We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.
Recently, @cite_3 considered scenarios in which an attacker designs attacks carefully such that the conventional bad data detection algorithms are not capable of detecting them. Inspired by their work, many other papers targeted this problem @cite_3 @cite_4 @cite_2 @cite_12 @cite_16 @cite_13 @cite_5 @cite_15 @cite_7 . An adversary attack has an impact on the real-time and day-ahead electricity markets. Such situations have been studied by @cite_8 @cite_0 , among others.
@cite_4 @cite_7 @cite_8 @cite_3 @cite_0 @cite_2 @cite_5 @cite_15 @cite_16 @cite_13 @cite_12
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Malicious data attacks to the real-time electricity market are studied. In particular, an adversary launches an attack by manipulating data from a set of meters with the goal of influencing revenues of a real-time market. The adversary must deal with the tradeoff between avoiding being detected by the control center and making maximum profit from the real time market. Optimal attacking strategy is obtained through an optimization of a quasi-concave objective function. It is shown that the probability of detection of optimal attack will always be less than 0.5. Attack performance is evaluated using simulations on the IEEE 14-bus system. We consider adversarial examples in the black-box decision-based scenario. Here, an attacker has access to the final classification of a model, but not its parameters or softmax outputs. Most attacks for this scenario are based either on transferability, which is unreliable, or random sampling, which is often slow. Focusing on the latter, we propose to improve the efficiency of sampling-based attacks with prior beliefs about the target domain. We identify two such priors, image frequency and surrogate gradients, and discuss how to integrate them into a unified sampling procedure. We then formulate the Biased Boundary Attack, which achieves a drastic speedup over the original Boundary Attack. We demonstrate the effectiveness of our approach against an ImageNet classifier. We also showcase a targeted attack for the Google Cloud Vision API, where we craft convincing examples with just a few hundred queries. Finally, we demonstrate that our approach outperforms the state of the art when facing strong defenses: Our attack scored second place in the targeted attack track of the NeurIPS 2018 Adversarial Vision Challenge. The problem of detecting and characterizing impacts of malicious attacks against smart grid state estimation is considered. Different from the classical bad data detection for state estimation, the detection of malicious data injected by an adversary must take into account carefully designed attacks capable of evading conventional bad data detection. A Bayesian framework is presented for the characterization of fundamental tradeoffs at the control center and for the adversary. For the control center, a detector based on the generalized likelihood ratio test (GRLT) is introduced and compared with conventional bad detection detection schemes. For the adversary, the tradeoff between increasing the mean square error (MSE) of the state estimation vs. the probability of being detected by the control center is characterized. A heuristic is presented for the design of worst attack. In the field of infrastructures’ surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal’s source can be defective, whether it is human—miss-interpretation of the signal—or computed—material malfunction; the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected; the interpretation given to the combination of signals does not correspond to the reality on the field—because the knowledge of the rule designer is subjective or because the environment evolves over-time—the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules’ representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and Inductive Logic Programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place. In order to understand the security level of an organization network, detection methods are important to tackle the probable risks of the attackers' malicious activities. Intrusion detection systems, as detection solutions of the defense in depth concept, are one of the main devices to record and analyze suspicious behaviors. Besides the benefits of these systems for security enhancement, they will bring some challenges and issues for security administrators. A large number of raw alerts generated by the intrusion detection systems clearly reflect the need for a novel proactive alert correlation framework to reduce redundant alerts, correlate security incidents, discover and model multi-step attack scenarios, and track them. Several alert correlation frameworks have been proposed in the literature, but the majority of them address the alert correlation in the offline settings. In this paper, we propose a three-phase alert correlation framework, which processes the generated alerts in real time, correlates the alerts with the aid of causal knowledge discovery to automatically extract causal relationships between alerts, constructs the attack scenarios using the Bayesian network concept, and predicts the next goal of the attacks using the creating attack prediction rules. Experimental results show that the scalable proposed framework is efficient enough in learning and detecting known and unknown multi-step attack scenarios without using any predefined knowledge. The results also show that the proposed framework perfectly estimates complex attacks before they can damage the assets of the network. Copyright © 2016 John Wiley & Sons, Ltd. Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical cognitive radio systems the data fusion center typically does not know the attacker's strategy. To alleviate the problem of the unknown strategy of attacker(s), an abnormality-detection approach, based on the abnormality detection in data mining, is proposed. The performance of the attacker detection in the single-attacker scenario is analyzed explicitly. For the case in which the attacker does not know the reports of honest secondary users (called independent attack), it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends to infinity. For the case in which the attacker knows all the reports of other secondary users, based on which the attacker sends its report (called dependent attack), an approach for the attacker to perfectly avoid being detected is found, provided that the attacker has perfect information about the miss-detection and false-alarm probabilities. This motivates cognitive radio networks to protect the reports of secondary users. The performance of attacker detection in the general case of multiple attackers is demonstrated using numerical simulations. Abstract Security information and event management (SIEM) systems receive a large number of alerts from different intrusion detection systems. They are expected, from these alerts, to make reliable and timely decisions regarding the types of ongoing attack scenarios and their priorities. However, the lack of an agreed-upon vocabulary for the representation of the domain knowledge makes it difficult for state-of-the-art SIEM systems to effectively manage these complex decisions. To overcome this problem, an ontology-based expert system approach can provide domain knowledge modeling as a foundation for disambiguation of meaning and automatic reasoning regarding ongoing attack scenarios. The proposed approach reconstructs attack scenarios by reasoning based on the evidences in the alert stream. The main idea of the proposed approach is to identify the causal relation between alerts using their similarity. This approach assumes that the similarity between two successive steps in an attack scenario is greater than that of two non-successive steps. Moreover, the similarity between the steps of the same attack scenario is greater than that between the steps of two different attack scenarios. The benefit of the proposed approach includes the fast and incremental reconstruction of known and unknown attack scenarios without expert intervention, which is an enormous step forward in developing expert and intelligent systems for cyber security. We evaluated the proposed technique by performing experiments on two known datasets: DARPA 2000 and MACCDC 2012. The results prove the advantages of the proposed approach with regard to completeness and soundness criteria. Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9 accuracy, our method achieves 55.7 ; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6 accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 --- it achieved 50.6 classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by 10 . Code is available at this https URL. Adversarial attacks involve adding, small, often imperceptible, perturbations to inputs with the goal of getting a machine learning model to misclassifying them. While many different adversarial attack strategies have been proposed on image classification models, object detection pipelines have been much harder to break. In this paper, we propose a novel strategy to craft adversarial examples by solving a constrained optimization problem using an adversarial generator network. Our approach is fast and scalable, requiring only a forward pass through our trained generator network to craft an adversarial sample. Unlike in many attack strategies, we show that the same trained generator is capable of attacking new images without explicitly optimizing on them. We evaluate our attack on a trained Faster R-CNN face detector on the cropped 300-W face dataset where we manage to reduce the number of detected faces to @math of all originally detected faces. In a different experiment, also on 300-W, we demonstrate the robustness of our attack to a JPEG compression based defense typical JPEG compression level of @math reduces the effectiveness of our attack from only @math of detected faces to a modest @math . In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerging research field in the conjunction of deep learning and security. In particular, MagNet consisting of an adversary detector and a data reformer is by far one of the strongest defenses in the black-box oblivious attack setting, where the attacker aims to craft transferable adversarial examples from an undefended DNN model to bypass an unknown defense module deployed on the same DNN model. Under this setting, MagNet can successfully defend a variety of attacks in DNNs, including the high-confidence adversarial examples generated by the Carlini and Wagner's attack based on the @math distortion metric. However, in this paper, under the same attack setting we show that adversarial examples crafted based on the @math distortion metric can easily bypass MagNet and mislead the target DNN image classifiers on MNIST and CIFAR-10. We also provide explanations on why the considered approach can yield adversarial examples with superior attack performance and conduct extensive experiments on variants of MagNet to verify its lack of robustness to @math distortion based attacks. Notably, our results substantially weaken the assumption of effective threat models on MagNet that require knowing the deployed defense technique when attacking DNNs (i.e., the gray-box attack setting). Future power networks will be characterized by safe and reliable functionality against physical and cyber attacks. This paper proposes a unified framework and advanced monitoring procedures to detect and identify network components malfunction or measurements corruption caused by an omniscient adversary. We model a power system under cyber-physical attack as a linear time-invariant descriptor system with unknown inputs. Our attack model generalizes the prototypical stealth, (dynamic) false-data injection and replay attacks. We characterize the fundamental limitations of both static and dynamic procedures for attack detection and identification. Additionally, we design provably-correct (dynamic) detection and identification procedures based on tools from geometric control theory. Finally, we illustrate the effectiveness of our method through a comparison with existing (static) detection algorithms, and through a numerical study.