GLOD: Gaussian Likelihood Out of Distribution Detector

Discriminative deep neural networks (DNNs) do well at classifying input associated with the classes they have been trained on. However, out-of-distribution (OOD) input poses a great challenge to such models and consequently represents a major risk when these models are used in safety-critical systems. In the last two years, extensive research has been performed in the domain of OOD detection. This research has relied mainly on training the model with OOD data or using an auxiliary (external) model for OOD detection. Such methods have limited capability in detecting OOD samples and may not be applicable in many real world use cases. In this paper, we propose GLOD - Gaussian likelihood out of distribution detector - an extended DNN classifier capable of efficiently detecting OOD samples without relying on OOD training data or an external detection model. GLOD uses a layer that models the Gaussian density function of the trained classes. The layer outputs are used to estimate a Log-Likelihood Ratio which is employed to detect OOD samples. We evaluate GLOD's detection performance on three datasets: SVHN, CIFAR-10, and CIFAR-100. Our results show that GLOD surpasses state-of-the-art OOD detection techniques in detection performance by a large margin.

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.

Adversarial Learning in the Cyber Security Domain

In recent years, machine learning algorithms, and more specially, deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This paper comprehensively summarizes the latest research on adversarial attacks against security solutions that are based on machine learning techniques and presents the risks they pose to cyber security solutions. First, we discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain. Following that, we define a unified taxonomy, where the adversarial attack methods are characterized based on their stage of occurrence, and the attacker's goals and capabilities. Then, we categorize the applications of adversarial attack techniques in the cyber security domain. Finally, we use our taxonomy to shed light on gaps in the cyber security domain that have already been addressed in other adversarial learning domains and discuss their impact on future adversarial learning trends in the cyber security domain.

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.

Lightweight Collaborative Anomaly Detection for the IoT using Blockchain

Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. However, anomaly detection models must be trained for a long time in order to capture all benign behaviors. Furthermore, the anomaly detection model is vulnerable to adversarial attacks since, during the training phase, all observations are assumed to be benign. In this paper, we propose (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to ensemble an anomaly detection model in a distributed environment. Blockchain framework incrementally updates a trusted anomaly detection model via self-attestation and consensus among the IoT devices. We evaluate our method on a distributed IoT simulation platform, which consists of 48 Raspberry Pis. The simulation demonstrates how the approach can enhance the security of each device and the security of the network as a whole.

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.

IoT Device Identification Using Deep Learning

The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers due to the less secure nature of the devices. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization's network also increases the risk of attacks. In order to address this threat, organizations often implement security policies in which only the connection of white-listed IoT devices is permitted. To monitor adherence to such policies and protect their networks, organizations must be able to identify the IoT devices connected to their networks and, more specifically, to identify connected IoT devices that are not on the white-list (unknown devices). In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network. In contrast to previous work, our approach does not require that complex feature engineering be applied on the network traffic, since we represent the communication behavior of IoT devices using small images built from the IoT devices network traffic payloads. In our experiments, we trained a multiclass classifier on a publicly available dataset, successfully identifying 10 different IoT devices and the traffic of smartphones and computers, with over 99% accuracy. We also trained multiclass classifiers to detect unauthorized IoT devices connected to the network, achieving over 99% overall average detection accuracy.

Neural Network Representation Control: Gaussian Isolation Machines and CVC Regularization

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. We also demonstrate the benefits of incorporating part of the GIM's loss function into standard neural networks as a regularization method.

Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks

Recent discoveries in the field of adversarial machine learning have shown that Artificial Neural Networks (ANNs) are susceptible to adversarial attacks. These attacks cause misclassification of specially crafted adversarial samples. In light of this phenomenon, it is worth investigating whether other types of neural networks are less susceptible to adversarial attacks. In this work, we applied standard attack methods originally aimed at conventional ANNs, towards stochastic ANNs and also towards Spiking Neural Networks (SNNs), across three different datasets namely MNIST, CIFAR-10 and Patch Camelyon. We analysed their adversarial robustness against attacks performed in the raw image space of the different model variants. We employ a variety of attacks namely Basic Iterative Method (BIM), Carlini & Wagner L2 attack (CWL2) and Boundary attack. Our results suggests that SNNs and stochastic ANNs exhibit some degree of adversarial robustness as compared to their ANN counterparts under certain attack methods. Namely, we found that the Boundary and the state-of-the-art CWL2 attacks are largely ineffective against stochastic ANNs. Following this observation, we proposed a modified version of the CWL2 attack and analysed the impact of this attack on the models' adversarial robustness. Our results suggest that with this modified CWL2 attack, many models are more easily fooled as compared to the vanilla CWL2 attack, albeit observing an increase in L2 norms of adversarial perturbations. Lastly, we also investigate the resilience of alternative neural networks against adversarial samples transferred from ResNet18. We show that the modified CWL2 attack provides an improved cross-architecture transferability compared to other attacks.