ترغب بنشر مسار تعليمي؟ اضغط هنا

Generating Comprehensive Data with Protocol Fuzzing for Applying Deep Learning to Detect Network Attacks

86   0   0.0 ( 0 )
 نشر من قبل Qingtian Zou
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English
 تأليف Qingtian Zou




اسأل ChatGPT حول البحث

Network attacks have become a major security concern for organizations worldwide and have also drawn attention in the academics. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new approach, protocol fuzzing, to automatically generate high-quality network data, on which deep learning models can be trained. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.



قيم البحث

اقرأ أيضاً

With massive data being generated daily and the ever-increasing interconnectivity of the worlds Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national secu rity. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of attacks are classified. We demonstrate the advantage of the unsupervised representation learning model in binary intrusion detection tasks. Besides, we alleviate the data imbalance problem with SVM-SMOTE oversampling technique in 4-class classification and further demonstrate the effectiveness and the drawback of the oversampling mechanism with a deep neural network as a base model.
Recommender systems play a crucial role in helping users to find their interested information in various web services such as Amazon, YouTube, and Google News. Various recommender systems, ranging from neighborhood-based, association-rule-based, matr ix-factorization-based, to deep learning based, have been developed and deployed in industry. Among them, deep learning based recommender systems become increasingly popular due to their superior performance. In this work, we conduct the first systematic study on data poisoning attacks to deep learning based recommender systems. An attackers goal is to manipulate a recommender system such that the attacker-chosen target items are recommended to many users. To achieve this goal, our attack injects fake users with carefully crafted ratings to a recommender system. Specifically, we formulate our attack as an optimization problem, such that the injected ratings would maximize the number of normal users to whom the target items are recommended. However, it is challenging to solve the optimization problem because it is a non-convex integer programming problem. To address the challenge, we develop multiple techniques to approximately solve the optimization problem. Our experimental results on three real-world datasets, including small and large datasets, show that our attack is effective and outperforms existing attacks. Moreover, we attempt to detect fake users via statistical analysis of the rating patterns of normal and fake users. Our results show that our attack is still effective and outperforms existing attacks even if such a detector is deployed.
We present a new benchmark (ProFuzzBench) for stateful fuzzing of network protocols. The benchmark includes a suite of representative open-source network servers for popular protocols, and tools to automate experimentation. We discuss challenges and potential directions for future research based on this benchmark.
This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attackers capability and affected stage of the machine learning pipeline, the attack surfaces are recognize d to be wide and then formalized into six categorizations: code poisoning, outsourcing, pretrained, data collection, collaborative learning and post-deployment. Accordingly, attacks under each categorization are combed. The countermeasures are categorized into four general classes: blind backdoor removal, offline backdoor inspection, online backdoor inspection, and post backdoor removal. Accordingly, we review countermeasures, and compare and analyze their advantages and disadvantages. We have also reviewed the flip side of backdoor attacks, which are explored for i) protecting intellectual property of deep learning models, ii) acting as a honeypot to catch adversarial example attacks, and iii) verifying data deletion requested by the data contributor.Overall, the research on defense is far behind the attack, and there is no single defense that can prevent all types of backdoor attacks. In some cases, an attacker can intelligently bypass existing defenses with an adaptive attack. Drawing the insights from the systematic review, we also present key areas for future research on the backdoor, such as empirical security evaluations from physical trigger attacks, and in particular, more efficient and practical countermeasures are solicited.
DDoS attacks are simple, effective, and still pose a significant threat even after more than two decades. Given the recent success in machine learning, it is interesting to investigate how we can leverage deep learning to filter out application layer attack requests. There are challenges in adopting deep learning solutions due to the ever-changing profiles, the lack of labeled data, and constraints in the online setting. Offline unsupervised learning methods can sidestep these hurdles by learning an anomaly detector $N$ from the normal-day traffic ${mathcal N}$. However, anomaly detection does not exploit information acquired during attacks, and their performance typically is not satisfactory. In this paper, we propose two frameworks that utilize both the historic ${mathcal N}$ and the mixture ${mathcal M}$ traffic obtained during attacks, consisting of unlabeled requests. We also introduce a machine learning optimization problem that aims to sift out the attacks using ${mathcal N}$ and ${mathcal M}$. First, our proposed approach, inspired by statistical methods, extends an unsupervised anomaly detector $N$ to solve the problem using estimated conditional probability distributions. We adopt transfer learning to apply $N$ on ${mathcal N}$ and ${mathcal M}$ separately and efficiently, combining the results to obtain an online learner. Second, we formulate a specific loss function more suited for deep learning and use iterative training to solve it in the online setting. On publicly available datasets, our online learners achieve a $99.3%$ improvement on false-positive rates compared to the baseline detection methods. In the offline setting, our approaches are competitive with classifiers trained on labeled data.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا