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

SIEM Optimization using Honeypots

Στην εποχή μας, τα συστήματα Information and Event Management (SIEM) αποτελούν αναπόσπαστο μέρος της υποδομής ασφάλειας ενός οργανισμού. Παρά το γεγονός ότι τα συστήματα SIEM είναι ένας ισχυρός μηχανισμός, μπορεί να είναι όσο αποτελεσματικός όσο πολύτιμες είναι οι πληροφορίες με τις οποίες τροφοδοτείται. Δεδομένου ότι η αύξηση της απόδοσης ενός SIEM περιορίζεται στην αύξηση του αριθμού των συσκευών ασφαλείας που στέλνουν τις καταγραφές (logs) τους σε αυτό και την βελτιστοποίηση του μηχανισμού συσχέτιστης (correlation engine), ο μόνος τρόπος για να ενισχυθεί πραγματικά η απόδοσή του είναι η τροφοδοτησή του με εξωτερικές ως προς τον οργανισμό πληροφορίες. Προτείνουμε τη χρήση honeypots υψηλής αλληλεπίδρασης για την δημιουργία τοπικής νοημοσύνης και τον συσχετισμό τους με γεγονότα που παράγονται στο περιβάλλον ενός πραγματικού οργανισμού. Με τη χρήση εκτενών καταγραφών είμαστε σε θέση να αναγνωρίσουμε τη μη φυσιολογική συμπεριφορά που παράγεται από μη αναγνωρισμένες ως τώρα απειλές σε ποικίλες συσκευές ασφαλείας (security devices) στο δίκτυο του οργανισμού.Nowadays, Security Information and Event Management (SIEM) systems are an integral part of an organization’s security infrastructure. Although SIEM is a powerful mechanism, it can be as effective as valuable the information fed into. Given that a SIEM’s optimization is limited to the increase of the number of security devices reporting to it and fine-tune the correlation engine, the only way to truly enhance its performance is the use of external intelligence. We propose the use of high interaction Honeypots to create domestic intelligence and correlate events produced in a real organization's environment. By using extensive logging we are able to identify abnormal behavior produced by ignored threats in multiple devices in the organization's network

Cyber Infrastructure Protection: Vol. II

View the Executive SummaryIncreased reliance on the Internet and other networked systems raise the risks of cyber attacks that could harm our nation’s cyber infrastructure. The cyber infrastructure encompasses a number of sectors including: the nation’s mass transit and other transportation systems; banking and financial systems; factories; energy systems and the electric power grid; and telecommunications, which increasingly rely on a complex array of computer networks, including the public Internet. However, many of these systems and networks were not built and designed with security in mind. Therefore, our cyber infrastructure contains many holes, risks, and vulnerabilities that may enable an attacker to cause damage or disrupt cyber infrastructure operations. Threats to cyber infrastructure safety and security come from hackers, terrorists, criminal groups, and sophisticated organized crime groups; even nation-states and foreign intelligence services conduct cyber warfare. Cyber attackers can introduce new viruses, worms, and bots capable of defeating many of our efforts. Costs to the economy from these threats are huge and increasing. Government, business, and academia must therefore work together to understand the threat and develop various modes of fighting cyber attacks, and to establish and enhance a framework to assess the vulnerability of our cyber infrastructure and provide strategic policy directions for the protection of such an infrastructure. This book addresses such questions as: How serious is the cyber threat? What technical and policy-based approaches are best suited to securing telecommunications networks and information systems infrastructure security? What role will government and the private sector play in homeland defense against cyber attacks on critical civilian infrastructure, financial, and logistical systems? What legal impediments exist concerning efforts to defend the nation against cyber attacks, especially in preventive, preemptive, and retaliatory actions?https://press.armywarcollege.edu/monographs/1527/thumbnail.jp

Automatic Configuration of Programmable Logic Controller Emulators

Programmable logic controllers (PLCs), which are used to control much of the world\u27s critical infrastructures, are highly vulnerable and exposed to the Internet. Many efforts have been undertaken to develop decoys, or honeypots, of these devices in order to characterize, attribute, or prevent attacks against Industrial Control Systems (ICS) networks. Unfortunately, since ICS devices typically are proprietary and unique, one emulation solution for a particular vendor\u27s model will not likely work on other devices. Many previous efforts have manually developed ICS honeypots, but it is a very time intensive process. Thus, a scalable solution is needed in order to automatically configure PLC emulators. The ScriptGenE Framework presented in this thesis leverages several techniques used in reverse engineering protocols in order to automatically configure PLC emulators using network traces. The accuracy, flexibility, and efficiency of the ScriptGenE Framework is tested in three fully automated experiments

Wide spectrum attribution: Using deception for attribution intelligence in cyber attacks

