<prompt> Hlahisa mokhoa oa ho tsebahatsa li-imeile tsa spam.Ha ho hlokahale tlhahiso.  <response>: Instruction in English: Generate an algorithm for identifying spam emails. . Response in English: Algorithm for Identifying Spam Emails:1. Create a list of common spam words and phrases that tend to appear in spam emails.2. Check the senders address to see if it is a legitimate sender or an unknown sender. It is also essential to verify if the email address is spoofed, a common tactic of spammers.3. Scan the content of the email and compare it to the list of common spam words and phrases.4. Check if the email has an unusually high number of hyperlinks or images in its content, as this is a common feature of spam emails.5. Analyze the emails subject and headers for any signatures of phishing or deceptive subject lines.6. Check for any urgency, pressure, or call-to-action language commonly used to trick or manipulate the recipient.7. Check if the email was sent to a large number of recipients, as bulk sending is a common sign of spam.8. In case the email contains attachments, check their type and size as malicious files are often sent as attachments.9. Run a reputation check on the senders email address and IP address, verifying if they have a history of sending spam email.10. Finally, apply machine learning techniques coupled with the above rules to identify and flag any potential spam emails.In conclusion, the algorithm processes the emails based on the provided rules giving the spam score, and if the score is above a certain threshold, the email will be flagged as spam. . Response in Sesotho: Algorithm bakeng sa ho Khetholla Li-imeile tsa Spam: 1. Etsa lethathamo la mantsoe a tloaelehileng a spam le lipoleloana tse atisang ho hlaha ho li-imeile tsa spam. 2. Sheba aterese ea motho ea e rometseng ho bona hore na ke motho ea romeletseng ka molao kapa ke motho ea sa tsejoeng. Ho bohlokoa hape ho netefatsa hore na aterese ea lengolo-tsoibila e senyehile, e leng leqheka le tloaelehileng la li-spammers. 3. Hlakola litaba tsa lengolo-tsoibila me u li bapise le lethathamo la mantsoe le lipoleloana tse tloaelehileng tsa spam. 4. Hlahloba hore na imeile e na le palo e sa tloaelehang ea li-hyperlink kapa litšoantšo ka hara eona, kaha sena ke tšobotsi e tloaelehileng ea li-imeile tsa spam. 5. Sekaseka sehlooho le lihlooho tsa lengolo-tsoibila bakeng sa mesaeno efe kapa efe ea phishing kapa litaba tse thetsang. 6. Sheba hore na ha ho na potlaka, kgatello, kapa puo ya pitso ya ho nka khato e tlwaelehileng e sebediswang ho qhekella kapa ho thetsa moamohedi. 7. Hlahloba hore na lengolo-tsoibila le rometsoe ho palo e kholo ea batho ba e amohelang, kaha ho romela ka bongata ke letšoao le tloaelehileng la spam. 8. Haeba lengolo-tsoibila le na le li-attachments, hlahloba mofuta le boholo ba tsona kaha lifaele tse kotsi li atisa ho romeloa e le li-attachments. 9. Etsa tlhahlobo ea botumo atereseng ea lengolo-tsoibila ea motho ea e rometseng le atereseng ea IP, ho netefatsa hore na ba na le nalane ea ho romella lengolo-tsoibila la spam. 10. Qetellong, sebelisa mekhoa ea ho ithuta ka mochine hammoho le melao e ka holimo ho tsebahatsa le ho tšoaea li-imeile life kapa life tse ka bang teng tsa spam. Qetellong, algorithm e sebetsana le li-imeile ho latela melao e fanoeng e fanang ka lintlha tsa spam, me haeba lintlha li ka holimo ho moeli o itseng, imeile e tla tšoauoa e le spam. 