56,994 research outputs found

    Distributed Port Scanning Detection

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    Conventional Network Intrusion Detection System (NIDS) have heavyweight processing and memory requirements as they maintain per flow state using data structures like linked lists or trees. This is required for some specialized jobs such as Stateful Packet Inspection (SPI) where the network communications between entities are recreated in its entirety to inspect application level data. The downside to this approach is that the NIDS must be in a position to view all inbound and outbound traffic of the protected network. The NIDS can be overwhelmed by a DDoS attack since most of these try and exhaust the available state of network entities. For some applications like port scan detection, we do not require to reconstruct the complete network tra�c. We propose to integrate a detector into all routers so that a more distributed detection approach can be achieved. Since routers are devices with limited memory and processing capabilities, conventional NIDS approaches do not work while integrating a detector in them. We describe a method to detect port scans using aggregation. A data structure called a Partial Completion Filter(PCF) or a counting Bloom filter is used to reduce the per flow state

    Scan Correlation -- Revealing distributed scan campaigns

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    Public networks are exposed to port scans from the Internet. Attackers search for vulnerable services they can exploit. In large scan campaigns, attackers often utilize different machines to perform distributed scans, which impedes their detection and might also camouflage the actual goal of the scanning campaign. In this paper, we present a correlation algorithm to detect scans, identify potential relations among them, and reassemble them to larger campaigns. We evaluate our approach on real-world Internet traffic and our results indicate that it can summarize and characterize standalone and distributed scan campaigns based on their tools and intention.Comment: Accepted for publication at DISSECT '2

    Impregnable Defence Architecture using Dynamic Correlation-based Graded Intrusion Detection System for Cloud

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    Data security and privacy are perennial concerns related to cloud migration, whether it is about applications, business or customers. In this paper, novel security architecture for the cloud environment designed with intrusion detection and prevention system (IDPS) components as a graded multi-tier defense framework. It is a defensive formation of collaborative IDPS components with dynamically revolving alert data placed in multiple tiers of virtual local area networks (VLANs). The model has two significant contributions for impregnable protection, one is to reduce alert generation delay by dynamic correlation and the second is to support the supervised learning of malware detection through system call analysis. The defence formation facilitates malware detection with linear support vector machine- stochastic gradient descent (SVM-SGD) statistical algorithm. It requires little computational effort to counter the distributed, co-ordinated attacks efficiently. The framework design, then, takes distributed port scan attack as an example for assessing the efficiency in terms of reduction in alert generation delay, the number of false positives and learning time through comparison with existing techniques is discussed

    Uncovering Vulnerable Industrial Control Systems from the Internet Core

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    Industrial control systems (ICS) are managed remotely with the help of dedicated protocols that were originally designed to work in walled gardens. Many of these protocols have been adapted to Internet transport and support wide-area communication. ICS now exchange insecure traffic on an inter-domain level, putting at risk not only common critical infrastructure but also the Internet ecosystem (e.g., DRDoS~attacks). In this paper, we uncover unprotected inter-domain ICS traffic at two central Internet vantage points, an IXP and an ISP. This traffic analysis is correlated with data from honeypots and Internet-wide scans to separate industrial from non-industrial ICS traffic. We provide an in-depth view on Internet-wide ICS communication. Our results can be used i) to create precise filters for potentially harmful non-industrial ICS traffic, and ii) to detect ICS sending unprotected inter-domain ICS traffic, being vulnerable to eavesdropping and traffic manipulation attacks

    Know Your Enemy: Stealth Configuration-Information Gathering in SDN

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    Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideratio

    Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination
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