arXiv subjects

Ron Bitton

Publications and source records attributed to Ron Bitton.

28 records · Page 2Linked to original sources

arXiv2020

Adversarial robustness via stochastic regularization of neural activation sensitivity

Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples. Thus we can no longer hope to immune classifiers against adversarial examples and instead can only aim to achieve the following two defense goals: 1) making adversarial examples harder to find, or 2) weakening their adversarial nature by pushing them further away from correctly classified data points. Most if not all the previously suggested defense mechanisms attend to just one of those two goals, and as such, could be bypassed by adaptive attacks that take the defense mechanism into consideration. In this work we suggest a novel defense mechanism that simultaneously addresses both defense goals: We flatten the gradients of the loss surface, making adversarial examples harder to find, using a novel stochastic regularization term that explicitly decreases the sensitivity of individual neurons to small input perturbations. In addition, we push the decision boundary away from correctly classified inputs by leveraging Jacobian regularization. We present a solid theoretical basis and an empirical testing of our suggested approach, demonstrate its superiority over previously suggested defense mechanisms, and show that it is effective against a wide range of adaptive attacks.

cs.LG↗

arXiv2020

An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions

Attack graphs are one of the main techniques used to automate the risk assessment process. In order to derive a relevant attack graph, up-to-date information on known attack techniques should be represented as interaction rules. Designing and creating new interaction rules is not a trivial task and currently performed manually by security experts. However, since the number of new security vulnerabilities and attack techniques continuously and rapidly grows, there is a need to frequently update the rule set of attack graph tools with new attack techniques to ensure that the set of interaction rules is always up-to-date. We present a novel, end-to-end, automated framework for modeling new attack techniques from textual description of a security vulnerability. Given a description of a security vulnerability, the proposed framework first extracts the relevant attack entities required to model the attack, completes missing information on the vulnerability, and derives a new interaction rule that models the attack; this new rule is integrated within MulVAL attack graph tool. The proposed framework implements a novel pipeline that includes a dedicated cybersecurity linguistic model trained on the the NVD repository, a recurrent neural network model used for attack entity extraction, a logistic regression model used for completing the missing information, and a novel machine learning-based approach for automatically modeling the attacks as MulVAL's interaction rule. We evaluated the performance of each of the individual algorithms, as well as the complete framework and demonstrated its effectiveness.

cs.CR↗

arXiv2020

Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities

The existence of a security vulnerability in a system does not necessarily mean that it can be exploited. In this research, we introduce Autosploit -- an automated framework for evaluating the exploitability of vulnerabilities. Given a vulnerable environment and relevant exploits, Autosploit will automatically test the exploits on different configurations of the environment in order to identify the specific properties necessary for successful exploitation of the existing vulnerabilities. Since testing all possible system configurations is infeasible, we introduce an efficient approach for testing and searching through all possible configurations of the environment. The efficient testing process implemented by Autosploit is based on two algorithms: generalized binary splitting and Barinel, which are used for noiseless and noisy environments respectively. We implemented the proposed framework and evaluated it using real vulnerabilities. The results show that Autosploit is able to automatically identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments. These important results can be utilized for more accurate and effective risk assessment.

cs.CR↗

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.

cs.LG↗

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.

cs.LG↗

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.

cs.CR↗

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.

cs.CR↗

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.

cs.CR↗

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%.

cs.CR↗

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.

cs.CR↗