Maltrail is a malicious traffic detection system, utilizing publicly available (black)lists containing malicious and/or generally suspicious trails, along with static trails compiled from various AV reports and custom user defined lists, where trail can be anything from domain name (e.g. zvpprsensinaix.com
for Banjori malware), URL (e.g. hXXp://109.162.38.120/harsh02.exe
for known malicious executable), IP address (e.g. 185.130.5.231
for known attacker) or HTTP User-Agent header value (e.g. sqlmap
for automatic SQL injection and database takeover tool). Also, it uses (optional) advanced heuristic mechanisms that can help in discovery of unknown threats (e.g. new malware).
The following (black)lists (i.e. feeds) are being utilized:
360bigviktor, 360chinad, 360conficker, 360cryptolocker, 360gameover,
360locky, 360necurs, 360suppobox, 360tofsee, 360virut, abuseipdb, alienvault,
atmos, badips, bitcoinnodes, blackbook, blocklist, botscout,
bruteforceblocker, ciarmy, cobaltstrike, cruzit, cybercrimetracker,
dataplane, dshieldip, emergingthreatsbot, emergingthreatscip,
emergingthreatsdns, feodotrackerip, gpfcomics, greensnow, ipnoise,
kriskinteldns, kriskintelip, malc0de, malwaredomainlistdns, malwaredomains,
maxmind, minerchk, myip, openphish, palevotracker, policeman, pony,
proxylists, proxyrss, proxyspy, ransomwaretrackerdns, ransomwaretrackerip,
ransomwaretrackerurl, riproxies, rutgers, sblam, socksproxy, sslbl,
sslproxies, talosintelligence, torproject, trickbot, turris, urlhaus,
viriback, vxvault, zeustrackermonitor, zeustrackerurl, etc.
As of static entries, the trails for the following malicious entities (e.g. malware C&Cs or sinkholes) have been manually included (from various AV reports and personal research):
1ms0rry, 404, 9002, aboc, absent, ab, acbackdoor, acridrain, activeagent,
adrozek, advisorbot, adwind, adylkuzz, adzok, afrodita, agaadex, agenttesla,
aldibot, alina, allakore, almalocker, almashreq, alpha, alureon, amadey,
amavaldo, amend_miner, ammyyrat, android_acecard, android_actionspy,
android_adrd, android_ahmythrat, android_alienspy, android_andichap,
android_androrat, android_anubis, android_arspam, android_asacub,
android_backflash, android_bankbot, android_bankun, android_basbanke,
android_basebridge, android_besyria, android_blackrock, android_boxer,
android_buhsam, android_busygasper, android_calibar, android_callerspy,
android_camscanner, android_cerberus, android_chuli, android_circle,
android_claco, android_clickfraud, android_cometbot, android_cookiethief,
android_coolreaper, android_copycat, android_counterclank, android_cyberwurx,
android_darkshades, android_dendoroid, android_dougalek, android_droidjack,
android_droidkungfu, android_enesoluty, android_eventbot, android_ewalls,
android_ewind, android_exodus, android_exprespam, android_fakeapp,
android_fakebanco, android_fakedown, android_fakeinst, android_fakelog,
android_fakemart, android_fakemrat, android_fakeneflic, android_fakesecsuit,
android_fanta, android_feabme, android_flexispy, android_fobus,
android_fraudbot, android_friend, android_frogonal, android_funkybot,
android_gabas, android_geinimi, android_generic, android_geost,
android_ghostpush, android_ginmaster, android_ginp, android_gmaster,
android_gnews, android_godwon, android_golddream, android_goldencup,
android_golfspy, android_gonesixty, android_goontact, android_gplayed,
android_gustuff, android_gypte, android_henbox, android_hiddad,
android_hydra, android_ibanking, android_joker, android_jsmshider,
android_kbuster, android_kemoge, android_ligarat, android_lockdroid,
android_lotoor, android_lovetrap, android_malbus, android_mandrake,
android_maxit, android_mobok, android_mobstspy, android_monokle,
android_notcompatible, android_oneclickfraud, android_opfake,
android_ozotshielder, android_parcel, android_phonespy, android_pikspam,
android_pjapps, android_qdplugin, android_raddex, android_ransomware,
android_redalert, android_regon, android_remotecode, android_repane,
android_riltok, android_roamingmantis, android_roidsec, android_rotexy,
android_samsapo, android_sandrorat, android_selfmite, android_shadowvoice,
android_shopper, android_simbad, android_simplocker, android_skullkey,
android_sndapps, android_spynote, android_spytekcell, android_stels,
android_svpeng, android_swanalitics, android_teelog, android_telerat,
android_tetus, android_thiefbot, android_tonclank, android_torec,
android_triada, android_uracto, android_usbcleaver, android_viceleaker,
android_vmvol, android_walkinwat, android_windseeker, android_wirex,
android_wolfrat, android_xavirad, android_xbot007, android_xerxes,
android_xhelper, android_xploitspy, android_z3core, android_zertsecurity,
android_ztorg, andromeda, antefrigus, antibot, anubis, anuna, apocalypse,
apt_12, apt_17, apt_18, apt_23, apt_27, apt_30, apt_33, apt_37, apt_38,
apt_aridviper, apt_babar, apt_bahamut, etc.
