Identifying the root cause and impact of a system intrusion remains a foundational challenge in computer security. Digital provenance provides a detailed history of the flow of information within a computing system, connecting suspicious events to their root causes. Although existing provenance-based auditing techniques provide value in forensic analysis, they assume that such analysis takes place only retrospectively. Such post-hoc analysis is insufficient for realtime security applications, moreover, even for forensic tasks, prior provenance collection systems exhibited poor performance and scalability, jeopardizing the timeliness of query responses. We present CamQuery, which provides inline, realtime provenance analysis, making it suitable for implementing security applications. CamQuery is a Linux Security Module that offers support for both userspace and in-kernel execution of analysis applications. We demonstrate the applicability of CamQuery to a variety of runtime security applications including data loss prevention, intrusion detection, and regulatory compliance. In evaluation, we demonstrate that CamQuery reduces the latency of realtime query mechanisms, while imposing minimal overheads on system execution. CamQuery thus enables the further deployment of provenance-based technologies to address central challenges in computer security.
Security Monitoring. In today's enterprise environments, security incidents occur when a primary indicator of compromise is triggered from security monitoring software such as an anti-virus detection alert or a blacklisted URL in the organisation's network logs @cite_81 . In current security products, such indicators report only limited context as to the circumstances under which the alert occurred, process ID or packet header information, but do not report the historical chain of events that led to the suspicious activity. Past work has attempted to compensate for this lack of lineage through the fusion @cite_60 @cite_73 or correlation @cite_78 @cite_110 @cite_52 @cite_91 of multiple indicators of the compromise. However, it does not address the fundamental limitation that security monitoring tools lack the ability to reason over the entire context of a system execution. Thus, attack reconstruction has typically been relegated to (offline) forensic analysis @cite_11 @cite_24 @cite_71 @cite_86 @cite_4 @cite_48 @cite_105 @cite_30 @cite_90 . In contrast, provides a mechanism to build runtime security monitoring based on the entire history of system execution, thus representing a significant step forward compared to the state-of-the-art.
@cite_30 @cite_4 @cite_91 @cite_78 @cite_60 @cite_48 @cite_90 @cite_52 @cite_24 @cite_81 @cite_71 @cite_110 @cite_86 @cite_73 @cite_105 @cite_11
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In order to understand the security level of an organization network, detection methods are important to tackle the probable risks of the attackers' malicious activities. Intrusion detection systems, as detection solutions of the defense in depth concept, are one of the main devices to record and analyze suspicious behaviors. Besides the benefits of these systems for security enhancement, they will bring some challenges and issues for security administrators. A large number of raw alerts generated by the intrusion detection systems clearly reflect the need for a novel proactive alert correlation framework to reduce redundant alerts, correlate security incidents, discover and model multi-step attack scenarios, and track them. Several alert correlation frameworks have been proposed in the literature, but the majority of them address the alert correlation in the offline settings. In this paper, we propose a three-phase alert correlation framework, which processes the generated alerts in real time, correlates the alerts with the aid of causal knowledge discovery to automatically extract causal relationships between alerts, constructs the attack scenarios using the Bayesian network concept, and predicts the next goal of the attacks using the creating attack prediction rules. Experimental results show that the scalable proposed framework is efficient enough in learning and detecting known and unknown multi-step attack scenarios without using any predefined knowledge. The results also show that the proposed framework perfectly estimates complex attacks before they can damage the assets of the network. Copyright © 2016 John Wiley & Sons, Ltd. As more and more Internet-based attacks arise, organizations are responding by deploying an assortment of security products that generate situational intelligence in the form of logs. These logs often contain high volumes of interesting and useful information about activities in the network, and are among the first data sources that information security specialists consult when they suspect that an attack has taken place. However, security products often come from a patchwork of vendors, and are inconsistently installed and administered. They generate logs whose formats differ widely and that are often incomplete, mutually contradictory, and very large in volume. Hence, although this collected information is useful, it is often dirty. We present a novel system, Beehive, that attacks the problem of automatically mining and extracting knowledge from the dirty log data produced by a wide variety of security products in a large enterprise. We improve on signature-based approaches to detecting security incidents and instead identify suspicious host behaviors that Beehive reports as potential security incidents. These incidents can then be further analyzed by incident response teams to determine whether a policy violation or attack has occurred. We have evaluated Beehive on the log data collected in a large enterprise, EMC, over a period of two weeks. We compare the incidents identified by Beehive against enterprise Security Operations Center reports, antivirus software alerts, and feedback from enterprise security specialists. We show that Beehive is able to identify malicious events and policy violations which would otherwise go undetected. As antivirus and network intrusion detection systems have increasingly proven insufficient to detect advanced threats, large security operations centers have moved to deploy endpoint-based sensors that provide deeper visibility into low-level events across their enterprises. Unfortunately, for many organizations in government and industry, the installation, maintenance, and resource requirements of these newer solutions pose barriers to adoption and are perceived as risks to organizations' missions. To mitigate this problem we investigated the utility of agentless detection of malicious endpoint behavior, using only the standard built-in Windows audit logging facility as our signal. We found that Windows audit logs, while emitting manageable sized data streams on the endpoints, provide enough information to allow robust detection of malicious behavior. Audit logs provide an effective, low-cost alternative to deploying additional expensive agent-based breach detection systems in many government and industrial settings, and can be used to detect, in our tests, 83 percent of malware samples with a 0.1 false positive rate. They can also supplement already existing host signature-based antivirus solutions, like Kaspersky, Symantec, and McAfee, detecting, in our testing environment, 78 of malware missed by those antivirus systems. Security researchers can send vulnerability notifications to take proactive measures in securing systems at scale. However, the factors affecting a notification’s efficacy have not been deeply explored. In this paper, we report on an extensive study of notifying thousands of parties of security issues present within their networks, with an aim of illuminating which fundamental aspects of notifications have the greatest impact on efficacy. The vulnerabilities used to drive our study span a range of protocols and considerations: exposure of industrial control systems; apparent firewall omissions for IPv6-based services; and exploitation of local systems in DDoS amplification attacks. We monitored vulnerable systems for several weeks to determine their rate of remediation. By comparing with experimental controls, we analyze the impact of a number of variables: choice of party to contact (WHOIS abuse contacts versus national CERTs versus US-CERT), message verbosity, hosting an information website linked to in the message, and translating the message into the notified party’s local language. We also assess the outcome of the emailing process itself (bounces, automated replies, human replies, silence) and characterize the sentiments and perspectives expressed in both the human replies and an optional anonymous survey that accompanied our notifications. We find that various notification regimens do result in different outcomes. The best observed process was directly notifying WHOIS contacts with detailed information in the message itself. These notifications had a statistically significant impact on improving remediation, and human replies were largely positive. However, the majority of notified contacts did not take action, and even when they did, remediation was often only partial. Repeat notifications did not further patching. These results are promising but ultimately modest, behooving the security community to more deeply investigate ways to improve the effectiveness of vulnerability notifications.