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Access control configurations are gatekeepers to block unwelcome access to sensitive data. Unfortunately, system administrators (sysadmins) sometimes over-grant permissions when resolving unintended access-deny issues reported by legitimate users, which may open up security vulnerabilities for attackers. One of the primary reasons is that modern software does not provide informative logging to guide sysadmins to understand the reported problems. This paper makes one of the first attempts (to the best of our knowledge) to help developers improve log messages in order to help sysadmins correctly understand and fix access-deny issues without over-granting permissions. First, we conducted an observation study to understand the current practices of access-deny logging in the server software. Our study shows that many access-control program locations do not have any log messages; and a large percentage of existing log messages lack useful information to guide sysadmins to correctly understand and fix the issues. On top of our observations, we built SECLOG, which uses static analysis to automatically help developers find missing access-deny log locations and identify relevant information at the log location. We evaluated SECLOG with ten widely deployed server applications. Overall, SECLOG identified 380 new log statements for access-deny cases, and also enhanced 550 existing access-deny log messages with diagnostic information. We have reported 114 log statements to the developers of these applications, and so far 70 have been accepted into their main branches. We also conducted a user study with sysadmins (n=32) on six real-world access-deny issues. SECLOG can reduce the number of insecure fixes from 27 to 1, and also improve the diagnosis time by 64.2% on average.
Voice data generated on instant messaging or social media applications contains unique user voiceprints that may be abused by malicious adversaries for identity inference or identity theft. Existing voice anonymization techniques, e.g., signal processing and voice conversion/synthesis, suffer from degradation of perceptual quality. In this paper, we develop a voice anonymization system, named V-Cloak, which attains real-time voice anonymization while preserving the intelligibility, naturalness and timbre of the audio. Our designed anonymizer features a one-shot generative model that modulates the features of the original audio at different frequency levels. We train the anonymizer with a carefully-designed loss function. Apart from the anonymity loss, we further incorporate the intelligibility loss and the psychoacoustics-based naturalness loss. The anonymizer can realize untargeted and targeted anonymization to achieve the anonymity goals of unidentifiability and unlinkability. We have conducted extensive experiments on four datasets, i.e., LibriSpeech (English), AISHELL (Chinese), CommonVoice (French) and CommonVoice (Italian), five Automatic Speaker Verification (ASV) systems (including two DNN-based, two statistical and one commercial ASV), and eleven Automatic Speech Recognition (ASR) systems (for different languages). Experiment results confirm that V-Cloak outperforms five baselines in terms of anonymity performance. We also demonstrate that V-Cloak trained only on the VoxCeleb1 dataset against ECAPA-TDNN ASV and DeepSpeech2 ASR has transferable anonymity against other ASVs and cross-language intelligibility for other ASRs. Furthermore, we verify the robustness of V-Cloak against various de-noising techniques and adaptive attacks. Hopefully, V-Cloak may provide a cloak for us in a prism world.
Modern software is continuously patched to fix bugs and security vulnerabilities. Patching is particularly important in robotic vehicles (RVs), in which safety and security bugs can cause severe physical damages. However, existing automated methods struggle to identify faulty patches in RVs, due to their inability to systematically determine patch-introduced behavioral modifications, which affect how the RV interacts with the physical environment. In this paper, we introduce PATCHVERIF, an automated patch analysis framework. PATCHVERIF’s goal is to evaluate whether a given patch introduces bugs in the patched RV control software. To this aim, PATCHVERIF uses a combination of static and dynamic analysis to measure how the analyzed patch affects the physical state of an RV. Specifically, PATCHVERIF uses a dedicated input mutation algorithm to generate RV inputs that maximize the behavioral differences (in the physical space) between the original code and the patched one. Using the collected information about patch-introduced behavioral modifications, PATCHVERIF employs support vector machines (SVMs) to infer whether a patch is faulty or correct. We evaluated PATCHVERIF on two popular RV control software (ArduPilot and PX4), and it successfully identified faulty patches with an average precision and recall of 97.9% and 92.1%, respectively. Moreover, PATCHVERIF discovered 115 previously unknown bugs, 103 of which have been acknowledged, and 51 of them have already been fixed.
