A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

Adaptive conflict-free optimization of rule sets for network security packet filtering devices

Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices

SUTMS - Unified Threat Management Framework for Home Networks

Home networks were initially designed for web browsing and non-business critical applications. As infrastructure improved, internet broadband costs decreased, and home internet usage transferred to e-commerce and business-critical applications. Today’s home computers host personnel identifiable information and financial data and act as a bridge to corporate networks via remote access technologies like VPN. The expansion of remote work and the transition to cloud computing have broadened the attack surface for potential threats. Home networks have become the extension of critical networks and services, hackers can get access to corporate data by compromising devices attacked to broad- band routers. All these challenges depict the importance of home-based Unified Threat Management (UTM) systems. There is a need of unified threat management framework that is developed specifically for home and small networks to address emerging security challenges. In this research, the proposed Smart Unified Threat Management (SUTMS) framework serves as a comprehensive solution for implementing home network security, incorporating firewall, anti-bot, intrusion detection, and anomaly detection engines into a unified system. SUTMS is able to provide 99.99% accuracy with 56.83% memory improvements. IPS stands out as the most resource-intensive UTM service, SUTMS successfully reduces the performance overhead of IDS by integrating it with the flow detection mod- ule. The artifact employs flow analysis to identify network anomalies and categorizes encrypted traffic according to its abnormalities. SUTMS can be scaled by introducing optional functions, i.e., routing and smart logging (utilizing Apriori algorithms). The research also tackles one of the limitations identified by SUTMS through the introduction of a second artifact called Secure Centralized Management System (SCMS). SCMS is a lightweight asset management platform with built-in security intelligence that can seamlessly integrate with a cloud for real-time updates

Enhancing snort IDs performance using data mining

Intrusion detection systems (IDSs) such as Snort apply deep packet inspection to detect intrusions. Usually, these are rule-based systems, where each incoming packet is matched with a set of rules. Each rule consists of two parts: the rule header and the rule options. The rule header is compared with the packet header. The rule options usually contain a signature string that is matched with packet content using an efficient string matching algorithm. The traditional approach to IDS packet inspection checks a packet against the detection rules by scanning from the first rule in the set and continuing to scan all the rules until a match is found. This approach becomes inefficient if the number of rules is too large and if the majority of the packets match with rules located at the end of the rule set. In this thesis, we propose an intelligent predictive technique for packet inspection based on data mining. We consider each rule in a rule set as a ‘class’. A classifier is first trained with labeled training data. Each such labeled data point contains packet header information, packet content summary information, and the corresponding class label (i.e. the rule number with which the packet matches). Then the classifier is used to classify new incoming packets. The predicted class, i.e. rule, is checked against the packet to see if this packet really matches the predicted rule. If it does, the corresponding action (i.e. alert) of the rule is taken. Otherwise, if the prediction of the classifier is wrong, we go back to the traditional way of matching rules. The advantage of this intelligent predictive packet matching is that it offers much faster rule matching. We have proved, both analytically and empirically, that even with millions of real network traffic packets and hundreds of rules, the classifier can achieve very high accuracy, thereby making the IDS several times faster in making matching decisions

Automatic Inference of High-Level Network Intents by Mining Forwarding Patterns

There is a semantic gap between the high-level intents of network operators and the low-level configurations that achieve the intents. Previous works tried to bridge the gap using verification or synthesis techniques, both requiring formal specifications of the intended behavior which are rarely available or even known in the real world. This paper discusses an alternative approach for bridging the gap, namely to infer the high-level intents from the low-level network behavior. Specifically, we provide Anime, a framework and a tool that given a set of observed forwarding behavior, automatically infers a set of possible intents that best describe all observations. Our results show that Anime can infer high-quality intents from the low-level forwarding behavior with acceptable performance.Comment: SOSR 202

A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this paper, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including distributed denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing methods in monitoring and classifying network behavior of enterprise hosts to verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers are elaborated, highlighting their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we highlight several research gaps on enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive