A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
Ref. @cite_13 discusses a network intrusion system that aims at detecting misbehavior by capturing TCP packet headers. They report that their AIS is unsuitable for detecting anomalies in communication networks. This result is questioned in @cite_9 where it is stated that this is due to the choice of problem representation and due to the choice of matching threshold @math for @math -contiguous bits matching.
@cite_9 @cite_13
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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. 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. Network intrusion detection is the problem of detecting anomalous network connections caused by intrusive activities. Many intrusion detection systems proposed before use both normal and intrusion data to build their classifiers. However, intrusion data are usually scarce and difficult to collect. We propose to solve this problem using a novelty detection approach. In particular, we propose to take a nonparametric density estimation approach based on Parzen-window estimators with Gaussian kernels to build an intrusion detection system using normal data only. To facilitate comparison, we have tested our system on the KDD Cup 1999 dataset. Our system compares favorably with the KDD Cup winner which is based on an ensemble of decision trees with bagged boosting, as our system uses no intrusion data at all and much less normal data for training. The next-generation Internet promises to provide a fundamental shift in the underlying architecture to support dynamic deployment of network protocols. With the introduction of programmability and dynamic protocol deployment in routers, potential vulnerabilities and attacks are expected to increase. In this paper, we consider the problem of detecting packet forwarding misbehavior in routers. Specifically, we focus on an attack scenario, where a router selectively drops packets destined for another node. Detecting such an attack is challenging since it requires differentiating malicious packet drops from congestion-based packet losses. We propose a controller-based misbehavior detection technique that effectively detects malicious routers using a hash-based delay sampling and verification. We provide a performance analysis of the detection accuracy and quantify the performance overhead of our system. Our results show that our technique provides accurate detection with low sampling rates. This paper investigates the role of negative selection in an artificial immune system (AIS) for network intrusion detection. The work focuses on the use of negative selection as a network traffic anomaly detector. The results of the negative selection algorithm experiments show a severe scaling problem for handling real network traffic data. The paper concludes by suggesting that the most appropriate use of negative selection in the AIS is as a filter for invalid detectors, not the generation of competent detectors. This paper presents a system for detecting intrusions when analyzing the network traffic payload looking for malware evidences. The system implements the detection algorithm as a Snort preprocessor component. Since they work together, a highly effective system against known attacks has been achieved (based on Snort rules) and a highly effective system against unknown threats (which was the main aim of the designed system). As the majority of such systems, the proposal consists of two phases: a training phase and a detection phase. During the training phase a statistical model of the legitimate network usage is created through Bloom Filters and N-grams techniques. Subsequently, the results obtained by analyzing a dataset of attacks are compared with such model. This will allow a set of rules to be developed which will be able to determine whether the packets payloads contain malware. In the detection phase, the traffic to analyze is compared with the model created in the training phase and the results obtained when applying rules. The performed experiments showed really satisfactory results, with 100 malware detection and just 0.15 false positives. 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. This paper aims to investigate and identify distinguishable TCP services, that comprise of both attack and normal types of TCP packets, using J48 decision tree algorithm. A predictive model capable of discriminating between normal and abnormal behavior of network traffic is developed by integrating Hidden Markov Model (HMM) technique with anomaly intrusion detection approach for each distinguishable TCP service. The model has been trained for each TCP session of the KDD Cup 1999 dataset using Baum-Welch training (BWT) and Viterbi training (VT) algorithms. Evaluation of the developed HMM model is performed using Forward and Backward algorithms. Results show that the proposed model is able to classify network traffic with approximately 76 to 99 accuracy. The overall performance of model is measured using standard evaluation method ROC curves. Network Intrusion Detection and Prevention Systems have emerged as one of the most effective ways of providing security to those connected to the network, and at the heart of almost every modern intrusion detection system is a string matching algorithm. String matching is one of the most critical elements because it allows for the system to make decisions based not just on the headers, but the actual content flowing through the network. Unfortunately, checking every byte of every packet to see if it matches one of a set of ten thousand strings becomes a computationally intensive task as network speeds grow into the tens, and eventually hundreds, of gigabits second. To keep up with these speeds a specialized device is required, one that can maintain tight bounds on worst case performance, that can be updated with new rules without interrupting operation, and one that is efficient enough that it could be included on chip with existing network chips or even into wireless devices. We have developed an approach that relies on a special purpose architecture that executes novel string matching algorithms specially optimized for implementation in our design. We show how the problem can be solved by converting the large database of strings into many tiny state machines, each of which searches for a portion of the rules and a portion of the bits of each rule. Through the careful co-design and optimization of our our architecture with a new string matching algorithm we show that it is possible to build a system that is 10 times more efficient than the currently best known approaches. In mobile ad-hoc networks, nodes act both as terminals and information relays, and they participate in a common routing protocol, such as Dynamic Source Routing (DSR). The networks are vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. 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. Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services. Network Intrusion Detection Systems (NIDS) have become crucial to securing modern networks. To be effective, a NIDS must be able to counter evasion attempts and operate at or near wire-speed. Failure to do so allows malicious packets to slip through a NIDS undetected. In this paper, we explore NIDS evasion through algorithmic complexity attacks. We present a highly effective attack against the Snort NIDS, and we provide a practical algorithmic solution that successfully thwarts the attack. This attack exploits the behavior of rule matching, yielding inspection times that are up to 1.5 million times slower than that of benign packets. Our analysis shows that this attack is applicable to many rules in Snort?s ruleset, rendering vulnerable the thousands of networks protected by it. Our countermeasure confines the inspection time to within one order of magnitude of benign packets. Experimental results using a live system show that an attacker needs only 4.0 kbps of bandwidth to perpetually disable an unmodified NIDS, whereas all intrusions are detected when our countermeasure is used. With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are increasingly limited by their need of an up-to-date and comprehensive knowledge base. Data mining techniques have been successfully applied in host-based intrusion detection. Applying data mining techniques on raw network data, however, is made difficult by the sheer size of the input; this is usually avoided by discarding the network packet contents.In this paper, we introduce a two-tier architecture to overcome this problem: the first tier is an unsupervised clustering algorithm which reduces the network packets payload to a tractable size. The second tier is a traditional anomaly detection algorithm, whose efficiency is improved by the availability of data on the packet payload content. In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra-and inter-audit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents. In network intrusion detection research, one popular strategy for finding attacks is monitoring a network's activity for anomalies: deviations from profiles of normality previously learned from benign traffic, typically identified using tools borrowed from the machine learning community. However, despite extensive academic research one finds a striking gap in terms of actual deployments of such systems: compared with other intrusion detection approaches, machine learning is rarely employed in operational "real world" settings. We examine the differences between the network intrusion detection problem and other areas where machine learning regularly finds much more success. Our main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively. We support this claim by identifying challenges particular to network intrusion detection, and provide a set of guidelines meant to strengthen future research on anomaly detection. Intrusion Detection is an important component of the security of the Network, through the critical information of the network and host system, it can determine the user’s invasion of illegal and legal acts of the user misuse of resources, and make an adequate response. According to the problem, which machine learning anomaly detection effect is not ideal when the user behavior changes and a separate anomaly detection. Based on the Bayesian inference anomaly detection, applying the Bayesian inference of statistical methods to machine learning anomaly detection, this paper established a decision tree corresponding to the method. This method overcomes the satisfactory of the anomaly detection individual test, and improves the machine learning in the predictive ability of anomaly detection and anomaly detection efficiency. Intrusion detection is the problem of identifying unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. The proliferation of heterogeneous computer networks provides additional implications for the intrusion detection problem. Namely, the increased connectivity of computer systems gives greater access to outsiders, and makes it easier for intruders to avoid detection. IDS’s are based on the belief that an intruder’s behavior will be noticeably different from that of a legitimate user. We are designing and implementing a prototype Distributed Intrusion Detection System (DIDS) that combines distributed monitoring and data reduction (through individual host and LAN monitors) with centralized data analysis (through the DIDS director) to monitor a heterogeneous network of computers. This approach is unique among current IDS’s. A main problem considered in this paper is the Network-user Identification problem, which is concerned with tracking a user moving across the network, possibly with a new user-id on each computer. Initial system prototypes have provided quite favorable results on this problem and the detection of attacks on a network. This paper provides an overview of the motivation behind DIDS, the system architecture and capabilities, and a discussion of the early prototype. Vehicle to Vehicle (V2V) and Vehicle to Infrastruc- ture (V2I) communication aims to increase safety, efficiency, and comfort of drivers. Vehicles periodically broadcast their current status, such as position, velocity, and other information. Received information is stored in a local knowledge base, often called world model, and used for application decisions. Because of the potential impact, V2V communication is an interesting target for malicious attackers. Message integrity protection using cryptographic signatures only protects against outsider attackers. In addition to signatures, misbehavior detection mechanisms comparable to intrusion detection systems (IDS) are needed to detect insider attackers. Given the complexity and large number of foreseen V2V and V2I applications, misbehavior detection cannot be a one-size-fits-all solution. In this paper, we present a flexible framework that can combine a range of different misbehavior detection mechanisms by modeling their outputs using subjective logic. We demonstrate the feasibility of our framework by using a combination of existing detection mechanisms to increase their misbehavior detection results. Abstract With the growing usage of technology, intrusion detection became an emerging area of research. Intrusion Detection System (IDS) attempts to identify and notify the activities of users as normal (or) anomaly. IDS is a nonlinear and complicated problem and deals with network traffic data. Many IDS methods have been proposed and produce different levels of accuracy. This is why development of effective and robust Intrusion detection system is necessary. In this paper, we have built a model for intrusion detection system using random forest classifier. Random Forest (RF) is an ensemble classifier and performs well compared to other traditional classifiers for effective classification of attacks. To evaluate the performance of our model, we conducted experiments on NSL-KDD data set. Empirical result show that proposed model is efficient with low false alarm rate and high detection rate.