Modern cyber attacks have evolved considerably. The skill level required to conduct a cyber attack is low. Computing power is cheap, targets are diverse and plentiful. Point-and-click crimeware kits are widely circulated in the underground economy, while source code for sophisticated malware such as Stuxnet is available for all to download and repurpose. Despite decades of research into defensive techniques, such as firewalls, intrusion detection systems, anti-virus, code auditing, etc, the quantity of successful cyber attacks continues to increase, as does the number of vulnerabilities identified. Measures to identify perpetrators, known as attribution, have existed for as long as there have been cyber attacks. The most actively researched technical attribution techniques involve the marking and logging of network packets. These techniques are performed by network devices along the packet journey, which most often requires modification of existing router hardware and/or software, or the inclusion of additional devices. These modifications require wide-scale infrastructure changes that are not only complex and costly, but invoke legal, ethical and governance issues. The usefulness of these techniques is also often questioned, as attack actors use multiple stepping stones, often innocent systems that have been compromised, to mask the true source. As such, this thesis identifies that no publicly known previous work has been deployed on a wide-scale basis in the Internet infrastructure. This research investigates the use of an often overlooked tool for attribution: cyber de- ception. The main contribution of this work is a significant advancement in the field of deception and honeypots as technical attribution techniques. Specifically, the design and implementation of two novel honeypot approaches; i) Deception Inside Credential Engine (DICE), that uses policy and honeytokens to identify adversaries returning from different origins and ii) Adaptive Honeynet Framework (AHFW), an introspection and adaptive honeynet framework that uses actor-dependent triggers to modify the honeynet envi- ronment, to engage the adversary, increasing the quantity and diversity of interactions. The two approaches are based on a systematic review of the technical attribution litera- ture that was used to derive a set of requirements for honeypots as technical attribution techniques. Both approaches lead the way for further research in this field

An integrated networkbased mobile botnet detection system

The increase in the use of mobile devices has made them target for attackers, through the use of sophisticated malware. One of the most significant types of such malware is mobile botnets. Due to their continually evolving nature, botnets are difficult to tackle through signature and traditional anomaly based detection methods. Machine learning techniques have also been used for this purpose. However, the study of their effectiveness has shown methodological weaknesses that have prevented the emergence of conclusive and thorough evidence about their merit. To address this problem, in this thesis we propose a mobile botnet detection system, called MBotCS and report the outcomes of a comprehensive experimental study of mobile botnet detection using supervised machine learning techniques to analyse network traffic and system calls on Android mobile devices. The research covers a range of botnet detection scenarios that is wider from what explored so far, explores atomic and box learning algorithms, and investigates thoroughly the sensitivity of the algorithm performance on different factors (algorithms, features of network traffic, system call data aggregation periods, and botnets vs normal applications and so on). These experiments have been evaluated using real mobile device traffic, and system call captured from Android mobile devices, running normal apps and mobile botnets. The experiments study has several superiorities comparing with existing research. Firstly, experiments use not only atomic but also box ML classifiers. Secondly, a comprehensive set of Android mobile botnets, which had not been considered previously, without relying on any form of synthetic training data. Thirdly, experiments contain a wider set of detection scenarios including unknown botnets and normal applications. Finally, experiments include the statistical significance of differences in detection performance measures with respect to different factors. The study resulted in positive evidence about the effectiveness of the supervised learning approach, as a solution to the mobile botnet detection problem

Tracking and Mitigation of Malicious Remote Control Networks

Attacks against end-users are one of the negative side effects of today’s networks. The goal of the attacker is to compromise the victim’s machine and obtain control over it. This machine is then used to carry out denial-of-service attacks, to send out spam mails, or for other nefarious purposes. From an attacker’s point of view, this kind of attack is even more efficient if she manages to compromise a large number of machines in parallel. In order to control all these machines, she establishes a "malicious remote control network", i.e., a mechanism that enables an attacker the control over a large number of compromised machines for illicit activities. The most common type of these networks observed so far are so called "botnets". Since these networks are one of the main factors behind current abuses on the Internet, we need to find novel approaches to stop them in an automated and efficient way. In this thesis we focus on this open problem and propose a general root cause methodology to stop malicious remote control networks. The basic idea of our method consists of three steps. In the first step, we use "honeypots" to collect information. A honeypot is an information system resource whose value lies in unauthorized or illicit use of that resource. This technique enables us to study current attacks on the Internet and we can for example capture samples of autonomous spreading malware ("malicious software") in an automated way. We analyze the collected data to extract information about the remote control mechanism in an automated fashion. For example, we utilize an automated binary analysis tool to find the Command & Control (C&C) server that is used to send commands to the infected machines. In the second step, we use the extracted information to infiltrate the malicious remote control networks. This can for example be implemented by impersonating as a bot and infiltrating the remote control channel. Finally, in the third step we use the information collected during the infiltration phase to mitigate the network, e.g., by shutting down the remote control channel such that the attacker cannot send commands to the compromised machines. In this thesis we show the practical feasibility of this method. We examine different kinds of malicious remote control networks and discuss how we can track all of them in an automated way. As a first example, we study botnets that use a central C&C server: We illustrate how the three steps can be implemented in practice and present empirical measurement results obtained on the Internet. Second, we investigate botnets that use a peer-to-peer based communication channel. Mitigating these botnets is harder since no central C&C server exists which could be taken offline. Nevertheless, our methodology can also be applied to this kind of networks and we present empirical measurement results substantiating our method. Third, we study fast-flux service networks. The idea behind these networks is that the attacker does not directly abuse the compromised machines, but uses them to establish a proxy network on top of these machines to enable a robust hosting infrastructure. Our method can be applied to this novel kind of malicious remote control networks and we present empirical results supporting this claim. We anticipate that the methodology proposed in this thesis can also be used to track and mitigate other kinds of malicious remote control networks