Machine-learning based intrusion detection classifiers are able to detect unknown attacks, but at the same time, they may be susceptible to evasion by obfuscation techniques. An adversary intruder which possesses a crucial knowledge about a protection system can easily bypass the detection module. The main objective of our work is to improve the performance capabilities of intrusion detection classifiers against such adversaries. To this end, we firstly propose several obfuscation techniques of remote attacks that are based on the modification of various properties of network connections; then we conduct a set of comprehensive experiments to evaluate the effectiveness of intrusion detection classifiers against obfuscated attacks. We instantiate our approach by means of a tool, based on NetEm and Metasploit, which implements our obfuscation operators on any TCP communication. This allows us to generate modified network traffic for machine learning experiments employing features for assessing network statistics and behavior of TCP connections. We perform the evaluation of five classifiers: Gaussian Naive Bayes, Gaussian Naive Bayes with kernel density estimation, Logistic Regression, Decision Tree, and Support Vector Machines. Our experiments confirm the assumption that it is possible to evade the intrusion detection capability of all classifiers trained without prior knowledge about obfuscated attacks, causing an exacerbation of the TPR ranging from 7.8 to 66.8 . Further, when widening the training knowledge of the classifiers by a subset of obfuscated attacks, we achieve a significant improvement of the TPR by 4.21 - 73.3 , while the FPR is deteriorated only slightly (0.1 - 1.48 ). Finally, we test the capability of an obfuscations-aware classifier to detect unknown obfuscated attacks, where we achieve over 90 detection rate on average for most of the obfuscations.
In order to answer non-payload-based evasions of NIDS, the concept of network traffic normalizer was introduced by @cite_27 . The authors proposed the implementation of normalizer called . Norm performs normalizations of ambiguities in the TCP traffic stream that can be seen by NIDS. How -ever, introducing a network normalization brought problems related to platform dependent semantic of network ambiguities interpretation as well as throughput reduction. These problems lead @cite_1 to introduce the concept and implementation of , which eliminates them with minimal runtime cost by building profiles of the network topology including the TCP IP policies of hosts on the network. A NIDS may then use the host profiles to disambiguate the interpretation of the network traffic on a per-host basis. However, because of the shortcomings of network normalizers, their usage in a network can result in side-effects and can even be prone to various attacks, e.g., state holding, and CPU overload @cite_12 , @cite_32 , @cite_14 .
@cite_14 @cite_1 @cite_32 @cite_27 @cite_12
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A critical problem faced by a network intrusion detection system (NIDS) is that of ambiguity. The NIDS cannot always determine what traffic reaches a given host nor how that host will interpret the traffic, and attackers may exploit this ambiguity to avoid detection or cause misleading alarms. We present a lightweight solution, active mapping, which eliminates TCP IP-based ambiguity in a NIDS analysis with minimal runtime cost. Active mapping efficiently builds profiles of the network topology and the TCP IP policies of hosts on the network; a NIDS may then use the host profiles to disambiguate the interpretation of the network traffic on a per-host basis. Active mapping avoids the semantic and performance problems of traffic normalization, in which traffic streams are modified to remove ambiguities. We have developed a prototype implementation of active mapping and modified a NIDS to use the active mapping-generated profile database in our tests. We found wide variation across operating systems' TCP IP stack policies in real-world tests (about 6700 hosts), underscoring the need for this sort of disambiguation. A fundamental problem for network intrusion detection systems is the ability of a skilled attacker to evade detection by exploiting ambiguities in the traffic stream as seen by the monitor. We discuss the viability of addressing this problem by introducing a new network forwarding element called a traffic normalizer. The normalizer sits directly in the path of traffic into a site and patches up the packet stream to eliminate potential ambiguities before the traffic is seen by the monitor, removing evasion opportunities. We examine a number of tradeoffs in designing a normalizer, emphasizing the important question of the degree to which normalizations undermine end-to-end protocol semantics. We discuss the key practical issues of "cold start" and attacks on the normalizer, and develop a methodology for systematically examining the ambiguities present in a protocol based on walking the protocol's header. We then present norm, a publicly available user-level implementation of a normalizer that can normalize a TCP traffic stream at 100,000 pkts sec in memory-to-memory copies, suggesting that a kernel implementation using PC hardware could keep pace with a bidirectional 100 Mbps link with sufficient headroom to weather a high-speed flooding attack of small packets. We use simulation to study the dynamics of the congestion cent rol algorithm embedded in the BSD 4.3-Tahoe TCP implementation. We investigate the simple case of a few TCP connections, originating and terminating at the same pair of hosts, using a single bottleneck link. This work is an extension of our earlier work ([16]), where one-way traffic (i.e., all of the sources are on the same host and all of the destinations are on the other host) was studied. In this paper we investigate the dynamics that results from two-way traffic (in which there are data sources on both hosts). We find that the one-way traffic clustering and loss-synchronization phenomena d cussed in [16] persist in this new situation, albeit in a slightly modified form. In addition, there are two new phenomena not present in the earlier study: (1) ACK-compression, which is due to the interaction of data and ACK packets and gives rise to rapid fluctuations in queue length, and (2) an out-of-phase queue-synchronization mode, which keeps link utilization less than optimal even in the limit of very large buffers. These phenomena are helpful in understanding results from an earlier study of network oscillations ([19]). Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52 , 1.06 , and 0.91 with only 20, 50, and 100 labeled samples, respectively.