Measuring inconsistency in a network intrusion detection rule set based on Snort

In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the *This is a revised and significantly extended version of [1]

Efficient Assessment and Evaluation for Websites Vulnerabilities Using SNORT

An endless number of methods or ways exists to access illegally a web server or a website. The task of defending a system (e.g. network, server, website, etc.) is complex and challenging. SNORT is one of the popular open source tools that can be used to detect and possibly prevent illegal access and attacks for networks and websites. However, this largely depends on the way SNORT rules are designed and implemented. In this paper, we investigated in details several examples of SNORT rules and how they can be tuned to improve websites protection. We demonstrated practical methods to design and implement those methods in such ways that can show to security personnel how effectively can SNORT rules be used. Continuous experiments are conducted to evaluate and optimized the proposed rules. Results showed their ability to prevent tested network attacks. Each network should try to find the best set of rules that can detect and prevent most network attacks while at the same time cause minimal impact on network performance

A framework for cost-sensitive automated selection of intrusion response

In recent years, cost-sensitive intrusion response has gained significant interest due to its emphasis on the balance between potential damage incurred by the intrusion and cost of the response. However, one of the challenges in applying this approach is defining a consistent and adaptable measurement framework to evaluate the expected benefit of a response. In this thesis we present a model and framework for the cost-sensitive assessment and selection of intrusion response. Specifically, we introduce a set of measurements that characterize the potential costs associated with the intrusion handling process, and propose an intrusion response evaluation method with respect to the risk of potential intrusion damage, the effectiveness of the response action and the response cost for a system. The proposed framework has the important quality of abstracting the system security policy from the response selection mechanism, permitting policy adjustments to be made without changes to the model. We provide an implementation of the proposed solution as an IDS-independent plugin tool, and demonstrate its advantages over traditional static response systems and an existing dynamic response system

SNAP: Stateful Network-Wide Abstractions for Packet Processing

Early programming languages for software-defined networking (SDN) were built on top of the simple match-action paradigm offered by OpenFlow 1.0. However, emerging hardware and software switches offer much more sophisticated support for persistent state in the data plane, without involving a central controller. Nevertheless, managing stateful, distributed systems efficiently and correctly is known to be one of the most challenging programming problems. To simplify this new SDN problem, we introduce SNAP. SNAP offers a simpler "centralized" stateful programming model, by allowing programmers to develop programs on top of one big switch rather than many. These programs may contain reads and writes to global, persistent arrays, and as a result, programmers can implement a broad range of applications, from stateful firewalls to fine-grained traffic monitoring. The SNAP compiler relieves programmers of having to worry about how to distribute, place, and optimize access to these stateful arrays by doing it all for them. More specifically, the compiler discovers read/write dependencies between arrays and translates one-big-switch programs into an efficient internal representation based on a novel variant of binary decision diagrams. This internal representation is used to construct a mixed-integer linear program, which jointly optimizes the placement of state and the routing of traffic across the underlying physical topology. We have implemented a prototype compiler and applied it to about 20 SNAP programs over various topologies to demonstrate our techniques' scalability

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

A Cloud-based Intrusion Detection and Prevention System for Mobile Voting in South Africa

Publishe ThesisInformation and Communication Technology (ICT) has given rise to new technologies and solutions that were not possible a few years ago. One of these new technologies is electronic voting, also known as e-voting, which is the use of computerised equipment to cast a vote. One of the subsets of e-voting is mobile voting (m-voting). M-voting is the use of mobile phones to cast a vote outside the restricted electoral boundaries. Mobile phones are pervasive; they offer connection anywhere, at any time. However, utilising a fast-growing medium such as the mobile phone to cast a vote, poses various new security threats and challenges. Mobile phones utilise equivalent software design used by personal computers which makes them vulnerable or exposed to parallel security challenges like viruses, Trojans and worms. In the past, security solutions for mobile phones encountered several restrictions in practice. Several methods were used; however, these methods were developed to allow lightweight intrusion detection software to operate directly on the mobile phone. Nevertheless, such security solutions are bound to fail securing a device from intrusions as they are constrained by the restricted memory, storage, computational resources, and battery power of mobile phones. This study compared and evaluated two intrusion detection systems (IDSs), namely Snort and Suricata, in order to propose a cloud-based intrusion detection and prevention system (CIDPS) for m-voting in South Africa. It employed simulation as the primary research strategy to evaluate the IDSs. A quantitative research method was used to collect and analyse data. The researcher established that as much as Snort has been the preferred intrusion detection and prevention system (IDPS) in the past, Suricata presented more effective and accurate results close to what the researcher anticipated. The results also revealed that, though Suricata was proven effective enough to protect m-voting while saving the computational resources of mobile phones, more work needs to be done to alleviate the false-negative alerts caused by the anomaly detection method. This study adopted Suricata as a suitable cloud-based analysis engine to protect a mobile voting application like XaP

Evaluating Machine Learning Classifiers for Hybrid Network Intrusion Detection Systems

Existing classifier evaluation methods do not fully capture the intended use of classifiers in hybrid intrusion detection systems (IDS), systems that employ machine learning alongside a signature-based IDS. This research challenges traditional classifier evaluation methods in favor of a value-focused evaluation method that incorporates evaluator-specific weights for classifier and prediction threshold selection. By allowing the evaluator to weight known and unknown threat detection by alert classification, classifier selection is optimized to evaluator values for this application. The proposed evaluation methods are applied to a Cyber Defense Exercise (CDX) dataset. Network data is processed to produce connection-level features, then labeled using packet-level alerts from a signature-based IDS. Seven machine learning algorithms are evaluated using traditional methods and the value-focused method. Comparing results demonstrates fallacies with traditional methods that do not consider evaluator values. Classifier selection fallacies are revealed in 2 of 5 notional weighting schemes and prediction threshold selection fallacies are revealed in 5 of 5 weighting schemes