This paper introduces a new similarity measure, the covering similarity, which we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pairwise similarity can also be directly derived from the covering similarity to compare two symbolic sequences. An efficient implementation to compute the covering similarity is proposed which uses a suffix-tree data structure, but other implementations, based on suffix array for instance, are possible and are possibly necessary for handling very large-scale problems. We have used this similarity to isolate attack sequences from normal sequences in the scope of host-based intrusion detection. We have assessed the covering similarity on two well-known benchmarks in the field. In view of the results reported on these two datasets for the state-of-the-art methods, according to the comparative study, we have carried out based on three challenging similarity measures commonly used for string processing, or in bioinformatics, we show that the covering similarity is particularly relevant to address the detection of anomalies in sequences of system calls.
In this work we exclusively address host intrusion detection system (HIDS) through semi-supervised anomaly-based approaches. Since Forrest's pioneering work @cite_37 most of HIDS (at least in the UNIX LINUX sphere) use system call sequencesas their primary source of information. Generally, sequences of system calls are represented as sequences of integer symbols, for which the order of occurrence of the symbol is of crucial importance. Numerous work and surveys have been published in the area of anomaly detection in the scope of intrusion detection, see @cite_11 @cite_25 , @cite_19 for recent studies. If we reduce the area of interest to anomaly detection in sequential data, four avenues for handling symbolic sequences are mainly followed:
@cite_19 @cite_37 @cite_25 @cite_11
2167240430 1984350393 1967354722 2134269391 1973841765 2100537916 2138209477 2803476375 1590452455 2551087083 2145195811 2129860818 2146211060 1570974058 2531448500 2066877142 1941427975 2531279268 2022229650 2006508099
Host-based anomaly intrusion detection system design is very challenging due to the notoriously high false alarm rate. This paper introduces a new host-based anomaly intrusion detection methodology using discontiguous system call patterns, in an attempt to increase detection rates whilst reducing false alarm rates. The key concept is to apply a semantic structure to kernel level system calls in order to reflect intrinsic activities hidden in high-level programming languages, which can help understand program anomaly behaviour. Excellent results were demonstrated using a variety of decision engines, evaluating the KDD98 and UNM data sets, and a new, modern data set. The ADFA Linux data set was created as part of this research using a modern operating system and contemporary hacking methods, and is now publicly available. Furthermore, the new semantic method possesses an inherent resilience to mimicry attacks, and demonstrated a high level of portability between different operating system versions. Intrusion detection systems (IDSs) are used to detect traces of malicious activities targeted against the network and its resources. Anomaly-based IDSs build models of the expected behavior of applications by analyzing events that are generated during the applications' normal operation. Once these models have been established, subsequent events are analyzed to identify deviations, on the assumption that anomalies represent evidence of an attack. Host-based anomaly detection systems often rely on system call sequences to characterize the normal behavior of applications. Recently, it has been shown how these systems can be evaded by launching attacks that execute legitimate system call sequences. The evasion is possible because existing techniques do not take into account all available features of system calls. In particular, system call arguments are not considered. We propose two primary improvements upon existing host-based anomaly detectors. First, we apply multiple detection models to system call arguments. Multiple models allow the arguments of each system call invocation to be evaluated from several different perspectives. Second, we introduce a sophisticated method of combining the anomaly scores from each model into an overall aggregate score. The combined anomaly score determines whether an event is part of an attack. Individual anomaly scores are often contradicting and, therefore, a simple weighted sum cannot deliver reliable results. To address this problem, we propose a technique that uses Bayesian networks to perform system call classification. We show that the analysis of system call arguments and the use of Bayesian classification improves detection accuracy and resilience against evasion attempts. In addition, the paper describes a tool based on our approach and provides a quantitative evaluation of its performance in terms of both detection effectiveness and overhead. A comparison with four related approaches is also presented. An anomaly intrusion detection method based on HMM is presented. The system call trace of a UNIX privileged process is passed to a HMM to obtain state transition sequences. Preliminary experiments prove the state transition sequences can express the different mode between normal action and intrusion behaviour in a more stable and simple manner. Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset. Traditional host-based anomaly detection systems model normal behavior of applications by analyzing system call sequences. The current sequence is then examined (using the model) for anomalous behavior, which could correspond to attacks. Though these techniques have been shown to be quite effective, a key element is missing – the inclusion and utilization of the system call arguments. Recent research shows that sequence-based systems are prone to evasion. We propose an idea of learning different representations for system call arguments. Results indicate that this information can be effectively used for detecting more attacks than traditional sequence-based techniques, with reasonable storage and computational overhead. Prevention of security breaches completely using the existing security technologies is unrealistic. As a result, intrusion detection is an important component in network security. However, many current intrusion detection systems (IDSs) are rule-based systems, which have limitations to detect novel intrusions. Moreover, encoding rules is time-consuming and highly depends on the knowledge of known intrusions. Therefore, we propose new systematic frameworks that apply a data mining algorithm called random forests in misuse, anomaly, and hybrid-network-based IDSs. In misuse detection, patterns of intrusions are built automatically by the random forests algorithm over training data. After that, intrusions are detected by matching network activities against the patterns. In anomaly detection, novel intrusions are detected by the outlier detection mechanism of the random forests algorithm. After building the patterns of network services by the random forests algorithm, outliers related to the patterns are determined by the outlier detection algorithm. The hybrid detection system improves the detection performance by combining the advantages of the misuse and anomaly detection. We evaluate our approaches over the knowledge discovery and data mining 1999 (KDDpsila99) dataset. The experimental results demonstrate that the performance provided by the proposed misuse approach is better than the best KDDpsila99 result; compared to other reported unsupervised anomaly detection approaches, our anomaly detection approach achieves higher detection rate when the false positive rate is low; and the presented hybrid system can improve the overall performance of the aforementioned IDSs. This paper investigates anomaly detection techniques that have been successful for detecting external threats and applies them to the insider threat problem. The "insider threat" involves the actions of a trusted and privileged user who is inappropriately accessing or disseminating sensitive information or otherwise compromising information systems. In contrast, the "external threat" involves the actions of an outsider attempting to compromise or gain access to the information systems. Although approaches for automatically detecting external threat instances have been quite successful (i.e., intrusion detection systems), there is very little similar work for the insider threat. In the past, anomaly detection systems have proven useful for detecting external threat. Anomaly detection at the system call level offers a high degree of information assurance in terms of tamper-resistance and system activity coverage. Therefore, we investigate three system-call-based feature representations: n-grams of system call names, histograms of system call names, and individual system calls with associated parameters. We find that none of these representations consistently performs as well when dealing with the internal threat as previous results show for external threat detection. However, parameter-based features for certain system calls do show some sensitivity to detecting the insider threat, and we plan to explore and enhance this sensitivity in future work. This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. Similarly, a section surveying algorithmic developments that are applicable to HIDS but tested on network data sets is included, as this is a large and growing area of applicable literature. To accommodate current researchers, a supplementary section giving descriptions of publicly available datasets is included, outlining their characteristics and shortcomings when used for IDS evaluation. Related surveys are organized and described. All sections are accompanied by tables concisely organizing the literature and datasets discussed. Finally, challenges, trends, and broader observations are throughout the survey and in the conclusion along with future directions of IDS research. Anomaly based intrusion detection has been held out as the best (perhaps only) hope for detecting previously unknown exploits. We examine two anomaly detectors based on the analysis of sequences of system calls and demonstrate that the general information hiding paradigm applies in this area also. Given even a fairly restrictive definition of normal behavior, we were able to devise versions of several exploits that escape detection. This is done in several ways: by modifying the exploit so that its manifestations match "normal," by making a serious attack have the manifestations of a less serious but similar attack, and by making the attack look like an entirely different attack. We speculate that similar attacks are possible against other anomaly based IDS and that the results have implications for other areas of information hiding. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. In this paper, we propose a system-call language-modeling approach for designing anomaly-based host intrusion detection systems. To remedy the issue of high false-alarm rates commonly arising in conventional methods, we employ a novel ensemble method that blends multiple thresholding classifiers into a single one, making it possible to accumulate 'highly normal' sequences. The proposed system-call language model has various advantages leveraged by the fact that it can learn the semantic meaning and interactions of each system call that existing methods cannot effectively consider. Through diverse experiments on public benchmark datasets, we demonstrate the validity and effectiveness of the proposed method. Moreover, we show that our model possesses high portability, which is one of the key aspects of realizing successful intrusion detection systems. This paper discusses our research in developing a generalized and systematic method for anomaly detection. The key ideas are to represent normal program behaviour using system call frequencies and to incorporate probabilistic techniques for classification to detect anomalies and intrusions. Using experiments on the sendmail system call data, we demonstrate that concise and accurate classifiers can be constructed to detect anomalies. An overview of the approach that we have implemented is provided. Intrusion detection systems rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. We study one such observable-sequences of system calls into the kernel of an operating system. Using system-call data sets generated by several different programs, we compare the ability of different data modeling methods to represent normal behavior accurately and to recognize intrusions. We compare the following methods: simple enumeration of observed sequences; comparison of relative frequencies of different sequences; a rule induction technique; and hidden Markov models (HMMs). We discuss the factors affecting the performance of each method and conclude that for this particular problem, weaker methods than HMMs are likely sufficient. A very effective means to evade signature-based intrusion detection systems (IDS) is to employ polymorphic techniques to generate attack instances that do not share a fixed signature. Anomaly-based intrusion detection systems provide good defense because existing polymorphic techniques can make the attack instances look different from each other, but cannot make them look like normal. In this paper we introduce a new class of polymorphic attacks, called polymorphic blending attacks, that can effectively evade byte frequency-based network anomaly IDS by carefully matching the statistics of the mutated attack instances to the normal profiles. The proposed polymorphic blending attacks can be viewed as a subclass of the mimicry attacks. We take a systematic approach to the problem and formally describe the algorithms and steps required to carry out such attacks. We not only show that such attacks are feasible but also analyze the hardness of evasion under different circumstances. We present detailed techniques using PAYL, a byte frequency-based anomaly IDS, as a case study and demonstrate that these attacks are indeed feasible. We also provide some insight into possible countermeasures that can be used as defense. Most behavioral detectors of malware remain specific to a given language and platform, mostly executables for Windows. The objective of this paper is to define a generic approach for behavioral detection based on two layers respectively responsible for abstraction and detection. The abstraction layer is specific to a platform and a language. It interprets the collected instructions, API calls and arguments and classifies these operations, as well as the objects involved, according to their purpose in the malware lifecycle. The detection layer remains generic and interoperable with different abstraction components. It relies on parallel automata parsing attribute-grammars where semantic rules are used for object typing (object classification) and object binding (data-flow). Theoretical results are first given with respect to the grammatical constraints weighting on the signature construction as well as to the resulting complexity of the detection. For experimentation purposes, two abstraction components have then been developed: one processing system call traces and the other processing the VBScript interpreted language. Experimentations have provided promising detection rates, in particular for scripts (89 ), with almost no false positives. In the case of process traces, the detection rate remains significant (51 ) but could be increased by sophisticated collection tools. The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. Intrusion detection is well-known as an essential component to secure the systems in Information and Communication Technology (ICT). Based on the type of analyzing events, two kinds of Intrusion Detection Systems (IDS) have been proposed: anomaly-based and misuse-based. In this paper, three-layer Recurrent Neural Network (RNN) architecture with categorized features as inputs and attack types as outputs of RNN is proposed as misuse-based IDS. The input features are categorized to basic features, content features, time-based traffic features, and host-based traffic features. The attack types are classified to Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). For this purpose, in this study, we use the 41 features per connection defined by International Knowledge Discovery and Data mining group (KDD). The RNN has an extra output which corresponds to normal class (no attack). The connections between the nodes of two hidden layers of RNN are considered partial. Experimental results show that the proposed model is able to improve classification rate, particularly in R2L attacks. This method also offers better Detection Rate (DR) and Cost Per Example (CPE) when compared to similar related works and also the simulated Multi-Layer Perceptron (MLP) and Elman-based intrusion detectors. On the other hand, False Alarm Rate (FAR) of the proposed model is not degraded significantly when compared to some recent machine learning methods. A method is introduced for detecting intrusions at the level of privileged processes. Evidence is given that short sequences of system calls executed by running processes are a good discriminator between normal and abnormal operating characteristics of several common UNIX programs. Normal behavior is collected in two waysc Synthetically, by exercising as many normal modes of usage of a program as possible, and in a live user environment by tracing the actual execution of the program. In the former case several types of intrusive behavior were studieds in the latter case, results were analyzed for false positives. Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the behavior patterns far away from our model are labeled as anomalous, for which far away is measured by a time-varying threshold. Experiments are implemented on three surveillance datasets, namely UMN, UCSD Ped1 Ped2 and Avenue dataset. All datasets have changing scenes due to mutable crowd density and behavior types. Anomaly detection results show that our model can adapt to the current scene rapidly and reduce false alarms while still detecting most anomalies. Quantitative comparisons with 12 recent approaches further confirm our superiority. HighlightsOnline GNG employs neighbor-related strategies to adjust dominant learning parameters in a fully automated way.Online GNG can estimate the learning efficiency and adjust the network size to fit the unstable data space dynamically.Online GNG effectively reduces the false alarms and leak detections caused by model aging which frequently happens in changing surveillance scenes. We consider the sequential (i.e., online) detection of false data injection attacks in smart grid, which aims to manipulate the state estimation procedure by injecting malicious data to the monitoring meters. The unknown parameters in the system, namely the state vector, injected malicious data and the set of attacked meters pose a significant challenge for designing a robust, computationally efficient, and high-performance detector. We propose a sequential detector based on the generalized likelihood ratio to address this challenge. Specifically, the proposed detector is designed to be robust to a variety of attacking strategies, and load situations in the power system, and its computational complexity linearly scales with the number of meters. Moreover, it considerably outperforms the existing first-order cumulative sum detector in terms of the average detection delay and robustness to various attacking strategies. For wide-area monitoring in smart grid, we further develop a distributed sequential detector using an adaptive sampling technique called level-triggered sampling. The resulting distributed detector features single bit per sample in terms of the communication overhead, while preserving the high performance of the proposed centralized detector. Many network intrusion detection systems (NIDS) use byte sequences as signatures to detect malicious activity. While being highly efficient, they tend to suffer from a high false-positive rate. We develop the concept of contextual signatures as an improvement of string-based signature-matching. Rather than matching fixed strings in isolation, we augment the matching process with additional context. When designing an efficient signature engine for the NIDS bro, we provide low-level context by using regular expressions for matching, and high-level context by taking advantage of the semantic information made available by bro's protocol analysis and scripting language. Therewith, we greatly enhance the signature's expressiveness and hence the ability to reduce false positives. We present several examples such as matching requests with replies, using knowledge of the environment, defining dependencies between signatures to model step-wise attacks, and recognizing exploit scans.To leverage existing efforts, we convert the comprehensive signature set of the popular freeware NIDS snort into bro's language. While this does not provide us with improved signatures by itself, we reap an established base to build upon. Consequently, we evaluate our work by comparing to snort, discussing in the process several general problems of comparing different NIDSs.