Building provenance graph that considers causal relationships among software behaviors can better provide contextual information of cyber attacks, especially for advanced attacks such as Advanced Persistent Threat (APT) attacks. Despite its promises in assisting attack investigation, existing approaches that use provenance graphs to perform attack detection suffer from two fundamental limitations. First, existing approaches adopt a centralized detection architecture that sends all system auditing logs to the server for processing, incurring intolerable costs of data transmission, data storage, and computation. Second, they adopt either rule-based techniques that cannot detect unknown threats, or anomaly-detection techniques that produce numerous false alarms, failing to achieve a balance of precision and recall in APT detection. To address these fundamental challenges, we propose DISTDET, a distributed detection system that detects APT attacks by (1) performing light weight detection based on the host model built in the client side, (2) filtering false alarms based on the semantics of the alarm proprieties, and (3) deriving global models to complement the local bias of the host models. Our experiments on a large-scale industrial environment (1,130 hosts, 14 days, ∼1.6 billion events) and the DARPA TC dataset show that DISTDET is as effective as sate-of-the-art techniques in detecting attacks, while dramatically reducing network bandwidth from 11.28Mb/s to 17.08Kb/S (676.5× reduction), memory usages from 364MB to 5.523MB (66× reduction), and storage from 1.47GB to 130.34MB (11.6× reduction). By the time of this writing, DISTDET has been deployed to 50+ industry customers with 22,000+ hosts for more than 6 months, and identified over 900 real-world attacks.
USB is the most prevalent peripheral interface in modern computer systems and its inherent insecurities make it an appealing attack vector. A well-known limitation of USB is that traffic is not encrypted. This allows on-path adversaries to trivially perform man-in-the-middle attacks. Off-path attacks that compromise the confidentiality of communications have also been shown to be possible. However, so far no off-path attacks that breach USB communications integrity have been demonstrated. In this work we show that the integrity of USB communications is not guaranteed even against off-path attackers. Specifically, we design and build malicious devices that, even when placed outside of the path between a victim device and the host, can inject data to that path. Using our developed injectors we can falsify the provenance of data input as interpreted by a host computer system. By injecting on behalf of trusted victim devices we can circumvent any software-based authorisation policy defences that computer systems employ against common USB attacks. We demonstrate two concrete attacks. The first injects keystrokes allowing an attacker to execute commands. The second demonstrates file-contents replacement including during system install from a USB disk. We test the attacks on 29 USB 2.0 and USB 3.x hubs and find 14 of them to be vulnerable.
Today's digital communication relies on complex protocols and specifications for exchanging structured messages and data. Communication naturally involves two endpoints: One generating data and one consuming it. Traditional fuzz testing approaches replace one endpoint, the generator, with a fuzzer and rapidly test many mutated inputs on the target program under test. While this fully automated approach works well for loosely structured formats, this does not hold for highly structured formats, especially those that go through complex transformations such as compression or encryption. In this work, we propose a novel perspective on generating inputs in highly complex formats without relying on heavyweight program analysis techniques, coarse-grained grammar approximation, or a human domain expert. Instead of mutating the inputs for a target program, we inject faults into the data generation program so that this data is almost of the expected format. Such data bypasses the initial parsing stages in the consumer program and exercises deeper program states, where it triggers more interesting program behavior. To realize this concept, we propose a set of compile-time and run-time analyses to mutate the generator in a targeted manner, so that it remains intact and produces semi-valid outputs that satisfy the constraints of the complex format. We have implemented this approach in a prototype called Fuzztruction and show that it outperforms the state-of-the-art fuzzers AFL++, SYMCC, and WEIZZ. Fuzztruction finds significantly more coverage than existing methods, especially on targets that use cryptographic primitives. During our evaluation, Fuzztruction uncovered 151 unique crashes (after automated deduplication). So far, we manually triaged and reported 27 bugs and 4 CVEs were assigned.
