction database in order to extract patterns representing normal behavior. These patterns are then analyzed in order to identify abnormal time periods characterized by a significantly small number of normal patterns. The proposed approach was demonstrated using a dataset collected from a real server.

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arXiv2016

Security Testbed for the Internet of Things

The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time and in any place, to each other and to the Internet. One of the major problems associated with the IoT is maintaining security; the heterogeneous nature of such deployments poses a challenge to many aspects of security, including security testing and analysis. In addition, there is no existing mechanism that performs security testing for IoT devices in different contexts. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed supports both standard and context-based security testing, with a set of security tests conducted under the different environmental conditions in which IoT devices operate. The requirements and architectural design of the proposed testbed are discussed, and the testbed operation is demonstrated in several testing scenarios.

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