Maltrail is based on the Traffic -> Sensor <-> Server <-> Client architecture. Sensor(s) is a standalone component running on the monitoring node (e.g. Linux platform connected passively to the SPAN/mirroring port or transparently inline on a Linux bridge) or at the standalone machine (e.g. Honeypot) where it "monitors" the passing Traffic for blacklisted items/trails (i.e. domain names, URLs and/or IPs). In case of a positive match, it sends the event details to the (central) Server where they are being stored inside the appropriate logging directory (i.e. LOG_DIR
described in the Configuration section). If Sensor is being run on the same machine as Server (default configuration), logs are stored directly into the local logging directory. Otherwise, they are being sent via UDP messages to the remote server (i.e. LOG_SERVER
described in the Configuration section).
Server's primary role is to store the event details and provide back-end support for the reporting web application. In default configuration, server and sensor will run on the same machine. So, to prevent potential disruptions in sensor activities, the front-end reporting part is based on the "Fat client" architecture (i.e. all data post-processing is being done inside the client's web browser instance). Events (i.e. log entries) for the chosen (24h) period are transferred to the Client, where the reporting web application is solely responsible for the presentation part. Data is sent toward the client in compressed chunks, where they are processed sequentially. The final report is created in a highly condensed form, practically allowing presentation of virtually unlimited number of events.
Note: Server component can be skipped altogether, and just use the standalone Sensor. In such case, all events would be stored in the local logging directory, while the log entries could be examined either manually or by some CSV reading application.
Fully functional demo pages with collected real-life threats can be found here.
To run Maltrail properly, Python 2.6, 2.7 or 3.x is required on *nix/BSD system, together with installed pcapy-ng package.
NOTE: Using of pcapy
lib instead of pcapy-ng
can lead to incorrect work of Maltrail, especially on Python 3.x environments. Examples.
Sensor component requires at least 1GB of RAM to run in single-process mode or more if run in multiprocessing mode, depending on the value used for option CAPTURE_BUFFER
. Additionally, Sensor component (in general case) requires administrative/root privileges.
Server component does not have any special requirements.
The following set of commands should get your Maltrail Sensor up and running (out of the box with default settings and monitoring interface "any"):
sudo apt-get install git python3 python3-dev python3-pip python-is-python3 libpcap-dev build-essential procps schedtool sudo pip3 install pcapy-ng git clone --depth 1 https://github.com/stamparm/maltrail.git cd maltrail sudo python3 sensor.py
sudo zypper install gcc gcc-c++ git libpcap-devel python3-devel python3-pip procps schedtool sudo pip3 install pcapy-ng git clone --depth 1 https://github.com/stamparm/maltrail.git cd maltrail sudo python3 sensor.py
To start the (optional) Server on same machine, open a new terminal and execute the following:
[[ -d maltrail ]] || git clone --depth 1 https://github.com/stamparm/maltrail.git cd maltrail python server.py
To test that everything is up and running execute the following:
ping -c 1 136.161.101.53 cat /var/log/maltrail/$(date +"%Y-%m-%d").log
Also, to test the capturing of DNS traffic you can try the following:
nslookup morphed.ru cat /var/log/maltrail/$(date +"%Y-%m-%d").log
To stop Sensor and Server instances (if running in background) execute the following:
sudo pkill -f sensor.py pkill -f server.py
Access the reporting interface (i.e. Client) by visiting the http://127.0.0.1:8338 (default credentials: admin:changeme!
) from your web browser:
Sensor's configuration can be found inside the maltrail.conf
file's section [Sensor]
:
If option USE_MULTIPROCESSING
is set to true
then all CPU cores will be used. One core will be used only for packet capture (with appropriate affinity, IO priority and nice level settings), while other cores will be used for packet processing. Otherwise, everything will be run on a single core. Option USE_FEED_UPDATES
can be used to turn off the trail updates from feeds altogether (and just use the provided static ones). Option UPDATE_PERIOD
contains the number of seconds between each automatic trails update (Note: default value is set to 86400
(i.e. one day)) by using definitions inside the trails
directory (Note: both Sensor and Server take care of the trails update). Option CUSTOM_TRAILS_DIR
can be used by user to provide location of directory containing the custom trails (*.txt
) files.
Option USE_HEURISTICS
turns on heuristic mechanisms (e.g. long domain name (suspicious)
, excessive no such domain name (suspicious)
, direct .exe download (suspicious)
, etc.), potentially introducing false positives. Option CAPTURE_BUFFER
presents a total memory (in bytes of percentage of total physical memory) to be used in case of multiprocessing mode for storing packet capture in a ring buffer for further processing by non-capturing processes. Option MONITOR_INTERFACE
should contain the name of the capturing interface. Use value any
to capture from all interfaces (if OS supports this). Option CAPTURE_FILTER
should contain the network capture (tcpdump
) filter to skip the uninteresting packets and ease the capturing process. Option SENSOR_NAME
contains the name that should be appearing inside the events sensor_name
value, so the event from one sensor could be distinguished from the other. If option LOG_SERVER
is set, then all events are being sent remotely to the Server, otherwise they are stored directly into the logging directory set with option LOG_DIR
, which can be found inside the maltrail.conf
file's section [All]
. In case that the option UPDATE_SERVER
is set, then all the trails are being pulled from the given location, otherwise they are being updated from trails definitions located inside the installation itself.
Options SYSLOG_SERVER
and/or LOGSTASH_SERVER
can be used to send sensor events (i.e. log data) to non-Maltrail servers. In case of SYSLOG_SERVER
, event data will be sent in CEF (Common Event Format) format to UDP (e.g. Syslog) service listening at the given address (e.g. 192.168.2.107:514
), while in case of LOGSTASH_SERVER
event data will be sent in JSON format to UDP (e.g. Logstash) service listening at the given address (e.g.
AI小说写作助手,一站式润色、改写、扩写
蛙蛙写作—国内先进的AI写作平台,涵盖小说、学术、社交媒体等多场景。提供续写、改写、润色等功能,助力创作者高效优化写作流程。界面简洁,功能全面,适合各类写作者提升内容品质和工作效率。
字节跳动发布的AI编程神器IDE
Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。
全能AI智能助手,随时解答生活与工作的多样问题
问小白,由元石科技研发的AI智能助手,快速准确地解答各种生活和工作问题,包括但不限于搜索、规划和社交互动,帮助用户在日常生活中提高效率,轻松管理个人事务。
实时语音翻译/同声传译工具
Transly是一个多场景的AI大语言模型驱动的同声传译、专业翻译助手,它拥有超精准的音频识别翻译能力,几乎零延迟的使用体验和支持多国语言可以让你带它走遍全球,无论你是留学生、商务人士、韩剧美剧爱好者,还是出国游玩、多国会议、跨国追星等等,都可以满足你所有需要同传的场景需求,线上线下通用,扫除语言障碍,让全世界的语言交流不再有国界。
一键生成PPT和Word,让学习生活更轻松
讯飞智文是一个利用 AI 技术的项目,能够帮助用户生成 PPT 以及各类文档。无论是商业领域的市场分析报告、年度目标制定,还是学生群体的职业生涯规划、实习避坑指南,亦或是活动策划、旅游攻略等内容,它都能提供支持,帮助用户精准表达,轻松呈现各种信息。
深度推理能力全新升级,全面对标OpenAI o1
科大讯飞的星火大模型,支持语言理解、知识问答和文本创作等多功能,适用于多种文件和业务场景,提升办公和日常生活的效率。讯飞星火是一个提供丰富智能服务的平台,涵盖科技资讯、图像创作、写作辅助、编程解答、科研文献解读等功能,能为不同需求的用户提供便捷高效的帮助,助力用户轻松获取信息、解决问题,满足多样化使用场景。
一种基于大语言模型的高效单流解耦语音令牌文本到语音合成模型
Spark-TTS 是一个基于 PyTorch 的开源文本到语音合成项目,由多个知名机构联合参与。该项目提供了高效的 LLM(大语言模型)驱动的语音合成方案,支持语音克隆和语音创建功能,可通过命令行界面(CLI)和 Web UI 两种方式使用。用户可以根据需求调整语音的性别、音高、速度等参数,生成高质量的语音。该项目适用于多种场景,如有声读物制作、智能语音助手开发等。
AI助力,做PPT更简单!
咔片是一款轻量化在线演示设计工具,借助 AI 技术,实现从内容生成到智能设计的一站式 PPT 制作服务。支持多种文档格式导入生成 PPT,提供海量模板、智能美化、素材替换等功能,适用于销售、教师、学生等各类人群,能高效制作出高品质 PPT,满足不同场景演示需求。
选题、配图、成文,一站式创作,让内容运营更高效
讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。
专业的AI公文写作平台,公文写作神器
AI 材料星,专业的 AI 公文写作辅助平台,为体制内工作人员提供高效的公文写作解决方案。拥有海量公文文库、9 大核心 AI 功能,支持 30 + 文稿类型生成,助力快速完成领导讲话、工作总结、述职报告等材料,提升办公效率,是体制打工人的得力写作神器。
最新AI工具、AI资讯
独家AI资源、AI项目落地
微信扫一扫关注公众号