We study microarchitectural side-channel attacks and defenses on non-volatile RAM (NVRAM) DIMMs. In this study, we first perform reverse-engineering of NVRAMs as implemented by the Intel Optane DIMM and reveal several of its previously undocumented microarchitectural details: on-DIMM cache structures (NVCache) and wear-leveling policies. Based on these findings, we first develop cross-core and cross-VM covert channels to establish the channel capacity of these shared hardware resources. Then, we devise NVCache-based side channels under the umbrella of NVLeak. We apply NVLeak to a series of attack case studies, including compromising the privacy of databases and key-value storage backed by NVRAM and spying on the execution path of code pages when NVRAM is used as a volatile runtime memory. Our results show that side-channel attacks exploiting NVRAM are practical and defeat previously-proposed defense that only focuses on on-chip hardware resources. To fill this gap in defense, we develop system-level mitigations based on cache partitioning to prevent side-channel leakage from NVCache.
The growth of web-based malware and phishing attacks has catalyzed significant advances in the research and use of interstitial warning pages and modals by a browser prior to loading the content of a suspect site. These warnings commonly use visual cues to attract users' attention, including specialized iconography, color, and the placement and size of buttons to communicate the importance of the scenario. While the efficacy of visual techniques has improved safety for sighted users, these techniques are unsuitable for blind and visually impaired users. We attribute this not to a lack of interest or technical capability by browser manufactures, where universal design is a core tenet of their engineering practices, but instead a reflection of the very real dearth of research literature to inform their choices, exacerbated by a deficit of clear methodologies for conducting studies with this population. Indeed, the challenges are manifold. In this paper, we analyze and address the methodological challenges of conducting security and privacy research with a visually impaired population, and contribute a new set of methodological best practices when conducting a study of this kind. Using our methodology, we conduct a preliminary study analyzing the experiences of the visually impaired with browser security warnings, perform a thematic analysis identifying common challenges visually impaired users experience, and present some initial solutions that could improve security for this population.
Critical software is written in memory unsafe languages that are vulnerable to use-after-free and double free bugs. This has led to proposals to secure memory allocators by strategically deferring memory reallocations long enough to make such bugs unexploitable. Unfortunately, existing solutions suffer from high runtime and memory overheads. Seeking a better solution, we propose to profile programs to identify units of code that correspond to the handling of individual tasks. With the intuition that little to no data should flow between separate tasks at runtime, reallocation of memory freed by the currently executing unit is deferred until after its completion; just long enough to prevent use-after-free exploitation. To demonstrate the efficacy of our design, we implement a prototype for Linux, PUMM, which consists of an offline profiler and an online enforcer that transparently wraps standard libraries to protect C/C++ binaries. In our evaluation of 40 real-world and 3,000 synthetic vulnerabilities across 26 programs, including complex multi-threaded cases like the Chakra JavaScript engine, PUMM successfully thwarts all real-world exploits, and only allows 4 synthetic exploits, while reducing memory overhead by 52.0% over prior work and incurring an average runtime overhead of 2.04%.
Since mobile apps' privacy policies are usually complex, various tools have been developed to examine whether privacy policies have contradictions and verify whether privacy policies are consistent with the apps' behaviors. However, to the best of our knowledge, no prior work answers whether the personal data collection practices (PDCPs) in an app's privacy policy are necessary for given purposes (i.e., whether to comply with the principle of data minimization). Though defined by most existing privacy regulations/laws such as GDPR, the principle of data minimization has been translated into different privacy practices depending on the different contexts (e.g., various developers and targeted users). In the end, the developers can collect personal data claimed in the privacy policies as long as they receive authorizations from the users. Currently, it mainly relies on legal experts to manually audit the necessity of personal data collection according to the specific contexts, which is not very scalable for millions of apps. In this study, we aim to take the first step to automatically investigate whether PDCPs in an app's privacy policy are overbroad from the perspective of counterpart comparison. Our basic insight is that, if an app claims to collect much more personal data in its privacy policy than most of its counterparts, it is more likely to be conducting overbroad collection. To achieve this, POLICYCOMP, an automatic framework for detecting overbroad PDCPs is proposed. We use POLICYCOMP to perform a large-scale analysis on 10,042 privacy policies and flag 48.29% of PDCPs to be overbroad. We shared our findings with 2,000 app developers and received 52 responses from them, 39 of which acknowledged our findings and took actions (e.g., removing overbroad PDCPs).
