SecSip: A Stateful Firewall for SIP-based Networks

SIP-based networks are becoming the de-facto standard for voice, video and instant messaging services. Being exposed to many threats while playing an major role in the operation of essential services, the need for dedicated security management approaches is rapidly increasing. In this paper we present an original security management approach based on a specific vulnerability aware SIP stateful firewall. Through known attack descriptions, we illustrate the power of the configuration language of the firewall which uses the capability to specify stateful objects that track data from multiple SIP elements within their lifetime. We demonstrate through measurements on a real implementation of the firewall its efficiency and performance

Preventing Distributed Denial-of-Service Attacks on the IMS Emergency Services Support through Adaptive Firewall Pinholing

Emergency services are vital services that Next Generation Networks (NGNs) have to provide. As the IP Multimedia Subsystem (IMS) is in the heart of NGNs, 3GPP has carried the burden of specifying a standardized IMS-based emergency services framework. Unfortunately, like any other IP-based standards, the IMS-based emergency service framework is prone to Distributed Denial of Service (DDoS) attacks. We propose in this work, a simple but efficient solution that can prevent certain types of such attacks by creating firewall pinholes that regular clients will surely be able to pass in contrast to the attackers clients. Our solution was implemented, tested in an appropriate testbed, and its efficiency was proven.Comment: 17 Pages, IJNGN Journa

A hybrid and cross-protocol architecture with semantics and syntax awareness to improve intrusion detection efficiency in Voice over IP environments

Includes abstract.Includes bibliographical references (leaves 134-140).Voice and data have been traditionally carried on different types of networks based on different technologies, namely, circuit switching and packet switching respectively. Convergence in networks enables carrying voice, video, and other data on the same packet-switched infrastructure, and provides various services related to these kinds of data in a unified way. Voice over Internet Protocol (VoIP) stands out as the standard that benefits from convergence by carrying voice calls over the packet-switched infrastructure of the Internet. Although sharing the same physical infrastructure with data networks makes convergence attractive in terms of cost and management, it also makes VoIP environments inherit all the security weaknesses of Internet Protocol (IP). In addition, VoIP networks come with their own set of security concerns. Voice traffic on converged networks is packet-switched and vulnerable to interception with the same techniques used to sniff other traffic on a Local Area Network (LAN) or Wide Area Network (WAN). Denial of Service attacks (DoS) are among the most critical threats to VoIP due to the disruption of service and loss of revenue they cause. VoIP systems are supposed to provide the same level of security provided by traditional Public Switched Telephone Networks (PSTNs), although more functionality and intelligence are distributed to the endpoints, and more protocols are involved to provide better service. A new design taking into consideration all the above factors with better techniques in Intrusion Detection are therefore needed. This thesis describes the design and implementation of a host-based Intrusion Detection System (IDS) that targets VoIP environments. Our intrusion detection system combines two types of modules for better detection capabilities, namely, a specification-based and a signaturebased module. Our specification-based module takes the specifications of VoIP applications and protocols as the detection baseline. Any deviation from the protocol’s proper behavior described by its specifications is considered anomaly. The Communicating Extended Finite State Machines model (CEFSMs) is used to trace the behavior of the protocols involved in VoIP, and to help exchange detection results among protocols in a stateful and cross-protocol manner. The signature-based module is built in part upon State Transition Analysis Techniques which are used to model and detect computer penetrations. Both detection modules allow for protocol-syntax and protocol-semantics awareness. Our intrusion detection uses the aforementioned techniques to cover the threats propagated via low-level protocols such as IP, ICMP, UDP, and TCP

A Comprehensive Survey of Voice over IP Security Research

We present a comprehensive survey of Voice over IP security academic research, using a set of 245 publications forming a closed cross-citation set. We classify these papers according to an extended version of the VoIP Security Alliance (VoIPSA) Threat Taxonomy. Our goal is to provide a roadmap for researchers seeking to understand existing capabilities and to identify gaps in addressing the numerous threats and vulnerabilities present in VoIP systems. We discuss the implications of our findings with respect to vulnerabilities reported in a variety of VoIP products. We identify two specific problem areas (denial of service, and service abuse) as requiring significant more attention from the research community. We also find that the overwhelming majority of the surveyed work takes a black box view of VoIP systems that avoids examining their internal structure and implementation. Such an approach may miss the mark in terms of addressing the main sources of vulnerabilities, i.e., implementation bugs and misconfigurations. Finally, we argue for further work on understanding cross-protocol and cross-mechanism vulnerabilities (emergent properties), which are the byproduct of a highly complex system-of-systems and an indication of the issues in future large-scale systems

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

Non-Conforming Behavior Detection for VoIP-Based Network Systems

This work proposes a detection scheme that identifies non-conforming behavior in a VoIP network, based on statistical analysis and hypothesis testing. VoIP networks are a popular, low-cost alternative for telephony that offer lower rates especially for long-distance calls. Other services such as FollowMe, enhance the traditional voice-oriented nature of these networks. Consequently several security concerns such as fraud calls, are related to the high availability required by a VoIP system. Fraud calls account for an average loss of 3% to 5% of the operators’ revenue. Thus the detection and prevention of the users from behaving in a non-conforming way, becomes crucial. A trustworthy and secure management and billing scheme is necessary, to guarantee the proper operation. This work proposes a behavioral control scheme for the VoIP clients. An initial training period defines the normal behavior. Then statistical analysis and t-testing is employed to extract results regarding the users’ profiles, with pre-defined confidence levels. A Buffer zone creates a more flexible decision-making process. The scheme also offers the ability to configure its parameters, in order to react appropriately under different network conditions and detect possible misuses. It is implemented and its rational operation is verified via several simulation scenarios

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

Addressing Insider Threats from Smart Devices

Smart devices have unique security challenges and are becoming increasingly common. They have been used in the past to launch cyber attacks such as the Mirai attack. This work is focused on solving the threats posed to and by smart devices inside a network. The size of the problem is quantified; the initial compromise is prevented where possible, and compromised devices are identified. To gain insight into the size of the problem, campus Domain Name System (DNS) measurements were taken that allow for wireless traffic to be separated from wired traffic. Two-thirds of the DNS traffic measured came from wireless hosts, implying that mobile devices are playing a bigger role in networks. Also, port scans and service discovery protocols were used to identify Internet of Things (IoT) devices on the campus network and follow-up work was done to assess the state of the IoT devices. Motivated by these findings, three solutions were developed. To handle the scenario when compromised mobile devices are connected to the network, a new strategy for steppingstone detection was developed with both an application layer and a transport layer solution. The proposed solution is effective even when the mobile device cellular connection is used. Also, malicious or vulnerable applications make it through the mobile app store vetting process. A user space tool was developed that identifies apps contacting malicious domains in real time and collects data for research purposes. Malicious app behavior can then be identified on the user’s device, catching malicious apps that were overlooked by software vetting. Last, the variety of IoT device types and manufacturers makes the job of keeping them secure difficult. A generic framework was developed to lighten the management burden of securing IoT devices, serve as a middle box to secure legacy devices, and also use DNS queries as a way to identify misbehaving devices

Enforcing Security with Behavioral Fingerprinting

International audienceAlthough fingerprinting techniques are helpful for security assessment, they have limited support to advanced security related applications. We have developed a new security framework focusing especially on the authentication reinforce- ment and the automatic generation of stateful firewall rules based on behavioral fingerprinting. Such fingerprinting is highly effective in capturing sequential patterns in the behavior of a device. A new machine learning technique is also adapted to monitor high speed networks by evaluating both computational complexity and experimented performances