In this paper, we report MaginotDNS, a powerful cache poisoning attack against DNS servers that simultaneously act as forwarder and recursive resolver (termed as CDNS). The attack is made possible through exploiting vulnerabilities in the bailiwick checking algorithms, one of the cornerstones of DNS security since the 1990s, and affects multiple versions of popular DNS software, including BIND and Microsoft DNS. Through field tests, we find that the attack is potent, allowing attackers to take over entire DNS zones, even including Top-Level Domains (e.g., .com and .net). Through a large-scale measurement study, we also confirm the extensive usage of CDNSes in real-world networks (up to 41.8% of our probed open DNS servers) and find that at least 35.5% of all CDNSes are vulnerable to MaginotDNS. After interviews with ISPs, we show a wide range of CDNS use cases and real-world attacks. We have reported all the discovered vulnerabilities to DNS software vendors and received acknowledgments from all of them. 3 CVE-ids have been assigned, and 2 vendors have fixed their software. Our study brings attention to the implementation inconsistency of security checking logic in different DNS software and server modes (i.e., recursive resolvers and forwarders), and we call for standardization and agreements among software vendors.
We present SimplePIR, the fastest single-server private information retrieval scheme known to date. SimplePIR’s security holds under the learning-with-errors assumption. To answer a client’s query, the SimplePIR server performs fewer than one 32-bit multiplication and one 32-bit addition per database byte. SimplePIR achieves 10 GB/s/core server throughput, which approaches the memory bandwidth of the machine and the performance of the fastest two-server private-information-retrieval schemes (which require non-colluding servers). SimplePIR has relatively large communication costs: to make queries to a 1 GB database, the client must download a 121 MB "hint" about the database contents; thereafter, the client may make an unbounded number of queries, each requiring 242 KB of communication. We present a second single-server scheme, DoublePIR, that shrinks the hint to 16 MB at the cost of slightly higher per-query communication (345 KB) and slightly lower throughput (7.4 GB/s/core). Finally, we apply our new private-information-retrieval schemes, together with a novel data structure for approximate set membership, to the task of private auditing in Certificate Transparency. We achieve a strictly stronger notion of privacy than Google Chrome’s current approach with 13x more communication: 16 MB of download per week, along with 1.5 KB per TLS connection.
Perceptual Manipulation Attacks (PMA) involve manipulating users’ multi-sensory (e.g., visual, auditory, haptic) perceptions of the world through Mixed Reality (MR) content, in order to influence users' judgments and following actions. For example, a MR driving application that is expected to show safety-critical output might also (maliciously or unintentionally) overlay the wrong signal on a traffic sign, misleading the user into slamming on the brake. While current MR technology is sufficient to create such attacks, little research has been done to understand how users perceive, react to, and defend against such potential manipulations. To provide a foundation for understanding and addressing PMA in MR, we conducted an in-person study with 21 participants. We developed three PMA in which we focused on attacking three different perceptions: visual, auditory, and situational awareness. Our study first investigates how user reactions are affected by evaluating their performance on “microbenchmark'' tasks under benchmark and different attack conditions. We observe both primary and secondary impacts from attacks, later impacting participants' performance even under non-attack conditions. We follow up with interviews, surfacing a range of user reactions and interpretations of PMA. Through qualitative data analysis of our observations and interviews, we identify various defensive strategies participants developed, and we observe how these strategies sometimes backfire. We derive recommendations for future investigation and defensive directions based on our findings.
Radio-frequency (RF) energy harvesting is a promising technology for Internet-of-Things (IoT) devices to power sensors and prolong battery life. In this paper, we present a novel side-channel attack that leverages RF energy harvesting signals to eavesdrop mobile app activities. To demonstrate this novel attack, we propose AppListener, an automated attack framework that recognizes fine-grained mobile app activities from harvested RF energy. The RF energy is harvested from a custom-built RF energy harvester which generates voltage signals from ambient Wi-Fi transmissions, and app activities are recognized from a three-tier classification algorithm. We evaluate AppListener with four mobile devices running 40 common mobile apps (e.g., YouTube, Facebook, and WhatsApp) belonging to five categories (i.e., video, music, social media, communication, and game); each category contains five application-specific activities. Experiment results show that AppListener achieves over 99% accuracy in differentiating four different mobile devices, over 98% accuracy in classifying 40 different apps, and 86.7% accuracy in recognizing five sets of application-specific activities. Moreover, a comprehensive study is conducted to show AppListener is robust to a number of impact factors, such as distance, environment, and non-target connected devices. Practices of integrating AppListener into commercial IoT devices also demonstrate that it is easy to deploy. Finally, countermeasures are presented as the first step to defend against this novel attack.
There is a constant evolution of technology for cloud environments, including the development of new memory storage technology, such as persistent memory. The newly-released Intel Optane persistent memory provides high-performance, persistent, and byte-addressable access for storage-class applications in data centers. While Optane’s direct data management is fast and efficient, it is unclear whether it comes with undesirable security implications. This is problematic, as cloud tenants are physically co-located on the same hardware. In this paper, we present the first side-channel security analysis of Intel Optane persistent memory. We reverse-engineer the internal cache hierarchy, cache sizes, associativity, replacement policies, and wear-leveling mechanism of the Optane memory. Based on this reverse-engineering, we construct four new attack primitives on Optane’s internal components. We then present four case studies using these attack primitives. First, we present local covert channels based on Optane’s internal caching. Second, we demonstrate a keystroke side-channel attack on a remote user via Intel’s Optane-optimized key-value store, pmemkv. Third, we study a fully remote covert channel through pmemkv. Fourth, we present our Note Board attack, also through pmemkv, enabling two parties to store and exchange messages covertly across long time gaps and even power cycles of the server. Finally, we discuss mitigations against our attacks.
Password-based authentication (PBA) remains the most popular form of user authentication on the web despite its long-understood insecurity. Given the deficiencies of PBA, many online services support multi-factor authentication (MFA) and/or risk-based authentication (RBA) to better secure user accounts. The security, usability, and implementations of MFA and RBA have been studied extensively, but attempts to measure their availability among popular web services have lacked breadth. Additionally, no study has analyzed MFA and RBA prevalence together or how the presence of Single-Sign-On (SSO) providers affects the availability of MFA and RBA on the web. In this paper, we present a study of 208 popular sites in the Tranco top 5K that support account creation to understand the availability of MFA and RBA on the web, the additional authentication factors that can be used for MFA and RBA, and how logging into sites through more secure SSO providers changes the landscape of user authentication security. We find that only 42.31% of sites support any form of MFA, and only 22.12% of sites block an obvious account hijacking attempt. Though most sites do not offer MFA or RBA, SSO completely changes the picture. If one were to create an account for each site through an SSO provider that offers MFA and/or RBA, whenever available, 80.29% of sites would have access to MFA and 72.60% of sites would stop an obvious account hijacking attempt. However, this proliferation through SSO comes with a privacy trade-off, as nearly all SSO providers that support MFA and RBA are major third-party trackers.
Person re-identification (Re-ID) has become increasingly important as it supports a wide range of security applications. Traditional person Re-ID mainly relies on optical camera-based systems, which incur several limitations due to the changes in the appearance of people, occlusions, and human poses. In this work, we propose a WiFi vision-based system, 3D-ID, for person Re-ID in 3D space. Our system leverages the advances of WiFi and deep learning to help WiFi devices "see'', identify, and recognize people. In particular, we leverage multiple antennas on next-generation WiFi devices and 2D AoA estimation of the signal reflections to enable WiFi to visualize a person in the physical environment. We then leverage deep learning to digitize the visualization of the person into 3D body representation and extract both the static body shape and dynamic walking patterns for person Re-ID. Our evaluation results under various indoor environments show that the 3D-ID system achieves an overall rank-1 accuracy of 85.3%. Results also show that our system is resistant to various attacks. The proposed 3D-ID is thus very promising as it could augment or complement camera-based systems.
We consider how the DNS security and privacy landscape has evolved over time, using data collected annually at A-root between 2008 and 2021. We consider issues such as deployment of security and privacy mechanisms, including source port randomization, TXID randomization, DNSSEC, and QNAME minimization. We find that achieving general adoption of new security practices is a slow, ongoing process. Of particular note, we find a significant number of resolvers lacking nearly all of the security mechanisms we considered, even as late as 2021. Specifically, in 2021, over 4% of the resolvers analyzed were unprotected by either source port randomization, DNSSEC validation, DNS cookies, or 0x20 encoding. Encouragingly, we find that the volume of traffic from resolvers with secure practices is significantly higher than that of other resolvers.
In the recent past, we have witnessed the shift towards attacks on the microarchitectural CPU level. In particular, cache side-channels play a predominant role as they allow an attacker to exfiltrate secret information by exploiting the CPU microarchitecture. These subtle attacks exploit the architectural visibility of conflicting cache addresses. In this paper, we present ClepsydraCache, which mitigates state-of-the-art cache attacks using a novel combination of cache decay and index randomization. Each cache entry is linked with a Time-To-Live (TTL) value. We propose a new dynamic scheduling mechanism of the TTL which plays a fundamental role in preventing those attacks while maintaining performance. ClepsydraCache efficiently protects against the latest cache attacks such as Prime+(Prune+)Probe. We present a full prototype in gem5 and lay out a proof-of-concept hardware design of the TTL mechanism, which demonstrates the feasibility of deploying ClepsydraCache in real-world systems.
As an emerging application paradigm, serverless computing attracts attention from more and more adversaries. Unfortunately, security tools for conventional web applications cannot be easily ported to serverless computing due to its distributed nature, and existing serverless security solutions focus on enforcing user specified information flow policies which are unable to detect the manipulation of the order of functions in application control flow paths. In this paper, we present Kalium, an extensible security framework that leverages local function state and global application state to enforce control-flow integrity (CFI) in serverless applications. We evaluate the performance overhead and security of Kalium using realistic open-source applications; our results show that Kalium mitigates several classes of attacks with relatively low performance overhead and outperforms the state-of-the-art serverless information flow protection systems.
Database management systems (DBMSs) are essential parts of modern software. To ensure the security of DBMSs, recent approaches apply fuzzing to testing DBMSs by automatically generating SQL queries. However, existing DBMS fuzzers are limited in generating complex and valid queries, as they heavily rely on their predefined grammar models and fixed knowledge about DBMSs, but do not capture DBMS-specific state information. As a result, these approaches miss many deep bugs in DBMSs. In this paper, we propose a novel stateful fuzzing approach to effectively test DBMSs and find deep bugs. Our basic idea is that after DBMSs process each SQL statement, there is useful state information that can be dynamically collected to facilitate later query generation. Based on this idea, our approach performs dynamic query interaction to incrementally generate complex and valid SQL queries, using the captured state information. To further improve the validity of generated queries, our approach uses the error status of query processing to filter out invalid test cases. We implement our approach as a fully automatic fuzzing framework, DynSQL. DynSQL is evaluated on 6 widely-used DBMSs (including SQLite, MySQL, MariaDB, PostgreSQL, MonetDB, and ClickHouse) and finds 40 unique bugs. Among these bugs, 38 have been confirmed, 21 have been fixed, and 19 have been assigned with CVE IDs. In our evaluation, DynSQL outperforms other state-of-the-art DBMS fuzzers, achieving 41% higher code coverage and finding many bugs missed by other fuzzers.
We present the first formal analysis and comparison of the security of the two most widely deployed exposure notification systems, ROBERT and the Google and Apple Exposure Notification (GAEN) framework. ROBERT is the most popular instalment of the centralised approach to exposure notification, in which the risk score is computed by a central server. GAEN, in contrast, follows the decentralised approach, where the user's phone calculates the risk. The relative merits of centralised and decentralised systems have proven to be a controversial question. The majority of the previous analyses have focused on the privacy implications of these systems, ours is the first formal analysis to evaluate the security of the deployed systems—the absence of false risk alerts. We model the French deployment of ROBERT and the most widely deployed GAEN variant, Germany's Corona-Warn-App. We isolate the precise conditions under which these systems prevent false alerts. We determine exactly how an adversary can subvert the system via network and Bluetooth sniffing, database leakage or the compromise of phones, back-end systems and health authorities. We also investigate the security of the original specification of the DP3T protocol, in order to identify gaps between the proposed scheme and its ultimate deployment. We find a total of 27 attack patterns, including many that distinguish the centralised from the decentralised approach, as well as attacks on the authorisation procedure that differentiate all three protocols. Our results suggest that ROBERT's centralised design is more vulnerable against both opportunistic and highly resourced attackers trying to perform mass-notification attacks.
While Deep Learning-based Network Intrusion Detection Systems (DL-NIDS) have recently been significantly explored and shown superior performance, they are insufficient to actively respond to the detected intrusions due to the semantic gap between their detection results and actionable interpretations. Furthermore, their high error costs make network operators unwilling to respond solely based on their detection results. The root cause of these drawbacks can be traced to the lack of explainability of DL-NIDS. Although some methods have been developed to explain deep learning-based systems, they are incapable of handling the history inputs and complex feature dependencies of structured data and do not perform well in explaining DL-NIDS. In this paper, we present XNIDS, a novel framework that facilitates active intrusion responses by explaining DL-NIDS. Our explanation method is highlighted by: (1) approximating and sampling around history inputs; and (2) capturing feature dependencies of structured data to achieve a high-fidelity explanation. Based on the explanation results, XNIDS can further generate actionable defense rules. We evaluate XNIDS with four state-of-the-art DL-NIDS. Our evaluation results show that XNIDS outperforms previous explanation methods in terms of fidelity, sparsity, completeness, and stability, all of which are important to active intrusion responses. Moreover, we demonstrate that XNIDS can efficiently generate practical defense rules, help understand DL-NIDS behaviors and troubleshoot detection errors
The stealthiness of an attack is the most vital consideration for an attacker to reach their goals without being detected. Therefore, attackers put in a great deal of effort to increase the success rate of attacks in order not to expose information on the attacker and attack attempts resulting from failures. Exploitation of the kernel, which is a prime target for the attacker, usually takes advantage of heap-based vulnerabilities, and these exploits' success rates fortunately remain low (e.g., 56.1% on average) due to the operating principle of the default Linux kernel heap allocator, SLUB. This paper presents Pspray, a timing side-channel attack-based exploitation technique that significantly increases the success probability of exploitation. According to our evaluation, with 10 real-world vulnerabilities, Pspray significantly improves the success rate of all those vulnerabilities (e.g., from 56.1% to 97.92% on average). To prevent this exploitation technique from being abused by the attacker, we further introduce a new defense mechanism to mitigate the threat of Pspray. After applying mitigation, the overall success rate of Pspray becomes similar to that from before using Pspray with negligible performance overhead (0.25%) and memory overhead (0.52%).
Studies of online influence operations, coordinated efforts to disseminate and amplify disinformation, focus on forensic analysis of social networks or of publicly available datasets of trolls and bot accounts. However, little is known about the experiences and challenges of human participants in influence operations. We conducted semi-structured interviews with 19 influence operations participants that contribute to the online image of Venezuela, to understand their incentives, capabilities, and strategies to promote content while evading detection. To validate a subset of their answers, we performed a quantitative investigation using data collected over almost four months, from Twitter accounts they control. We found diverse participants that include pro-government and opposition supporters, operatives and grassroots campaigners, and sockpuppet account owners and real users. While pro-government and opposition participants have similar goals and promotion strategies, they differ in their motivation, organization, adversaries and detection avoidance strategies. We report the Patria framework, a government platform for operatives to log activities and receive benefits. We systematize participant strategies to promote political content, and to evade and recover from Twitter penalties. We identify vulnerability points associated with these strategies, and suggest more nuanced defenses against influence operations.